diff --git a/.gitignore b/.gitignore
index 0fa04a75..573aacb2 100644
--- a/.gitignore
+++ b/.gitignore
@@ -218,3 +218,4 @@ uv.lock
 # VCS versioning
 src/eopf_geozarr/_version.py
 analysis/.edh_token
+docs/superpowers/
diff --git a/.release-please-manifest.json b/.release-please-manifest.json
index e1a24428..31ed71b1 100644
--- a/.release-please-manifest.json
+++ b/.release-please-manifest.json
@@ -1,3 +1,3 @@
 {
-  ".": "0.10.1"
+  ".": "0.10.2"
 }
\ No newline at end of file
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 20c5c3be..7319ccb4 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,6 +5,16 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## 0.10.2 (2026-07-17)
+
+## What's Changed
+* ci(deps): bump the actions group with 3 updates by @dependabot[bot] in https://github.com/EOPF-Explorer/data-model/pull/185
+* bump zarr conventions; migrate type checking to pyright by @d-v-b in https://github.com/EOPF-Explorer/data-model/pull/199
+* fix(conversion): concurrent-safe source opening (native-chunk-aligned reads + atomic CacheStore) for data-pipeline#339 by @lhoupert in https://github.com/EOPF-Explorer/data-model/pull/220
+
+
+**Full Changelog**: https://github.com/EOPF-Explorer/data-model/compare/v0.10.1...v0.10.2
+
 ## 0.10.1 (2026-06-09)
 
 ## What's Changed
diff --git a/README.md b/README.md
index 44f3f6ea..a5fe0d14 100644
--- a/README.md
+++ b/README.md
@@ -183,7 +183,6 @@ dt_geozarr = create_geozarr_dataset(
     output_path="s3://my-bucket/geozarr-data/output.zarr",
     spatial_chunk=4096,
     min_dimension=256,
-    tile_width=256,
     max_retries=3
 )
 ```
@@ -207,7 +206,6 @@ dt_geozarr = create_geozarr_dataset(
     output_path="path/to/output/geozarr.zarr",
     spatial_chunk=4096,
     min_dimension=256,
-    tile_width=256,
     max_retries=3
 )
 ```
@@ -227,7 +225,6 @@ Create a GeoZarr-spec 0.4 compliant dataset from EOPF data.
 - `output_path` (str): Output path for the Zarr store
 - `spatial_chunk` (int, default=4096): Spatial chunk size for encoding
 - `min_dimension` (int, default=256): Minimum dimension for overview levels
-- `tile_width` (int, default=256): Tile width for TMS compatibility
 - `max_retries` (int, default=3): Maximum number of retries for network operations
 
 **Returns:**
@@ -283,6 +280,71 @@ Check if a variable is a grid_mapping variable by looking for references to it.
 
 Validate that a specific band exists and is complete in the dataset.
 
+## Supported Products
+
+### Sentinel-2 MSI
+
+Sentinel-2 MSI (MultiSpectral Instrument) L1C and L2A products are detected
+automatically by `eopf-geozarr convert` and routed to the optimized multiscale
+layout (`convert-s2-optimized`).  The three native resolution groups (10 m, 20 m,
+60 m) are reused as-is and coarser overviews (120 m, 360 m, 720 m) are computed
+via /2 downsampling.
+
+### Sentinel-3 OLCI L1 EFR
+
+Sentinel-3 OLCI (Ocean and Land Colour Instrument) Level-1 EFR (Full Resolution)
+products are detected automatically by `eopf-geozarr convert` and routed to the
+dedicated OLCI converter.  Unlike Sentinel-2, OLCI data starts out on **native
+swath geometry**: measurements are stored on a per-pixel 2-D lat/lon grid.  The
+exporter warps this curvilinear swath once onto a regular grid (default
+`EPSG:4326`) so that the output is a standard GeoZarr raster.
+
+#### Auto-detection
+
+```bash
+eopf-geozarr convert S3A_OL_1_EFR.zarr output.zarr
+```
+
+#### Dedicated command
+
+```bash
+eopf-geozarr convert-s3-olci-optimized S3A_OL_1_EFR.zarr output.zarr \
+    --spatial-chunk 1024 \
+    --min-dimension 256 \
+    --compression-level 3
+```
+
+Key flags:
+
+- `--spatial-chunk` — target spatial chunk size in pixels (default: 1024)
+- `--compression-level` — Blosc/zstd compression level 1–9 (default: 3)
+- `--min-dimension` — stop generating /2 overview levels once either spatial
+  dimension would drop below this value (default: 256)
+- `--enable-sharding` — accepted but not yet wired into encoding (follow-up task)
+- `--keep-scale-offset` — accepted but not yet wired into encoding (follow-up task)
+- `--target-crs` — target CRS for the reprojected output grid (default: `EPSG:4326`)
+
+#### What is converted
+
+- **`/measurements/r0`**: all 21 OLCI radiance bands warped once from the
+  native swath onto a regular grid (default `EPSG:4326`, ~300 m preserved),
+  with 1-D `y`/`x` coordinates, a `spatial_ref` variable, and `grid_mapping`
+  on every band; the parent `measurements/` group carries the GeoZarr
+  `multiscales`, `spatial:`, and `proj:` convention metadata.
+- **Overview subgroups** (`r2`, `r4`, …): /2 fill-aware block-averaged copies
+  stored as sibling Zarr groups next to `r0`, each with its own CRS metadata.
+- **`/conditions` and `/quality`**: copied through unmodified.
+
+> **Note:** per-scan-line `time_stamp` is not representable on a regular grid
+> and is dropped from the converted measurements (it remains in the source
+> product).
+
+> **Note:** OLCI support is initial/measurements-focused (v1).  Tie-point grid
+> groups (`conditions/geometry`, `meteorology`, `instrument`) are copied through but
+> not converted to GeoZarr convention.  Encoding wiring for `--enable-sharding`,
+> `--spatial-chunk`, `--compression-level`, and `--keep-scale-offset` is accepted
+> but scheduled as a follow-up task.
+
 ## Architecture
 
 The library is organized into the following modules:
diff --git a/docs/converter.md b/docs/converter.md
index 898c251a..8c44b59d 100644
--- a/docs/converter.md
+++ b/docs/converter.md
@@ -177,6 +177,68 @@ dt_optimized = convert_s2_optimized(
 
 The result is a space-efficient multiscale pyramid: `/measurements/reflectance/{r10m, r20m, r60m, r120m, r360m, r720m}` where the native resolutions are preserved as-is and only the coarser levels are computed.
 
+## Sentinel-3 OLCI L1 EFR Conversion
+
+Sentinel-3 OLCI (Ocean and Land Colour Instrument) Level-1 EFR (Full Resolution)
+products are supported.  OLCI measurements start out on **native swath geometry**:
+a per-pixel 2-D lat/lon grid.  The converter reprojects (warps) the swath measurements
+once onto a regular grid (default `EPSG:4326`) using the swath geolocation arrays,
+then generates /2 fill-aware block-averaged overview subgroups for multi-resolution
+access.
+
+### Auto-detection
+
+The generic `convert` command detects OLCI products automatically:
+
+```bash
+eopf-geozarr convert S3A_OL_1_EFR.zarr output.zarr
+```
+
+### Dedicated command
+
+A dedicated command offers fine-grained control:
+
+```bash
+eopf-geozarr convert-s3-olci-optimized S3A_OL_1_EFR.zarr output.zarr \
+    --spatial-chunk 1024 \
+    --min-dimension 256 \
+    --compression-level 3
+```
+
+| Flag | Default | Description |
+|------|---------|-------------|
+| `--spatial-chunk` | 1024 | Target spatial chunk size in pixels |
+| `--compression-level` | 3 | Blosc/zstd compression level (1–9) |
+| `--min-dimension` | 256 | Minimum spatial dimension for overview levels |
+| `--enable-sharding` | off | Accepted but not yet wired into encoding (follow-up task) |
+| `--keep-scale-offset` | off | Accepted but not yet wired into encoding (follow-up task) |
+| `--target-crs` | EPSG:4326 | Target CRS for the reprojected output grid |
+
+### Output layout
+
+```
+output.zarr/
+├── measurements/        # Carries multiscales + spatial: + proj: convention metadata
+│   ├── r0/             # Warped native-resolution OLCI bands (oa01_radiance … oa21_radiance)
+│   │                   # with 1-D y/x coordinates and spatial_ref
+│   ├── r2/             # 1/2-resolution overview
+│   ├── r4/             # 1/4-resolution overview
+│   └── ...
+├── conditions/          # Copied through unmodified (tie-point geometry, meteorology)
+└── quality/             # Copied through unmodified (quality flags)
+```
+
+Each measurement group carries GeoZarr `spatial:` and `proj:` convention metadata,
+with 1-D `y`/`x` dimension coordinates and a `spatial_ref` variable referenced by
+`grid_mapping` on every band.  Per-scan-line `time_stamp` is dropped, since it has
+no representation on a regular grid.
+
+> **Note:** OLCI support is initial/measurements-focused (v1).  Tie-point grid
+> groups in `conditions/geometry`, `meteorology`, and `instrument` are copied
+> through but not converted to GeoZarr convention.  Encoding wiring for
+> `--enable-sharding`, `--spatial-chunk`, `--compression-level`, and
+> `--keep-scale-offset` is accepted but scheduled as a follow-up task.
+
 ## Error Handling
 
 The converter includes robust error handling and retry logic for network operations, ensuring reliable processing even in challenging environments.
diff --git a/docs/notebooks/sentinel3_olci_geozarr.ipynb b/docs/notebooks/sentinel3_olci_geozarr.ipynb
new file mode 100644
index 00000000..36ad101d
--- /dev/null
+++ b/docs/notebooks/sentinel3_olci_geozarr.ipynb
@@ -0,0 +1,735 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2888da1e",
+   "metadata": {},
+   "source": [
+    "# Sentinel-3 OLCI L1 EFR → GeoZarr\n",
+    "\n",
+    "This notebook demonstrates the Sentinel-3 OLCI exporter in `eopf-geozarr`:\n",
+    "\n",
+    "1. **Open** an EOPF Zarr Sentinel-3 OLCI L1 EFR product.\n",
+    "2. **Detect** that it is an OLCI product.\n",
+    "3. **Convert** it to a GeoZarr-compliant, multiscale Zarr store.\n",
+    "4. **Visualize** the multiscale pyramid by splitting one field of view into\n",
+    "   four quadrants, each rendered from a *different* overview level.\n",
+    "\n",
+    "OLCI is delivered as a **curvilinear swath** (per-pixel 2-D latitude/longitude,\n",
+    "no projected CRS). The exporter warps the swath once onto a regular grid\n",
+    "(default `EPSG:4326`) using the dense per-pixel geolocation, then builds\n",
+    "overviews by fill-aware 2×2 block averaging of the radiance bands. Every\n",
+    "pyramid level declares its CRS (`spatial_ref`/`grid_mapping` plus `proj:`\n",
+    "convention attrs), so generic GeoZarr readers can tile it directly.\n",
+    "\n",
+    "> Requires the `notebooks` dependency group:\n",
+    "> `uv sync --group notebooks` (jupyter, matplotlib, nbformat)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "96531b64",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-28T06:42:29.715144Z",
+     "iopub.status.busy": "2026-07-28T06:42:29.715055Z",
+     "iopub.status.idle": "2026-07-28T06:42:31.808045Z",
+     "shell.execute_reply": "2026-07-28T06:42:31.807326Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pathlib\n",
+    "import tempfile\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import rioxarray  # noqa: F401\n",
+    "import xarray as xr\n",
+    "import zarr\n",
+    "\n",
+    "from eopf_geozarr.s3_olci_optimization.olci_converter import (\n",
+    "    convert_olci_optimized,\n",
+    "    is_sentinel3_olci_dataset,\n",
+    ")\n",
+    "from eopf_geozarr.conversion.geozarr import get_zarr_group"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb881df9",
+   "metadata": {},
+   "source": [
+    "## 1. Open an EOPF OLCI product\n",
+    "\n",
+    "We open a real Sentinel-3 OLCI L1 EFR product from the EOPF sample store on\n",
+    "EODC. If the store is unreachable (offline / CI), we fall back to the small\n",
+    "structure-only test fixture shipped with the repository so the notebook still\n",
+    "runs end to end.\n",
+    "\n",
+    "EOPF radiance is stored as scaled `uint16`; we open with `mask_and_scale=False`\n",
+    "so the raw integers and their `scale_factor` / `_FillValue` are preserved for a\n",
+    "faithful conversion (the converter expects un-decoded input)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "baf81f90",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-28T06:42:31.810203Z",
+     "iopub.status.busy": "2026-07-28T06:42:31.809957Z",
+     "iopub.status.idle": "2026-07-28T06:42:32.915643Z",
+     "shell.execute_reply": "2026-07-28T06:42:32.914869Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Opened remote EODC OLCI product.\n",
+      "Top-level groups: ['/', '/conditions', '/conditions/geometry', '/conditions/image', '/conditions/instrument', '/conditions/meteorology', '/conditions/orphans', '/measurements', '/measurements/orphans', '/quality', '/quality/orphans']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# A real OLCI L1 EFR product on the EODC EOPF sample store (use an _NT_,\n",
+    "# fully-consolidated product — _NR_ near-real-time copies may lack metadata).\n",
+    "EODC_OLCI_URL = (\n",
+    "    \"https://objects.eodc.eu/e05ab01a9d56408d82ac32d69a5aae2a:202511-s03olcefr-eu/\"\n",
+    "    \"01/products/cpm_v262/\"\n",
+    "    \"S3A_OL_1_EFR____20251101T073957_20251101T074257_20251102T084255_\"\n",
+    "    \"0179_132_149_2160_PS1_O_NT_004.zarr\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def open_olci_input() -> xr.DataTree:\n",
+    "    \"\"\"Open the remote OLCI product; fall back to the repo test fixture offline.\"\"\"\n",
+    "    try:\n",
+    "        dt = xr.open_datatree(\n",
+    "            EODC_OLCI_URL, engine=\"zarr\", chunks={}, mask_and_scale=False\n",
+    "        )\n",
+    "        print(\"Opened remote EODC OLCI product.\")\n",
+    "        return dt\n",
+    "    except Exception as exc:  # noqa: BLE001 - notebook offline fallback\n",
+    "        print(f\"Remote open failed ({type(exc).__name__}); using bundled fixture.\")\n",
+    "        from tests.conftest import create_group_from_json\n",
+    "\n",
+    "        fixture = pathlib.Path(\n",
+    "            \"tests/_test_data/s3_examples/\"\n",
+    "            \"S3A_OL_1_EFR____20251101T073957_20251101T074257_20251102T084255_\"\n",
+    "            \"0179_132_149_2160_PS1_O_NT_004.json\"\n",
+    "        )\n",
+    "        store = create_group_from_json(fixture, pathlib.Path(tempfile.mkdtemp()))\n",
+    "\n",
+    "        # The JSON fixture materializes arrays as zeros; zero lat/lon is a\n",
+    "        # degenerate geolocation the warp rejects. Seed a plausible grid\n",
+    "        # (whole-degree spacing survives this fixture's raw int32 storage).\n",
+    "        import zarr as _zarr\n",
+    "\n",
+    "        fixture_group = _zarr.open_group(str(store), mode=\"a\")\n",
+    "        fixture_meas = fixture_group[\"measurements\"]\n",
+    "        assert isinstance(fixture_meas, _zarr.Group)\n",
+    "        lat_arr = fixture_meas[\"latitude\"]\n",
+    "        assert isinstance(lat_arr, _zarr.Array)\n",
+    "        ny, nx = lat_arr.shape\n",
+    "        lat_1d = np.linspace(45.0, 45.0 + 1.0 * (ny - 1), ny)\n",
+    "        lon_1d = np.linspace(10.0, 10.0 + 1.0 * (nx - 1), nx)\n",
+    "        lat_arr[:] = np.repeat(lat_1d[:, None], nx, axis=1)\n",
+    "        lon_arr = fixture_meas[\"longitude\"]\n",
+    "        assert isinstance(lon_arr, _zarr.Array)\n",
+    "        lon_arr[:] = np.repeat(lon_1d[None, :], ny, axis=0)\n",
+    "\n",
+    "        return xr.open_datatree(store, engine=\"zarr\", mask_and_scale=False)\n",
+    "\n",
+    "\n",
+    "dt_input = open_olci_input()\n",
+    "print(\"Top-level groups:\", sorted(dt_input.groups))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd59eb10",
+   "metadata": {},
+   "source": [
+    "### Inspect the measurements group\n",
+    "\n",
+    "OLCI L1 EFR carries 21 radiance bands (`oa01_radiance` … `oa21_radiance`) on a\n",
+    "single full-resolution grid, geolocated by per-pixel 2-D `latitude` /\n",
+    "`longitude` coordinate arrays."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3bab9194",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-28T06:42:32.917397Z",
+     "iopub.status.busy": "2026-07-28T06:42:32.917242Z",
+     "iopub.status.idle": "2026-07-28T06:42:32.921736Z",
+     "shell.execute_reply": "2026-07-28T06:42:32.921001Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "21 radiance bands; e.g. ['oa01_radiance', 'oa02_radiance', 'oa03_radiance'] … oa21_radiance\n",
+      "oa01_radiance: (4090, 4865) uint16 | dims ('rows', 'columns')\n",
+      "scale_factor: 0.013946459628641605 | _FillValue: 65535\n",
+      "geolocation coords: ['latitude', 'longitude', 'altitude']\n"
+     ]
+    }
+   ],
+   "source": [
+    "meas = dt_input[\"/measurements\"].to_dataset()\n",
+    "bands = [v for v in meas.data_vars if str(v).endswith(\"_radiance\")]\n",
+    "print(f\"{len(bands)} radiance bands; e.g. {bands[:3]} … {bands[-1]}\")\n",
+    "oa = meas[\"oa01_radiance\"]\n",
+    "print(\"oa01_radiance:\", oa.shape, oa.dtype, \"| dims\", oa.dims)\n",
+    "print(\"scale_factor:\", oa.attrs.get(\"scale_factor\"), \"| _FillValue:\", oa.attrs.get(\"_FillValue\"))\n",
+    "print(\"geolocation coords:\", [c for c in (\"latitude\", \"longitude\", \"altitude\") if c in meas])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90b511c0",
+   "metadata": {},
+   "source": [
+    "## 2. Detect the product type\n",
+    "\n",
+    "Detection is structural: a product is OLCI iff it validates against the OLCI\n",
+    "data model and its measurements group contains the radiance bands."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "35c20e40",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-28T06:42:32.923263Z",
+     "iopub.status.busy": "2026-07-28T06:42:32.923119Z",
+     "iopub.status.idle": "2026-07-28T06:42:33.001243Z",
+     "shell.execute_reply": "2026-07-28T06:42:33.000704Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "is_sentinel3_olci_dataset: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "group = get_zarr_group(dt_input)\n",
+    "print(\"is_sentinel3_olci_dataset:\", is_sentinel3_olci_dataset(group))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f2497b10",
+   "metadata": {},
+   "source": [
+    "## 3. Convert to GeoZarr\n",
+    "\n",
+    "`convert_olci_optimized` first warps the swath onto a regular EPSG:4326 grid\n",
+    "using the per-pixel geolocation, then writes the `r0` base level plus `/2`\n",
+    "block-averaged overview siblings (`r2`, `r4`, …) down to `min_dimension`,\n",
+    "and declares them with the GeoZarr `multiscales` convention. `conditions`\n",
+    "and `quality` are copied through unchanged.\n",
+    "\n",
+    "We use a small `min_dimension` here so even the small sample yields several\n",
+    "overview levels to visualize."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "781d44e8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-28T06:42:33.002683Z",
+     "iopub.status.busy": "2026-07-28T06:42:33.002577Z",
+     "iopub.status.idle": "2026-07-28T06:48:56.873720Z",
+     "shell.execute_reply": "2026-07-28T06:48:56.873157Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:42:46\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mReprojecting OLCI swath       \u001b[0m \u001b[36mdst_shape\u001b[0m=\u001b[35m(4371, 6773)\u001b[0m \u001b[36msrc_shape\u001b[0m=\u001b[35m(4090, 4865)\u001b[0m \u001b[36mtarget_crs\u001b[0m=\u001b[35mEPSG:4326\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:08\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mDropping swath-bound variable with no grid home\u001b[0m \u001b[36mvariable\u001b[0m=\u001b[35mtime_stamp\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:09\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mWriting native-resolution measurements\u001b[0m \u001b[36mshape\u001b[0m=\u001b[35m{'y': 4371, 'x': 6773}\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:11\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mGenerating overview levels    \u001b[0m \u001b[36mn_levels\u001b[0m=\u001b[35m10\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:31\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mWriting overview              \u001b[0m \u001b[36mgroup\u001b[0m=\u001b[35mmeasurements/r2\u001b[0m \u001b[36mshape\u001b[0m=\u001b[35m{'y': 2185, 'x': 3386}\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:36\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mWriting overview              \u001b[0m \u001b[36mgroup\u001b[0m=\u001b[35mmeasurements/r4\u001b[0m \u001b[36mshape\u001b[0m=\u001b[35m{'y': 1092, 'x': 1693}\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:37\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mWriting overview              \u001b[0m \u001b[36mgroup\u001b[0m=\u001b[35mmeasurements/r8\u001b[0m \u001b[36mshape\u001b[0m=\u001b[35m{'y': 546, 'x': 846}\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:38\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mWriting overview              \u001b[0m \u001b[36mgroup\u001b[0m=\u001b[35mmeasurements/r16\u001b[0m \u001b[36mshape\u001b[0m=\u001b[35m{'y': 273, 'x': 423}\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:38\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mWriting overview              \u001b[0m \u001b[36mgroup\u001b[0m=\u001b[35mmeasurements/r32\u001b[0m \u001b[36mshape\u001b[0m=\u001b[35m{'y': 136, 'x': 211}\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:38\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mWriting overview              \u001b[0m \u001b[36mgroup\u001b[0m=\u001b[35mmeasurements/r64\u001b[0m \u001b[36mshape\u001b[0m=\u001b[35m{'y': 68, 'x': 105}\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:38\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mWriting overview              \u001b[0m \u001b[36mgroup\u001b[0m=\u001b[35mmeasurements/r128\u001b[0m \u001b[36mshape\u001b[0m=\u001b[35m{'y': 34, 'x': 52}\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:39\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mWriting overview              \u001b[0m \u001b[36mgroup\u001b[0m=\u001b[35mmeasurements/r256\u001b[0m \u001b[36mshape\u001b[0m=\u001b[35m{'y': 17, 'x': 26}\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:39\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mWriting overview              \u001b[0m \u001b[36mgroup\u001b[0m=\u001b[35mmeasurements/r512\u001b[0m \u001b[36mshape\u001b[0m=\u001b[35m{'y': 8, 'x': 13}\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:39\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mWriting overview              \u001b[0m \u001b[36mgroup\u001b[0m=\u001b[35mmeasurements/r1024\u001b[0m \u001b[36mshape\u001b[0m=\u001b[35m{'y': 4, 'x': 6}\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:39\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mCopying ancillary group       \u001b[0m \u001b[36mgroup\u001b[0m=\u001b[35mconditions\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:39\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mCopying ancillary subgroup    \u001b[0m \u001b[36mgroup\u001b[0m=\u001b[35mconditions/geometry\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:40\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mCopying ancillary subgroup    \u001b[0m \u001b[36mgroup\u001b[0m=\u001b[35mconditions/image\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:43\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mCopying ancillary subgroup    \u001b[0m \u001b[36mgroup\u001b[0m=\u001b[35mconditions/instrument\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:43\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mCopying ancillary subgroup    \u001b[0m \u001b[36mgroup\u001b[0m=\u001b[35mconditions/meteorology\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:45\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mCopying ancillary subgroup    \u001b[0m \u001b[36mgroup\u001b[0m=\u001b[35mconditions/orphans\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:45\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mCopying ancillary group       \u001b[0m \u001b[36mgroup\u001b[0m=\u001b[35mquality\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:45\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mCopying ancillary subgroup    \u001b[0m \u001b[36mgroup\u001b[0m=\u001b[35mquality\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:54\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mCopying ancillary subgroup    \u001b[0m \u001b[36mgroup\u001b[0m=\u001b[35mquality/orphans\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:55\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mCopying measurements subgroup \u001b[0m \u001b[36mgroup\u001b[0m=\u001b[35mmeasurements/orphans\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[2m2026-07-28 08:48:55\u001b[0m [\u001b[32m\u001b[1minfo     \u001b[0m] \u001b[1mCopying ancillary subgroup    \u001b[0m \u001b[36mgroup\u001b[0m=\u001b[35mmeasurements/orphans\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pyramid levels (native + overviews): ['r0', 'r2', 'r4', 'r8', 'r16', 'r32', 'r64', 'r128', 'r256', 'r512', 'r1024']\n",
+      "CRS: EPSG:4326\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3687551/683526426.py:13: RuntimeWarning: Failed to open Zarr store with consolidated metadata, but successfully read with non-consolidated metadata. This is typically much slower for opening a dataset. To silence this warning, consider:\n",
+      "1. Consolidating metadata in this existing store with zarr.consolidate_metadata().\n",
+      "2. Explicitly setting consolidated=False, to avoid trying to read consolidate metadata, or\n",
+      "3. Explicitly setting consolidated=True, to raise an error in this case instead of falling back to try reading non-consolidated metadata.\n",
+      "  \"CRS:\", xr.open_dataset(output_path, engine=\"zarr\", group=\"measurements/r0\").rio.crs\n"
+     ]
+    }
+   ],
+   "source": [
+    "output_path = str(pathlib.Path(tempfile.mkdtemp()) / \"olci_geozarr.zarr\")\n",
+    "convert_olci_optimized(dt_input, output_path=output_path, min_dimension=4)\n",
+    "\n",
+    "store = zarr.open_group(output_path, mode=\"r\")\n",
+    "# Pyramid levels are the rN subgroups under measurements (exclude e.g. orphans):\n",
+    "# r0 is native resolution, r2/r4/… are the overviews.\n",
+    "levels = sorted(\n",
+    "    (k for k in store[\"measurements\"].group_keys() if k.startswith(\"r\")),\n",
+    "    key=lambda k: int(k[1:]),\n",
+    ")\n",
+    "print(\"Pyramid levels (native + overviews):\", levels)\n",
+    "print(\n",
+    "    \"CRS:\", xr.open_dataset(output_path, engine=\"zarr\", group=\"measurements/r0\").rio.crs\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c5310cc",
+   "metadata": {},
+   "source": [
+    "### Load one band at every pyramid level\n",
+    "\n",
+    "Level `r0` is the native resolution; `r2`, `r4`, … are successively coarser\n",
+    "block-averaged overviews. We read with\n",
+    "default decoding so the stored `uint16` radiance is scaled to physical units\n",
+    "for display."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3f654e2e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-28T06:48:56.875690Z",
+     "iopub.status.busy": "2026-07-28T06:48:56.875581Z",
+     "iopub.status.idle": "2026-07-28T06:48:57.124716Z",
+     "shell.execute_reply": "2026-07-28T06:48:57.123792Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3687551/3670716255.py:5: RuntimeWarning: Failed to open Zarr store with consolidated metadata, but successfully read with non-consolidated metadata. This is typically much slower for opening a dataset. To silence this warning, consider:\n",
+      "1. Consolidating metadata in this existing store with zarr.consolidate_metadata().\n",
+      "2. Explicitly setting consolidated=False, to avoid trying to read consolidate metadata, or\n",
+      "3. Explicitly setting consolidated=True, to raise an error in this case instead of falling back to try reading non-consolidated metadata.\n",
+      "  ds = xr.open_dataset(output_path, engine=\"zarr\", group=f\"measurements/{level}\")\n",
+      "/tmp/ipykernel_3687551/3670716255.py:5: RuntimeWarning: Failed to open Zarr store with consolidated metadata, but successfully read with non-consolidated metadata. This is typically much slower for opening a dataset. To silence this warning, consider:\n",
+      "1. Consolidating metadata in this existing store with zarr.consolidate_metadata().\n",
+      "2. Explicitly setting consolidated=False, to avoid trying to read consolidate metadata, or\n",
+      "3. Explicitly setting consolidated=True, to raise an error in this case instead of falling back to try reading non-consolidated metadata.\n",
+      "  ds = xr.open_dataset(output_path, engine=\"zarr\", group=f\"measurements/{level}\")\n",
+      "/tmp/ipykernel_3687551/3670716255.py:5: RuntimeWarning: Failed to open Zarr store with consolidated metadata, but successfully read with non-consolidated metadata. This is typically much slower for opening a dataset. To silence this warning, consider:\n",
+      "1. Consolidating metadata in this existing store with zarr.consolidate_metadata().\n",
+      "2. Explicitly setting consolidated=False, to avoid trying to read consolidate metadata, or\n",
+      "3. Explicitly setting consolidated=True, to raise an error in this case instead of falling back to try reading non-consolidated metadata.\n",
+      "  ds = xr.open_dataset(output_path, engine=\"zarr\", group=f\"measurements/{level}\")\n",
+      "/tmp/ipykernel_3687551/3670716255.py:5: RuntimeWarning: Failed to open Zarr store with consolidated metadata, but successfully read with non-consolidated metadata. This is typically much slower for opening a dataset. To silence this warning, consider:\n",
+      "1. Consolidating metadata in this existing store with zarr.consolidate_metadata().\n",
+      "2. Explicitly setting consolidated=False, to avoid trying to read consolidate metadata, or\n",
+      "3. Explicitly setting consolidated=True, to raise an error in this case instead of falling back to try reading non-consolidated metadata.\n",
+      "  ds = xr.open_dataset(output_path, engine=\"zarr\", group=f\"measurements/{level}\")\n",
+      "/tmp/ipykernel_3687551/3670716255.py:5: RuntimeWarning: Failed to open Zarr store with consolidated metadata, but successfully read with non-consolidated metadata. This is typically much slower for opening a dataset. To silence this warning, consider:\n",
+      "1. Consolidating metadata in this existing store with zarr.consolidate_metadata().\n",
+      "2. Explicitly setting consolidated=False, to avoid trying to read consolidate metadata, or\n",
+      "3. Explicitly setting consolidated=True, to raise an error in this case instead of falling back to try reading non-consolidated metadata.\n",
+      "  ds = xr.open_dataset(output_path, engine=\"zarr\", group=f\"measurements/{level}\")\n",
+      "/tmp/ipykernel_3687551/3670716255.py:5: RuntimeWarning: Failed to open Zarr store with consolidated metadata, but successfully read with non-consolidated metadata. This is typically much slower for opening a dataset. To silence this warning, consider:\n",
+      "1. Consolidating metadata in this existing store with zarr.consolidate_metadata().\n",
+      "2. Explicitly setting consolidated=False, to avoid trying to read consolidate metadata, or\n",
+      "3. Explicitly setting consolidated=True, to raise an error in this case instead of falling back to try reading non-consolidated metadata.\n",
+      "  ds = xr.open_dataset(output_path, engine=\"zarr\", group=f\"measurements/{level}\")\n",
+      "/tmp/ipykernel_3687551/3670716255.py:5: RuntimeWarning: Failed to open Zarr store with consolidated metadata, but successfully read with non-consolidated metadata. This is typically much slower for opening a dataset. To silence this warning, consider:\n",
+      "1. Consolidating metadata in this existing store with zarr.consolidate_metadata().\n",
+      "2. Explicitly setting consolidated=False, to avoid trying to read consolidate metadata, or\n",
+      "3. Explicitly setting consolidated=True, to raise an error in this case instead of falling back to try reading non-consolidated metadata.\n",
+      "  ds = xr.open_dataset(output_path, engine=\"zarr\", group=f\"measurements/{level}\")\n",
+      "/tmp/ipykernel_3687551/3670716255.py:5: RuntimeWarning: Failed to open Zarr store with consolidated metadata, but successfully read with non-consolidated metadata. This is typically much slower for opening a dataset. To silence this warning, consider:\n",
+      "1. Consolidating metadata in this existing store with zarr.consolidate_metadata().\n",
+      "2. Explicitly setting consolidated=False, to avoid trying to read consolidate metadata, or\n",
+      "3. Explicitly setting consolidated=True, to raise an error in this case instead of falling back to try reading non-consolidated metadata.\n",
+      "  ds = xr.open_dataset(output_path, engine=\"zarr\", group=f\"measurements/{level}\")\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3687551/3670716255.py:5: RuntimeWarning: Failed to open Zarr store with consolidated metadata, but successfully read with non-consolidated metadata. This is typically much slower for opening a dataset. To silence this warning, consider:\n",
+      "1. Consolidating metadata in this existing store with zarr.consolidate_metadata().\n",
+      "2. Explicitly setting consolidated=False, to avoid trying to read consolidate metadata, or\n",
+      "3. Explicitly setting consolidated=True, to raise an error in this case instead of falling back to try reading non-consolidated metadata.\n",
+      "  ds = xr.open_dataset(output_path, engine=\"zarr\", group=f\"measurements/{level}\")\n",
+      "/tmp/ipykernel_3687551/3670716255.py:5: RuntimeWarning: Failed to open Zarr store with consolidated metadata, but successfully read with non-consolidated metadata. This is typically much slower for opening a dataset. To silence this warning, consider:\n",
+      "1. Consolidating metadata in this existing store with zarr.consolidate_metadata().\n",
+      "2. Explicitly setting consolidated=False, to avoid trying to read consolidate metadata, or\n",
+      "3. Explicitly setting consolidated=True, to raise an error in this case instead of falling back to try reading non-consolidated metadata.\n",
+      "  ds = xr.open_dataset(output_path, engine=\"zarr\", group=f\"measurements/{level}\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  r0: shape (4371, 6773)\n",
+      "  r2: shape (2185, 3386)\n",
+      "  r4: shape (1092, 1693)\n",
+      "  r8: shape (546, 846)\n",
+      " r16: shape (273, 423)\n",
+      " r32: shape (136, 211)\n",
+      " r64: shape (68, 105)\n",
+      "r128: shape (34, 52)\n",
+      "r256: shape (17, 26)\n",
+      "r512: shape (8, 13)\n",
+      "r1024: shape (4, 6)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_3687551/3670716255.py:5: RuntimeWarning: Failed to open Zarr store with consolidated metadata, but successfully read with non-consolidated metadata. This is typically much slower for opening a dataset. To silence this warning, consider:\n",
+      "1. Consolidating metadata in this existing store with zarr.consolidate_metadata().\n",
+      "2. Explicitly setting consolidated=False, to avoid trying to read consolidate metadata, or\n",
+      "3. Explicitly setting consolidated=True, to raise an error in this case instead of falling back to try reading non-consolidated metadata.\n",
+      "  ds = xr.open_dataset(output_path, engine=\"zarr\", group=f\"measurements/{level}\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "BAND = \"oa08_radiance\"  # ~665 nm (red); good visual contrast\n",
+    "\n",
+    "\n",
+    "def read_level(level: str) -> xr.DataArray:\n",
+    "    ds = xr.open_dataset(output_path, engine=\"zarr\", group=f\"measurements/{level}\")\n",
+    "    return ds[BAND]\n",
+    "\n",
+    "\n",
+    "level_arrays = {lvl: read_level(lvl) for lvl in levels}\n",
+    "for lvl, arr in level_arrays.items():\n",
+    "    print(f\"{lvl:>4}: shape {tuple(arr.shape)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95da72f9",
+   "metadata": {},
+   "source": [
+    "## 4. Multiscale quadrant visualization\n",
+    "\n",
+    "To show the pyramid in a single field of view, we split the native-resolution\n",
+    "image into four quadrants and fill each quadrant with data from a **different**\n",
+    "overview level. Each level's array is nearest-neighbour upsampled back to the\n",
+    "native quadrant size, so coarser levels render as visibly blockier — the\n",
+    "resolution drop is the point.\n",
+    "\n",
+    "Top-left = native, top-right = `r2`, bottom-left = `r4`, bottom-right = the\n",
+    "next coarser level available (clamped to the coarsest if fewer exist)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "b40965eb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-07-28T06:48:57.126653Z",
+     "iopub.status.busy": "2026-07-28T06:48:57.126456Z",
+     "iopub.status.idle": "2026-07-28T06:49:03.348857Z",
+     "shell.execute_reply": "2026-07-28T06:49:03.348179Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 700x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def to_native_grid(arr: xr.DataArray, ny: int, nx: int) -> np.ndarray:\n",
+    "    \"\"\"Nearest-neighbour upsample a (rows, columns) array to (ny, nx).\"\"\"\n",
+    "    a = np.asarray(arr.values, dtype=float)\n",
+    "    row_idx = (np.arange(ny) * a.shape[0] // ny).clip(0, a.shape[0] - 1)\n",
+    "    col_idx = (np.arange(nx) * a.shape[1] // nx).clip(0, a.shape[1] - 1)\n",
+    "    return a[np.ix_(row_idx, col_idx)]\n",
+    "\n",
+    "\n",
+    "native = level_arrays[\"r0\"]\n",
+    "ny, nx = native.shape\n",
+    "hy, hx = ny // 2, nx // 2\n",
+    "\n",
+    "# Choose four levels (native + up to three coarser), clamped to what exists.\n",
+    "chosen = [levels[min(i, len(levels) - 1)] for i in range(4)]\n",
+    "\n",
+    "# Build a single canvas: each quadrant upsampled from its chosen level.\n",
+    "canvas = np.empty((ny, nx), dtype=float)\n",
+    "quadrants = {\n",
+    "    (slice(0, hy), slice(0, hx)): chosen[0],   # top-left\n",
+    "    (slice(0, hy), slice(hx, nx)): chosen[1],  # top-right\n",
+    "    (slice(hy, ny), slice(0, hx)): chosen[2],  # bottom-left\n",
+    "    (slice(hy, ny), slice(hx, nx)): chosen[3], # bottom-right\n",
+    "}\n",
+    "for (rsl, csl), lvl in quadrants.items():\n",
+    "    full = to_native_grid(level_arrays[lvl], ny, nx)\n",
+    "    canvas[rsl, csl] = full[rsl, csl]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(7, 7))\n",
+    "im = ax.imshow(canvas, cmap=\"viridis\")\n",
+    "ax.axhline(hy - 0.5, color=\"white\", lw=1)\n",
+    "ax.axvline(hx - 0.5, color=\"white\", lw=1)\n",
+    "labels = {\n",
+    "    (hx / 2, hy / 2): chosen[0],\n",
+    "    (hx + hx / 2, hy / 2): chosen[1],\n",
+    "    (hx / 2, hy + hy / 2): chosen[2],\n",
+    "    (hx + hx / 2, hy + hy / 2): chosen[3],\n",
+    "}\n",
+    "for (x, y), lvl in labels.items():\n",
+    "    res = 1 if lvl == \".\" else int(lvl[1:])\n",
+    "    name = \"native (r1)\" if lvl == \".\" else lvl\n",
+    "    ax.text(\n",
+    "        x, y, f\"{name}\\n1/{res} res\", color=\"white\", ha=\"center\", va=\"center\",\n",
+    "        fontweight=\"bold\", bbox={\"facecolor\": \"black\", \"alpha\": 0.4, \"pad\": 3},\n",
+    "    )\n",
+    "ax.set_title(f\"OLCI {BAND}: one FOV, four pyramid levels\")\n",
+    "ax.set_xlabel(\"columns (across-track)\")\n",
+    "ax.set_ylabel(\"rows (along-track)\")\n",
+    "fig.colorbar(im, ax=ax, shrink=0.8, label=\"radiance (scaled)\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4beef58",
+   "metadata": {},
+   "source": [
+    "Each quadrant covers the same ground area but is drawn from a coarser level as\n",
+    "you move down/right — the blockier quadrants are the lower-resolution overviews\n",
+    "from the multiscale pyramid, all produced by the single conversion above."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pyproject.toml b/pyproject.toml
index bd22dce3..57598ca1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "eopf-geozarr"
-version = "0.10.1"
+version = "0.10.2"
 description = "GeoZarr compliant data model for EOPF datasets"
 readme = "README.md"
 license = { text = "Apache-2.0" }
@@ -29,7 +29,9 @@ requires-python = ">=3.12"
 dependencies = [
     "pydantic-zarr>=0.8.0",
     "pydantic>=2.12",
-    "zarr[cast-value-rs]>=3.2.0",
+    # <3.3.0: open_source_datatree uses zarr's experimental CacheStore, whose
+    # location/API may change between minor releases; bump deliberately.
+    "zarr[cast-value-rs]>=3.2.0,<3.3.0",
     "xarray>=2025.7.1",
     "dask[array,distributed]>=2026.1.0",
     "numpy>=2.3.1",
@@ -71,6 +73,12 @@ docs = [
     "pymdown-extensions>=10.0",
     "mike>=2.1.3",
 ]
+notebooks = [
+    "cartopy>=0.23",
+    "jupyter>=1.0.0",
+    "matplotlib>=3.8.0",
+    "nbformat>=5.9.0",
+]
 
 [project.urls]
 Homepage = "https://github.com/developmentseed/eopf-geozarr"
@@ -204,7 +212,8 @@ constraint-dependencies = [
     "urllib3>=2.7.0",
     "requests>=2.33.0",
     "cryptography>=46.0.6",
-    "tornado>=6.5.5",
+    "tornado>=6.5.7",
+    "msgpack>=1.2.1",
     "filelock>=3.20.3",
     "virtualenv>=20.36.1",
     "black>=26.3.1",
diff --git a/src/eopf_geozarr/cli.py b/src/eopf_geozarr/cli.py
index 805c9237..f6f42ee0 100755
--- a/src/eopf_geozarr/cli.py
+++ b/src/eopf_geozarr/cli.py
@@ -15,6 +15,10 @@
 import xarray as xr
 
 from eopf_geozarr.s2_optimization.s2_converter import convert_s2_optimized, is_sentinel2_dataset
+from eopf_geozarr.s3_olci_optimization.olci_converter import (
+    convert_olci_optimized,
+    is_sentinel3_olci_dataset,
+)
 
 from . import create_geozarr_dataset
 from .conversion.fs_utils import (
@@ -24,12 +28,17 @@
     validate_s3_access,
 )
 from .conversion.geozarr import get_zarr_group
+from .conversion.open_source import open_source_datatree
 
 if TYPE_CHECKING:
     from dask.distributed import Client
 
 log = structlog.get_logger()
 
+# Default for convert's --spatial-chunk; shared between the parser definition
+# and the OLCI auto-detect path's ignored-options check.
+CONVERT_SPATIAL_CHUNK_DEFAULT = 4096
+
 # Suppress xarray FutureWarning about timedelta decoding
 warnings.filterwarnings("ignore", message=".*", category=FutureWarning)
 
@@ -100,6 +109,24 @@ def _is_sentinel2_input(dt: xr.DataTree) -> bool:
         return False
 
 
+def _is_sentinel3_olci_input(dt: xr.DataTree) -> bool:
+    """Best-effort Sentinel-3 OLCI detection that never breaks the generic path.
+
+    ``is_sentinel3_olci_dataset`` validates structurally against a Zarr v2 model
+    and can raise on unrelated inputs; any failure simply means "not a recognised
+    OLCI product", so fall back to the generic converter.
+
+    Detection is intentionally conservative (strict structural validation), so
+    near-miss OLCI products fall through to the generic path; the explicit
+    ``convert-s3-olci-optimized`` subcommand bypasses detection entirely.
+    """
+    try:
+        return is_sentinel3_olci_dataset(get_zarr_group(dt))
+    except Exception as exc:
+        log.debug("Sentinel-3 OLCI detection skipped", error=str(exc))
+        return False
+
+
 def convert_command(args: argparse.Namespace) -> None:
     """
     Convert EOPF dataset to GeoZarr compliant format.
@@ -184,14 +211,14 @@ def convert_command(args: argparse.Namespace) -> None:
 
         # Convert to GeoZarr compliant format
         log.info("Converting to GeoZarr compliant format...")
-        if _is_sentinel2_input(dt):
+        if _is_sentinel2_input(dt) and not getattr(args, "no_s2_optimized", False):
             # Sentinel-2 inputs use the optimized flat multiscale layout
             # (sibling r{N}m levels), shared with `convert-s2-optimized`. The
             # generic per-group options below do not apply to that layout.
             log.info(
                 "Detected Sentinel-2 input; using optimized flat multiscale layout "
                 "(per-group options such as --groups/--crs-groups/--gcp-group/--min-dimension "
-                "do not apply)"
+                "do not apply; pass --no-s2-optimized to force the generic path)"
             )
             dt_geozarr = convert_s2_optimized(
                 dt_input=dt,
@@ -203,6 +230,51 @@ def convert_command(args: argparse.Namespace) -> None:
                 keep_scale_offset=False,
                 max_retries=args.max_retries,
             )
+        # Opt-out flag first: skip the structural OLCI detection (a full
+        # model validation of the store) when the user already declined it.
+        elif not getattr(args, "no_s3_olci_optimized", False) and _is_sentinel3_olci_input(dt):
+            log.info(
+                "Detected Sentinel-3 OLCI product; using OLCI converter "
+                "(pass --no-s3-olci-optimized to force the generic path)"
+            )
+            # convert_olci_optimized requires raw (non-mask-scaled) input:
+            # radiance must stay packed uint16 with CF scale_factor/add_offset
+            # and _FillValue in .attrs (see _clear_encoding / reduce_swath).
+            # The tree above was opened with CF decoding on for detection and
+            # the generic path, so close it and re-open raw, matching
+            # convert_s3_olci_optimized_command.
+            dt.close()
+            dt_raw = open_source_datatree(
+                str(input_path),
+                storage_options=storage_options,
+                mask_and_scale=False,
+            )
+            # Only forward options the OLCI converter actually applies;
+            # enable_sharding / spatial_chunk are accepted by
+            # convert_olci_optimized but not yet wired into the encoding
+            # (forwarding them would trigger its ignored-options warning on
+            # every run, since convert's defaults differ). Re-add them here
+            # once the converter wires them through — and don't drop a user's
+            # explicit setting silently in the meantime.
+            ignored_cli_options = [
+                name
+                for name, is_non_default in (
+                    ("--spatial-chunk", args.spatial_chunk != CONVERT_SPATIAL_CHUNK_DEFAULT),
+                    ("--enable-sharding", args.enable_sharding),
+                )
+                if is_non_default
+            ]
+            if ignored_cli_options:
+                log.warning(
+                    "Options not yet applied by the OLCI converter and "
+                    "ignored on the auto-detect path",
+                    ignored_options=ignored_cli_options,
+                )
+            dt_geozarr = convert_olci_optimized(
+                dt_raw,
+                output_path=output_path,
+                min_dimension=args.min_dimension,
+            )
         else:
             dt_geozarr = create_geozarr_dataset(
                 dt_input=dt,
@@ -1074,7 +1146,22 @@ def create_parser() -> argparse.ArgumentParser:
 
     # Convert command
     convert_parser = subparsers.add_parser(
-        "convert", help="Convert EOPF dataset to GeoZarr compliant format"
+        "convert",
+        help="Convert EOPF dataset to GeoZarr compliant format",
+        description=(
+            "Convert EOPF dataset to GeoZarr compliant format. Sentinel-2 inputs are "
+            "auto-detected and converted with the optimized flat multiscale layout "
+            "(equivalent to convert-s2-optimized with keep_scale_offset disabled); for "
+            "those inputs the per-group options --groups, --crs-groups, --gcp-group and "
+            "--min-dimension do not apply. Sentinel-3 OLCI inputs are likewise "
+            "auto-detected and converted with the OLCI swath converter (equivalent to "
+            "convert-s3-olci-optimized), which honors --min-dimension but not the "
+            "per-group options; OLCI detection is intentionally strict, so near-miss "
+            "products fall back to the generic path — use convert-s3-olci-optimized "
+            "to convert them explicitly. Pass --no-s2-optimized / "
+            "--no-s3-olci-optimized to force the generic conversion path, which "
+            "honors all options."
+        ),
     )
     convert_parser.add_argument(
         "input_path", type=str, help="Path to input EOPF dataset (Zarr format)"
@@ -1089,19 +1176,22 @@ def create_parser() -> argparse.ArgumentParser:
         type=str,
         nargs="+",
         default=["/measurements/r10m", "/measurements/r20m", "/measurements/r60m"],
-        help="Groups to convert (default: Sentinel-2 resolution groups)",
+        help="Groups to convert (ignored for auto-detected Sentinel-2 inputs)",
     )
     convert_parser.add_argument(
         "--spatial-chunk",
         type=int,
-        default=4096,
+        default=CONVERT_SPATIAL_CHUNK_DEFAULT,
         help="Spatial chunk size for encoding (default: 4096)",
     )
     convert_parser.add_argument(
         "--min-dimension",
         type=int,
         default=256,
-        help="Minimum dimension for overview levels (default: 256)",
+        help=(
+            "Minimum dimension for overview levels (default: 256; ignored for "
+            "auto-detected Sentinel-2 inputs)"
+        ),
     )
     convert_parser.add_argument(
         "--max-retries",
@@ -1113,12 +1203,19 @@ def create_parser() -> argparse.ArgumentParser:
         "--crs-groups",
         type=str,
         nargs="*",
-        help="Groups that need CRS information added on best-effort basis (e.g., /conditions/geometry)",
+        help=(
+            "Groups that need CRS information added on best-effort basis "
+            "(e.g., /conditions/geometry; ignored for auto-detected Sentinel-2 inputs)"
+        ),
     )
     convert_parser.add_argument(
         "--gcp-group",
         type=str,
-        help="Groups where Ground Control Points (GCPs) are located (e.g., /conditions/gcp) (Sentinel-1)",
+        help=(
+            "Groups where Ground Control Points (GCPs) are located "
+            "(e.g., /conditions/gcp) (Sentinel-1; ignored for auto-detected "
+            "Sentinel-2 inputs)"
+        ),
     )
     convert_parser.add_argument("--verbose", action="store_true", help="Enable verbose output")
     convert_parser.add_argument(
@@ -1131,6 +1228,22 @@ def create_parser() -> argparse.ArgumentParser:
         action="store_true",
         help="Enable zarr sharding for spatial dimensions of each variable",
     )
+    convert_parser.add_argument(
+        "--no-s2-optimized",
+        action="store_true",
+        help=(
+            "Disable Sentinel-2 auto-detection and use the generic conversion path, "
+            "honoring --groups/--crs-groups/--gcp-group/--min-dimension"
+        ),
+    )
+    convert_parser.add_argument(
+        "--no-s3-olci-optimized",
+        action="store_true",
+        help=(
+            "Disable Sentinel-3 OLCI auto-detection and use the generic conversion "
+            "path, honoring --groups/--crs-groups/--gcp-group"
+        ),
+    )
     convert_parser.set_defaults(func=convert_command)
 
     # Info command
@@ -1154,6 +1267,7 @@ def create_parser() -> argparse.ArgumentParser:
 
     # Add S2 optimization commands
     add_s2_optimization_commands(subparsers)
+    add_s3_olci_optimization_commands(subparsers)
 
     return parser
 
@@ -1216,10 +1330,7 @@ def convert_s2_optimized_command(args: argparse.Namespace) -> None:
     try:
         # Load input dataset
         log.info("Loading Sentinel-2 dataset from", input_path=args.input_path)
-        storage_options = get_storage_options(str(args.input_path))
-        dt_input = xr.open_datatree(
-            str(args.input_path), engine="zarr", chunks="auto", storage_options=storage_options
-        )
+        dt_input = open_source_datatree(str(args.input_path))
 
         # Convert
         convert_s2_optimized(
@@ -1247,6 +1358,72 @@ def convert_s2_optimized_command(args: argparse.Namespace) -> None:
                     log.warning("Error closing dask cluster", error=str(e))
 
 
+def add_s3_olci_optimization_commands(subparsers: argparse._SubParsersAction) -> None:
+    """Add S3 OLCI optimization commands to CLI parser."""
+    p = subparsers.add_parser(
+        "convert-s3-olci-optimized",
+        help="Convert a Sentinel-3 OLCI L1 EFR dataset to optimized GeoZarr",
+    )
+    p.add_argument("input_path", type=str, help="Path to input OLCI dataset (Zarr)")
+    p.add_argument("output_path", type=str, help="Path for output optimized dataset")
+    p.add_argument(
+        "--spatial-chunk",
+        type=int,
+        default=1024,
+        help="Spatial chunk size (not yet applied; reserved for a follow-up)",
+    )
+    p.add_argument(
+        "--enable-sharding",
+        action="store_true",
+        help="Enable Zarr v3 sharding (not yet applied; reserved for a follow-up)",
+    )
+    p.add_argument(
+        "--compression-level",
+        type=int,
+        default=3,
+        choices=range(1, 10),
+        help="Compression level 1-9 (default: 3; not yet applied; reserved for a follow-up)",
+    )
+    p.add_argument(
+        "--min-dimension",
+        type=int,
+        default=256,
+        help="Minimum overview dimension (default: 256)",
+    )
+    p.add_argument(
+        "--keep-scale-offset",
+        action="store_true",
+        help=(
+            "Preserve scale-offset encoding instead of decoding to float "
+            "(not yet applied; output is currently always raw integer)"
+        ),
+    )
+    p.add_argument(
+        "--target-crs",
+        type=str,
+        default="EPSG:4326",
+        help="Target CRS for the reprojected output grid (default: EPSG:4326)",
+    )
+    p.add_argument("--verbose", action="store_true", help="Enable verbose output")
+    p.set_defaults(func=convert_s3_olci_optimized_command)
+
+
+def convert_s3_olci_optimized_command(args: argparse.Namespace) -> None:
+    """Execute S3 OLCI optimized conversion command."""
+    dt_input = open_source_datatree(str(args.input_path), mask_and_scale=False)
+    convert_olci_optimized(
+        dt_input,
+        output_path=args.output_path,
+        enable_sharding=args.enable_sharding,
+        spatial_chunk=args.spatial_chunk,
+        compression_level=args.compression_level,
+        min_dimension=args.min_dimension,
+        keep_scale_offset=args.keep_scale_offset,
+        target_crs=args.target_crs,
+    )
+    log.info("S3 OLCI optimization completed", output_path=args.output_path)
+
+
 def main() -> None:
     """Execute main entry point for the CLI."""
     parser = create_parser()
diff --git a/src/eopf_geozarr/conversion/__init__.py b/src/eopf_geozarr/conversion/__init__.py
index 1a0e448a..1a292070 100644
--- a/src/eopf_geozarr/conversion/__init__.py
+++ b/src/eopf_geozarr/conversion/__init__.py
@@ -17,6 +17,7 @@
     iterative_copy,
     setup_datatree_metadata_geozarr_spec_compliant,
 )
+from .open_source import open_source_datatree
 from .utils import (
     calculate_aligned_chunk_size,
     downsample_2d_array,
@@ -36,6 +37,7 @@
     "is_s3_path",
     "iterative_copy",
     "open_s3_zarr_group",
+    "open_source_datatree",
     "parse_s3_path",
     "s3_path_exists",
     "setup_datatree_metadata_geozarr_spec_compliant",
diff --git a/src/eopf_geozarr/conversion/geozarr.py b/src/eopf_geozarr/conversion/geozarr.py
index 8f9c68f4..3d7a6bdc 100644
--- a/src/eopf_geozarr/conversion/geozarr.py
+++ b/src/eopf_geozarr/conversion/geozarr.py
@@ -507,14 +507,11 @@ def write_geozarr_group(
     if not success:
         raise RuntimeError(f"Failed to write all bands for {group_name}")
 
-    # Create GeoZarr-spec compliant multiscales
-    if _is_sentinel1(dt_input):
-        assert gcp_group is not None, "GCP group required for processing Sentinel-1"
-        ds_gcp: xr.Dataset | None = dt_input[gcp_group].to_dataset()
-        # For Sentinel-1, ds_gcp is set to None since data is now reprojected and doesn't need GCP handling
-        ds_gcp = None
-    else:
-        ds_gcp = None
+    # Create GeoZarr-spec compliant multiscales. GCPs are not needed here:
+    # Sentinel-1 data was already reprojected upstream (see
+    # setup_datatree_metadata_geozarr_spec_compliant), so multiscales are
+    # created without GCP handling.
+    ds_gcp: xr.Dataset | None = None
 
     try:
         log.info("Creating GeoZarr-spec compliant multiscales", group_name=group_name)
@@ -528,7 +525,10 @@ def write_geozarr_group(
             enable_sharding=enable_sharding,
         )
     except Exception as e:
-        log.warning(
+        # Deliberately continue with the remaining groups, but surface the
+        # failure loudly (with traceback): the output store is missing its
+        # multiscales metadata for this group.
+        log.exception(
             "Failed to create GeoZarr-spec compliant multiscales",
             group_name=group_name,
             error=str(e),
diff --git a/src/eopf_geozarr/conversion/open_source.py b/src/eopf_geozarr/conversion/open_source.py
new file mode 100644
index 00000000..d43d9530
--- /dev/null
+++ b/src/eopf_geozarr/conversion/open_source.py
@@ -0,0 +1,101 @@
+"""Open source EOPF zarr stores with dask chunks aligned to native chunks.
+
+With ``chunks="auto"``, dask may split one on-disk zarr chunk into several
+read tasks that each fetch and decompress the same object key — multiplying
+egress and racing on shared cache files (EOPF-Explorer/data-pipeline#339).
+``open_source_datatree`` opens with ``chunks={}`` (the store's native chunk
+grid), so each zarr chunk is read by exactly one dask task; downstream
+conversion code rechunks to its own output encoding anyway. Note that one
+task then materializes one whole native chunk (~241 MB raw for cpm_v270
+aot/wvp) — under tight memory budgets, lower dask task concurrency rather
+than sub-splitting stored chunks.
+"""
+
+import hashlib
+from pathlib import Path
+from typing import Any
+
+import xarray as xr
+
+from .fs_utils import S3FsOptions, get_storage_options
+
+
+def open_source_datatree(
+    path: str,
+    *,
+    storage_options: S3FsOptions | dict[str, Any] | None = None,
+    cache_dir: str | None = None,
+    engine: str = "zarr",
+    mask_and_scale: bool = True,
+) -> xr.DataTree:
+    """Open a source datatree with dask chunks matching the native zarr chunks.
+
+    Parameters
+    ----------
+    path : str
+        Source store URL (local path, s3://, or https://).
+    storage_options : dict, optional
+        fsspec storage options. Defaults to ``get_storage_options(path)``
+        (S3 credentials/endpoint for s3:// paths, None otherwise).
+    cache_dir : str, optional
+        Local directory for an on-disk read cache of source objects, backed
+        by zarr's ``CacheStore`` over a ``LocalStore``. LocalStore writes are
+        atomic (temp file + rename), so a concurrent read never sees a
+        partially downloaded object. Entries are namespaced per source URL
+        (CacheStore keys entries by relative zarr key, so different sources
+        sharing a directory would otherwise collide) and never expire; use an
+        ephemeral directory.
+    engine : str
+        xarray backend engine, default ``"zarr"``.
+    mask_and_scale : bool
+        Forwarded to ``xr.open_datatree``. Pass ``False`` to keep variables
+        packed (e.g. uint16 with CF scale_factor/add_offset in ``.attrs``)
+        instead of CF-decoding to floats, as the OLCI converter requires.
+
+    Returns
+    -------
+    xr.DataTree
+        Datatree whose dask arrays are chunked exactly on the store's native
+        chunk grid: each on-disk zarr chunk is read by exactly one dask task.
+    """
+    if storage_options is None:
+        storage_options = get_storage_options(path)
+    if cache_dir is None:
+        return xr.open_datatree(
+            path,
+            engine=engine,
+            chunks={},
+            storage_options=storage_options,
+            mask_and_scale=mask_and_scale,
+        )
+    # Imported lazily: CacheStore is zarr's experimental API, so a relocation
+    # in a future zarr release must not break importing this package.
+    try:
+        from zarr.experimental.cache_store import CacheStore
+    except ImportError as exc:
+        raise ImportError(
+            "cache_dir requires zarr.experimental.cache_store.CacheStore "
+            "(present in zarr 3.2.x). Your zarr version no longer provides "
+            "it; pin zarr accordingly or open without cache_dir."
+        ) from exc
+    from zarr.storage import FsspecStore, LocalStore
+
+    source = FsspecStore.from_url(
+        path,
+        storage_options=dict(storage_options) if storage_options else None,
+        read_only=True,
+    )
+    # CacheStore keys entries by relative zarr key ("aot/0.0" is the same key
+    # for every S2 product), so namespace the cache per source URL. Trailing
+    # slashes are stripped so equivalent URLs share a namespace.
+    cache_key = hashlib.sha256(path.rstrip("/").encode()).hexdigest()[:16]
+    source_cache = Path(cache_dir) / cache_key
+    cached = CacheStore(source, cache_store=LocalStore(source_cache))
+    # xarray's open_datatree accepts a zarr store at runtime, but its stub does
+    # not list Store among the accepted input types.
+    return xr.open_datatree(
+        cached,  # pyright: ignore[reportArgumentType]
+        engine=engine,
+        chunks={},
+        mask_and_scale=mask_and_scale,
+    )
diff --git a/src/eopf_geozarr/conversion/sentinel1_reprojection.py b/src/eopf_geozarr/conversion/sentinel1_reprojection.py
index 7681e931..17b8f8e0 100644
--- a/src/eopf_geozarr/conversion/sentinel1_reprojection.py
+++ b/src/eopf_geozarr/conversion/sentinel1_reprojection.py
@@ -281,6 +281,14 @@ def _reproject_data_variable(
     # Set nodata using rioxarray if not NaN
     if not np.isnan(nodata_value):
         reprojected_var = reprojected_var.rio.write_nodata(nodata_value)
+        # Also record the fill value in the encoding: downstream attribute
+        # sanitization (setup_datatree_metadata_geozarr_spec_compliant) strips
+        # `_FillValue` from attrs and only re-adds it for float variables, and
+        # `explicit_fill_value` reads the encoding — without this, integer
+        # nodata would be lost from the output metadata.
+        reprojected_var.encoding["_FillValue"] = (
+            np.asarray(nodata_value).astype(reprojected_var.dtype).item()
+        )
 
     return reprojected_var
 
diff --git a/src/eopf_geozarr/conversion/utils.py b/src/eopf_geozarr/conversion/utils.py
index ff5e8d2c..32810807 100644
--- a/src/eopf_geozarr/conversion/utils.py
+++ b/src/eopf_geozarr/conversion/utils.py
@@ -112,7 +112,17 @@ def sanitize_array_attrs(
 ) -> dict[str, Any]:
     """Return a copy of *attrs* with source-only and misleading keys removed.
 
-    - ``_eopf_attrs`` is always removed.
+    - ``_eopf_attrs`` and ``_FillValue`` are always removed. ``_FillValue``
+      belongs in the variable's *encoding* (where the zarr-level fill value is
+      carried), not in its attributes; callers that need a CF ``_FillValue``
+      attribute (e.g. the NaN workaround for xarray issue #11345) must re-add
+      it after sanitizing.
+
+      .. warning:: The Sentinel-3 OLCI converter has its own deliberately
+         divergent sanitizer
+         (``s3_olci_optimization.olci_converter._sanitize_olci_array_attrs_keep_fill``)
+         that **preserves** ``_FillValue`` for raw (non-mask-scaled) input.
+         Edits to the strip-list here do not apply there, and vice versa.
     - For decoded float measurement arrays (*is_decoded_float=True*), also
       removes raw-encoding leftovers ``dtype``, ``fill_value``,
       ``valid_min``, ``valid_max`` and rewrites
diff --git a/src/eopf_geozarr/data_api/s3_olci.py b/src/eopf_geozarr/data_api/s3_olci.py
new file mode 100644
index 00000000..7b55f8f0
--- /dev/null
+++ b/src/eopf_geozarr/data_api/s3_olci.py
@@ -0,0 +1,94 @@
+"""Pydantic-zarr model for the Sentinel-3 OLCI L1 EFR EOPF Zarr structure.
+
+Mirrors data_api/s2.py: GroupSpec + closed TypedDict members. Used for
+structural product detection (an EOPF product is OLCI iff it validates here).
+"""
+
+from __future__ import annotations
+
+from collections.abc import Mapping  # noqa: TC003
+
+from pydantic import BaseModel
+from typing_extensions import TypedDict
+
+from eopf_geozarr.data_api.geozarr.common import DatasetAttrs
+from eopf_geozarr.pyz.v2 import ArraySpec, GroupSpec
+
+
+class Sentinel3OlciRootAttrs(BaseModel):
+    """Root-level attributes for an OLCI DataTree (not validated in detail)."""
+
+    other_metadata: dict[str, object]
+    stac_discovery: dict[str, object]
+
+
+class Sentinel3OlciMeasurementsMembers(TypedDict, closed=True, total=False):
+    """Members of the OLCI measurements group.
+
+    The 21 radiance bands and the per-pixel geolocation coordinate arrays are
+    required in practice but typed optional so partial/variant products still
+    validate; the converter checks for the bands it needs.
+    """
+
+    latitude: ArraySpec[object]
+    longitude: ArraySpec[object]
+    altitude: ArraySpec[object]
+    time_stamp: ArraySpec[object]
+    orphans: GroupSpec[Mapping[str, object], Mapping[str, ArraySpec[object]]]
+    oa01_radiance: ArraySpec[object]
+    oa02_radiance: ArraySpec[object]
+    oa03_radiance: ArraySpec[object]
+    oa04_radiance: ArraySpec[object]
+    oa05_radiance: ArraySpec[object]
+    oa06_radiance: ArraySpec[object]
+    oa07_radiance: ArraySpec[object]
+    oa08_radiance: ArraySpec[object]
+    oa09_radiance: ArraySpec[object]
+    oa10_radiance: ArraySpec[object]
+    oa11_radiance: ArraySpec[object]
+    oa12_radiance: ArraySpec[object]
+    oa13_radiance: ArraySpec[object]
+    oa14_radiance: ArraySpec[object]
+    oa15_radiance: ArraySpec[object]
+    oa16_radiance: ArraySpec[object]
+    oa17_radiance: ArraySpec[object]
+    oa18_radiance: ArraySpec[object]
+    oa19_radiance: ArraySpec[object]
+    oa20_radiance: ArraySpec[object]
+    oa21_radiance: ArraySpec[object]
+
+
+class Sentinel3OlciMeasurementsGroup(GroupSpec[DatasetAttrs, Sentinel3OlciMeasurementsMembers]):
+    """OLCI measurements group: 21 radiance bands + 2-D geolocation."""
+
+
+class Sentinel3OlciRootMembers(TypedDict, closed=True, total=False):
+    """Members of the OLCI root group."""
+
+    measurements: Sentinel3OlciMeasurementsGroup
+    quality: GroupSpec[
+        Mapping[str, object],
+        Mapping[
+            str,
+            GroupSpec[Mapping[str, object], Mapping[str, ArraySpec[object]]] | ArraySpec[object],
+        ],
+    ]
+    conditions: GroupSpec[
+        Mapping[str, object],
+        Mapping[
+            str,
+            GroupSpec[Mapping[str, object], Mapping[str, ArraySpec[object]]] | ArraySpec[object],
+        ],
+    ]
+
+
+class Sentinel3OlciRoot(GroupSpec[Sentinel3OlciRootAttrs, Sentinel3OlciRootMembers]):
+    """Complete Sentinel-3 OLCI L1 EFR EOPF Zarr hierarchy."""
+
+    @property
+    def measurements(self) -> Sentinel3OlciMeasurementsGroup:
+        """Get the measurements group."""
+        group = self.members.get("measurements")
+        if group is None:
+            raise KeyError("measurements")
+        return group
diff --git a/src/eopf_geozarr/s2_optimization/s2_multiscale.py b/src/eopf_geozarr/s2_optimization/s2_multiscale.py
index 35a06520..a79a9832 100644
--- a/src/eopf_geozarr/s2_optimization/s2_multiscale.py
+++ b/src/eopf_geozarr/s2_optimization/s2_multiscale.py
@@ -483,6 +483,11 @@ def create_measurements_encoding(
         # Forward-propagate the existing encoding, minus keys that should be omitted
         keep_keys = XARRAY_ENCODING_KEYS - {"compressors", "shards", "chunks"}
 
+        # Whether to inject a CF _FillValue attribute for xarray issue #11345.
+        # The injection itself happens after sanitize_array_attrs below, which
+        # would otherwise strip it.
+        inject_nan_fillvalue = False
+
         if experimental_scale_offset_codec and not keep_scale_offset:
             # Push CF scale-offset into the zarr codec pipeline instead of
             # decoding to float. The data stays as packed integers on disk,
@@ -524,8 +529,7 @@ def create_measurements_encoding(
             # on the source variable.
             keep_keys = keep_keys - CF_SCALE_OFFSET_KEYS - {"_FillValue", "filters"}
             var_encoding["fill_value"] = "NaN"
-            # Inject CF _FillValue attribute for xarray issue #11345
-            var_data.attrs["_FillValue"] = np.nan
+            inject_nan_fillvalue = True
         elif not keep_scale_offset:
             # When stripping scale/offset, also strip _FillValue since the original
             # _FillValue is in raw integer units and meaningless for decoded float data.
@@ -536,8 +540,7 @@ def create_measurements_encoding(
             # to set the zarr-level fill value, distinct from "_FillValue" which
             # controls CF-convention attribute masking.
             var_encoding["fill_value"] = "NaN"
-            # Inject CF _FillValue attribute for xarray issue #11345
-            var_data.attrs["_FillValue"] = np.nan
+            inject_nan_fillvalue = True
 
         for key in keep_keys:
             if key in var_data.encoding:
@@ -554,6 +557,9 @@ def create_measurements_encoding(
         # otherwise leak into the output).
         is_float = np.issubdtype(var_data.dtype, np.floating)
         var_data.attrs = utils.sanitize_array_attrs(var_data.attrs, is_decoded_float=is_float)
+        if inject_nan_fillvalue:
+            # Inject CF _FillValue attribute for xarray issue #11345
+            var_data.attrs["_FillValue"] = np.nan
         encoding[str(var_name)] = var_encoding
 
     # Add coordinate encoding and sanitize coord attrs (e.g. drop
diff --git a/src/eopf_geozarr/s3_olci_optimization/__init__.py b/src/eopf_geozarr/s3_olci_optimization/__init__.py
new file mode 100644
index 00000000..6850218b
--- /dev/null
+++ b/src/eopf_geozarr/s3_olci_optimization/__init__.py
@@ -0,0 +1 @@
+"""Sentinel-3 OLCI L1 EFR optimization (GeoZarr export)."""
diff --git a/src/eopf_geozarr/s3_olci_optimization/olci_band_mapping.py b/src/eopf_geozarr/s3_olci_optimization/olci_band_mapping.py
new file mode 100644
index 00000000..db0cc35e
--- /dev/null
+++ b/src/eopf_geozarr/s3_olci_optimization/olci_band_mapping.py
@@ -0,0 +1,51 @@
+"""Band definitions for Sentinel-3 OLCI L1 EFR.
+
+OLCI has 21 radiance bands (Oa01..Oa21), all delivered at the same full
+resolution (~300 m) on a single swath grid.
+"""
+
+from dataclasses import dataclass
+
+RADIANCE_DTYPE = "uint16"
+
+# Band index -> central wavelength in nm (OLCI Oa01..Oa21).
+_WAVELENGTHS_NM: tuple[float, ...] = (
+    400.0,
+    412.5,
+    442.5,
+    490.0,
+    510.0,
+    560.0,
+    620.0,
+    665.0,
+    673.75,
+    681.25,
+    708.75,
+    753.75,
+    761.25,
+    764.375,
+    767.5,
+    778.75,
+    865.0,
+    885.0,
+    900.0,
+    940.0,
+    1020.0,
+)
+
+OLCI_BANDS: tuple[str, ...] = tuple(f"oa{i:02d}_radiance" for i in range(1, 22))
+
+
+@dataclass(frozen=True)
+class OlciBandInfo:
+    """Spectral characterization of a single OLCI radiance band."""
+
+    name: str
+    data_type: str
+    wavelength_center: float  # nanometers
+
+
+OLCI_BAND_INFO: dict[str, OlciBandInfo] = {
+    name: OlciBandInfo(name=name, data_type=RADIANCE_DTYPE, wavelength_center=wl)
+    for name, wl in zip(OLCI_BANDS, _WAVELENGTHS_NM, strict=True)
+}
diff --git a/src/eopf_geozarr/s3_olci_optimization/olci_converter.py b/src/eopf_geozarr/s3_olci_optimization/olci_converter.py
new file mode 100644
index 00000000..dfc70108
--- /dev/null
+++ b/src/eopf_geozarr/s3_olci_optimization/olci_converter.py
@@ -0,0 +1,418 @@
+"""Top-level Sentinel-3 OLCI L1 EFR -> GeoZarr conversion."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, cast
+
+import rioxarray  # noqa: F401
+import structlog
+import xarray as xr
+import zarr
+from rasterio.crs import CRS
+
+from eopf_geozarr.conversion.utils import build_convention_attrs
+from eopf_geozarr.data_api.s3_olci import Sentinel3OlciRoot
+from eopf_geozarr.s3_olci_optimization.olci_band_mapping import OLCI_BANDS
+from eopf_geozarr.s3_olci_optimization.olci_multiscale import (
+    grid_spatial_attrs,
+    reduce_swath,
+)
+from eopf_geozarr.s3_olci_optimization.olci_reproject import GRID_DIMS, reproject_olci
+
+if TYPE_CHECKING:
+    from zarr.core.common import JSON
+    from zarr_cm import LayoutObject, MultiscalesAttrs, Transform
+
+log = structlog.get_logger()
+
+
+def _sanitize_olci_array_attrs_keep_fill(attrs: dict[str, object]) -> dict[str, object]:
+    """Return a copy of *attrs* with stale source-only keys removed.
+
+    Strips ``_eopf_attrs``, ``dtype``, ``valid_min``, and ``valid_max`` (source
+    provenance and raw-integer-domain metadata that is misleading in GeoZarr
+    output).  Unlike the shared :func:`~eopf_geozarr.conversion.utils.sanitize_array_attrs`,
+    this helper intentionally **preserves** ``_FillValue`` because OLCI input is
+    opened with ``mask_and_scale=False`` (raw uint16) and downstream code (e.g.
+    ``reduce_swath``) needs ``_FillValue`` in ``.attrs`` to identify fill pixels
+    without CF decoding.
+
+    CF keys ``scale_factor``, ``add_offset``, ``units``, ``standard_name``,
+    ``coordinates``, and ``long_name`` are always preserved.
+    """
+    _strip = frozenset(("_eopf_attrs", "dtype", "valid_min", "valid_max"))
+    return {k: v for k, v in attrs.items() if k not in _strip}
+
+
+def is_sentinel3_olci_dataset(group: zarr.Group) -> bool:
+    """Return True if *group* is a Sentinel-3 OLCI L1 EFR product.
+
+    Detection is structural: the group must validate against
+    ``Sentinel3OlciRoot`` and its ``measurements`` group must contain the
+    first OLCI radiance band.
+
+    Detection is intentionally conservative: ``Sentinel3OlciRoot`` uses a
+    closed member set and requires the standard EOPF root attrs, so a
+    product with extra top-level groups or missing root metadata fails
+    validation and is treated as "not OLCI" (falling through to the generic
+    converter in the CLI). Users with such near-miss products should use the
+    explicit ``convert-s3-olci-optimized`` subcommand, which skips detection.
+    """
+    from eopf_geozarr.pyz.v2 import GroupSpec
+
+    try:
+        model = Sentinel3OlciRoot.model_validate(GroupSpec.from_zarr(group).model_dump())
+    except Exception as e:
+        # Classify, never raise: from_zarr/model_dump can fail with types
+        # other than ValidationError on malformed or unexpected stores, and
+        # any failure simply means "not a recognised OLCI product".
+        log.debug("Not an OLCI dataset", error=str(e))
+        return False
+    try:
+        return OLCI_BANDS[0] in model.measurements.members
+    except KeyError:
+        return False
+
+
+def _overview_levels(rows: int, cols: int, min_dimension: int) -> int:
+    """Return the number of /2 decimations before min(rows, cols) drops below min_dimension.
+
+    A level is generated only when the *post*-decimation minimum spatial
+    dimension is at least *min_dimension*.  For example, a 512x480 dataset
+    with min_dimension=256 yields zero levels because 480//2=240 < 256, while
+    a 1024x1024 dataset with min_dimension=256 yields two levels (512x512,
+    then 256x256).
+    """
+    levels = 0
+    r, c = rows, cols
+    while min(r, c) // 2 >= min_dimension:
+        r, c = r // 2, c // 2
+        levels += 1
+    return levels
+
+
+def _clear_encoding(ds: xr.Dataset) -> xr.Dataset:
+    """Return *ds* with all inherited source encoding cleared.
+
+    When the input DataTree was opened from a Zarr v2 store, xarray carries
+    ``numcodecs.Blosc`` compressors (and potentially scale-offset filters) in
+    each variable's ``.encoding``.  Passing that encoding to
+    ``Dataset.to_zarr(zarr_format=3)`` raises::
+
+        TypeError: Expected a BytesBytesCodec. Got <class 'numcodecs.blosc.Blosc'>
+
+    because numcodecs codecs are not valid Zarr v3 BytesBytesCodecs.  Clearing
+    the encoding lets the Zarr v3 writer choose its own default codecs.
+
+    This converter expects raw (non-mask-scaled) input: the caller must open
+    the source DataTree with ``mask_and_scale=False`` so that CF
+    ``scale_factor``/``add_offset`` stay in ``.attrs`` and integer fill pixels
+    are identified via ``attrs["_FillValue"]``.  Only Zarr v2 *codec* encoding
+    (e.g. ``numcodecs.Blosc`` compressors) is stripped here — CF metadata is
+    untouched.
+    """
+    ds = ds.copy()
+    ds.encoding = {}
+    for var in list(ds.data_vars) + list(ds.coords):
+        ds[var].encoding.clear()
+    return ds
+
+
+def _sanitize_data_vars(ds: xr.Dataset) -> xr.Dataset:
+    """Return *ds* with stale source attrs stripped from all data variables.
+
+    Applies :func:`_sanitize_olci_array_attrs_keep_fill` to every data variable in *ds*.
+    Coordinate variable attrs are left intact.
+
+    This removes ``_eopf_attrs``, ``dtype``, ``valid_min``, and ``valid_max``
+    (source-only / misleading) while preserving CF attrs
+    (``scale_factor``, ``add_offset``, ``_FillValue``, ``units``,
+    ``standard_name``, ``coordinates``).
+
+    Note: ``xr.DataArray.assign_attrs`` *merges* (update semantics), so we
+    copy the DataArray and replace ``.attrs`` in-place to ensure stale keys
+    are actually removed rather than retained from the old dict.
+    """
+    new_vars: dict[str, xr.DataArray] = {}
+    for name in ds.data_vars:
+        var = ds[name]
+        new_var = var.copy(data=var.data)
+        new_var.attrs = _sanitize_olci_array_attrs_keep_fill(dict(var.attrs))
+        new_vars[str(name)] = new_var
+    return ds.assign(new_vars)
+
+
+def _copy_subtree(node: xr.DataTree, output_path: str, *, root_group: str) -> None:
+    """Write every Dataset in *node*'s subtree to the Zarr store at *output_path*.
+
+    Each path is mapped relative to the node's own path: the node root becomes
+    *root_group*, and child nodes are placed at ``root_group/<relative_path>``.
+
+    ``DataTree.to_zarr`` does not yet support a ``group`` keyword for
+    specifying a root offset, so we write each leaf's :py:meth:`~xarray.DataTree.to_dataset`
+    using ``xr.Dataset.to_zarr`` instead.
+    """
+    node_path = node.path  # e.g. "/conditions"
+    for child in node.subtree:
+        ds = child.to_dataset()
+        if not ds.data_vars and not ds.coords:
+            continue
+        # Strip any inherited Zarr v2 encoding before writing to a v3 store.
+        ds = _clear_encoding(ds)
+        # Build the group path: strip the ancestor prefix and prepend root_group.
+        relative = child.path[len(node_path) :]  # "" for root, "/sub" for children
+        group_path = root_group + relative
+        log.info("Copying ancillary subgroup", group=group_path)
+        ds.to_zarr(
+            output_path,
+            group=group_path,
+            mode="a",
+            consolidated=False,
+            zarr_format=3,
+        )
+
+
+def convert_olci_optimized(
+    dt_input: xr.DataTree,
+    *,
+    output_path: str,
+    enable_sharding: bool = False,
+    spatial_chunk: int = 1024,
+    compression_level: int = 3,
+    min_dimension: int = 256,
+    keep_scale_offset: bool = False,
+    target_crs: str = "EPSG:4326",
+) -> xr.DataTree:
+    """Convert an EOPF OLCI L1 EFR DataTree to a GeoZarr multiscale store.
+
+    Warps the swath once to a regular *target_crs* grid, writes it to
+    ``measurements/r0``, then /2-reduced overview siblings (``r2``, ``r4``,
+    …) down to *min_dimension*; the ``measurements`` group carries the
+    GeoZarr convention metadata tying the levels together.  Any
+    ``conditions`` or ``quality`` groups present in *dt_input* are copied
+    through unchanged, along with any child subgroups of ``measurements``
+    (e.g. ``orphans``).  Every level (``r0``, ``r2``, …) carries its own
+    ``spatial_ref``/``grid_mapping`` plus zarr-cm spatial/geo-proj convention
+    attrs; the source product's per-scan-line ``time_stamp`` is dropped here
+    (it has no home on the regular grid) but survives in the source product.
+
+    Parameters
+    ----------
+    dt_input:
+        Input OLCI L1 EFR DataTree (must contain a ``/measurements`` node).
+    output_path:
+        Filesystem path for the output Zarr v3 store.
+    enable_sharding:
+        Enable Zarr v3 sharding on measurement arrays.
+        Not yet wired into encoding for this minimal pass; accepted as a
+        typed parameter for forward-compatibility (follow-up task).
+    spatial_chunk:
+        Target spatial chunk size (pixels per side).
+        Not yet wired into encoding for this minimal pass; accepted as a
+        typed parameter for forward-compatibility (follow-up task).
+    compression_level:
+        Blosc/zstd compression level.
+        Not yet wired into encoding for this minimal pass; accepted as a
+        typed parameter for forward-compatibility (follow-up task).
+    min_dimension:
+        Stop generating overview levels once either spatial dimension would
+        drop below this value after /2 decimation.
+    keep_scale_offset:
+        When ``True``, preserve CF ``scale_factor``/``add_offset`` in the
+        output encoding rather than decoding to float32.
+        Not yet wired into encoding for this minimal pass; accepted as a
+        typed parameter for forward-compatibility (follow-up task).
+    target_crs:
+        Target CRS for the output grid; the swath is warped once at native
+        resolution before the pyramid builds.
+
+    Returns
+    -------
+    xr.DataTree
+        The opened output DataTree (lazy; backed by the written Zarr store).
+        Opened with ``mask_and_scale=False``, mirroring the raw store and the
+        converter's input: radiance is packed ``uint16`` with its CF
+        ``scale_factor``/``_FillValue`` attrs intact, not decoded floats.
+        Native-resolution arrays live at ``measurements/r0`` with overview
+        levels (``r2``, ``r4``, …) as sibling groups, all on a regular grid
+        with 1-D ``y``/``x`` coordinates and a declared CRS; ``measurements``
+        itself holds only the multiscales/spatial convention metadata, so the
+        whole store opens cleanly with ``xr.open_datatree``.
+
+    Notes
+    -----
+    Parameters ``enable_sharding``, ``spatial_chunk``, ``compression_level``,
+    and ``keep_scale_offset`` are accepted but not yet applied to the on-disk
+    encoding.  Wiring them through the existing ``conversion`` helpers
+    (``create_measurements_encoding``, sharding codec, etc.) is left for a
+    follow-up task so as not to block the integration test.  A warning is
+    logged when a non-default value is passed for any of them, so callers
+    aren't silently handed default-encoded output.
+    """
+    unwired: dict[str, tuple[object, object]] = {
+        "enable_sharding": (enable_sharding, False),
+        "spatial_chunk": (spatial_chunk, 1024),
+        "compression_level": (compression_level, 3),
+        "keep_scale_offset": (keep_scale_offset, False),
+    }
+    ignored = [name for name, (value, default) in unwired.items() if value != default]
+    if ignored:
+        log.warning(
+            "Options not yet applied by the OLCI converter; output uses default encoding",
+            ignored_options=ignored,
+        )
+
+    measurements = dt_input["/measurements"].to_dataset()
+    # Structural detection does not constrain dimension names, but the whole
+    # swath pipeline (reduce_swath, decimate_swath, SWATH_DIMS) assumes
+    # rows/columns; fail with a clear error instead of a bare KeyError below.
+    missing_dims = [d for d in ("rows", "columns") if d not in measurements.sizes]
+    if missing_dims:
+        raise ValueError(
+            "OLCI converter requires swath dimensions ('rows', 'columns') on the "
+            f"measurements group; missing {missing_dims}. Use the generic convert "
+            "path for products with different dimension names."
+        )
+    # Strip any inherited Zarr v2 encoding (e.g. numcodecs.Blosc compressors)
+    # so the v3 writer can choose its own default codecs without raising a
+    # "Expected a BytesBytesCodec" error.  The caller is expected to have opened
+    # the source with mask_and_scale=False, so CF attrs (scale_factor,
+    # add_offset, _FillValue) live in .attrs and are preserved here.
+    measurements = _clear_encoding(measurements)
+    # Sanitize radiance variable attrs: strip source-only / misleading attrs
+    # (_eopf_attrs, dtype, valid_min, valid_max) while keeping CF scale/offset
+    # and _FillValue so that downstream readers and reduce_swath can work
+    # correctly with raw integer data.
+    measurements = _sanitize_data_vars(measurements)
+
+    # Warp the curvilinear swath onto a regular target_crs grid (1-D y/x
+    # coords, spatial_ref + grid_mapping on every variable) at (approximately)
+    # native resolution. Everything downstream — the pyramid, spatial attrs,
+    # and CRS metadata — operates on this gridded dataset.
+    measurements = reproject_olci(measurements, target_crs=target_crs)
+    # rioxarray's write_crs records grid_mapping in both .attrs (explicitly,
+    # above) and .encoding; xarray's to_zarr refuses to serialize a variable
+    # whose attrs and encoding disagree on an encoding-owned key, so clear the
+    # inherited encoding once more after the warp.
+    measurements = _clear_encoding(measurements)
+
+    # Truncate any pre-existing store first: the writes below are per-group
+    # (mode="w" scoped to measurements/r0, mode="a" for overviews/ancillary),
+    # so a prior run with more overview levels or extra ancillary groups would
+    # otherwise leave stale sibling groups behind, and the returned DataTree
+    # (built by re-scanning the store) would surface them.
+    zarr.open_group(output_path, mode="w", zarr_format=3)
+
+    # The native-resolution arrays go in a named child group (r0) alongside the
+    # overview groups (r2, r4, …) rather than directly in ``measurements``.
+    # If the parent held the full-res coordinates itself, every overview child
+    # would inherit them over the shared y/x dims at mismatched sizes and
+    # ``xr.open_datatree`` (and any generic GeoZarr reader) would reject the
+    # store with an alignment error.
+    log.info("Writing native-resolution measurements", shape=dict(measurements.sizes))
+    measurements.to_zarr(
+        output_path,
+        group="measurements/r0",
+        mode="w",
+        consolidated=False,
+        zarr_format=3,
+    )
+
+    # Write /2 reduced overview subgroups: r2, r4, r8, …
+    rows = measurements.sizes["y"]
+    cols = measurements.sizes["x"]
+    n_levels = _overview_levels(rows, cols, min_dimension)
+    log.info("Generating overview levels", n_levels=n_levels)
+
+    level_datasets: dict[str, xr.Dataset] = {"r0": measurements}
+    current = measurements
+    for level in range(1, n_levels + 1):
+        current = reduce_swath(current, factor=2, dims=GRID_DIMS)
+        current = _clear_encoding(current)
+        # Attrs already sanitized at native level and passed through by
+        # reduce_swath; no second sanitize pass needed.
+        group_name = f"r{2**level}"
+        level_datasets[group_name] = current
+        log.info("Writing overview", group=f"measurements/{group_name}", shape=dict(current.sizes))
+        current.to_zarr(
+            output_path,
+            group=f"measurements/{group_name}",
+            mode="a",
+            consolidated=False,
+            zarr_format=3,
+        )
+
+    # Build and attach GeoZarr convention metadata (spatial + multiscales CMO)
+    # to the measurements group attrs.
+    layout: list[LayoutObject] = [{"asset": "r0"}]
+    for lvl in range(1, n_levels + 1):
+        transform: Transform = {"scale": [2.0, 2.0], "translation": [0.0, 0.0]}
+        lo: LayoutObject = {
+            "asset": f"r{2**lvl}",
+            "derived_from": f"r{2 ** (lvl - 1)}" if lvl > 1 else "r0",
+            "transform": transform,
+            "resampling_method": "average",
+        }
+        layout.append(lo)
+
+    # zarr_cm's proj convention wants a CRSLike (to_epsg/to_wkt) object, not a
+    # bare string, so resolve target_crs once here.
+    crs_obj = CRS.from_string(target_crs)
+
+    root_rw = zarr.open_group(output_path, mode="a")
+    base_transform = measurements.rio.transform(recalc=True)
+    base_spatial = grid_spatial_attrs(
+        base_transform, (measurements.sizes["y"], measurements.sizes["x"])
+    )
+    for group_name, level_ds in level_datasets.items():
+        level_transform = level_ds.rio.transform(recalc=True)
+        level_conv = build_convention_attrs(
+            spatial=grid_spatial_attrs(level_transform, (level_ds.sizes["y"], level_ds.sizes["x"])),
+            crs=crs_obj,
+        )
+        root_rw[f"measurements/{group_name}"].attrs.update(cast("dict[str, JSON]", level_conv))
+
+    if n_levels > 0:
+        ms: MultiscalesAttrs = {"layout": layout, "resampling_method": "average"}
+        conv = build_convention_attrs(multiscales=ms, spatial=base_spatial, crs=crs_obj)
+    else:
+        conv = build_convention_attrs(spatial=base_spatial, crs=crs_obj)
+
+    root_rw["measurements"].attrs.update(cast("dict[str, JSON]", conv))
+
+    # Copy conditions/quality through unchanged (if present).
+    # DataTree.to_zarr does not support a root ``group`` argument, so we
+    # iterate the subtree and write each leaf Dataset individually.
+    for grp in ("conditions", "quality"):
+        try:
+            node_item = dt_input[f"/{grp}"]
+        except KeyError:
+            continue
+        if not isinstance(node_item, xr.DataTree):
+            continue
+        log.info("Copying ancillary group", group=grp)
+        _copy_subtree(node_item, output_path, root_group=grp)
+
+    # Copy any child subgroups of measurements (e.g. orphans) through unchanged.
+    try:
+        meas_node = dt_input["/measurements"]
+    except KeyError:
+        meas_node = None
+    if isinstance(meas_node, xr.DataTree):
+        for child in meas_node.children.values():
+            log.info("Copying measurements subgroup", group=f"measurements/{child.name}")
+            _copy_subtree(child, output_path, root_group=f"measurements/{child.name}")
+
+    # The r0 named-sibling layout keeps every parent group free of arrays, so
+    # the whole store — overview levels and nested ancillary groups included —
+    # opens directly as a DataTree. mask_and_scale=False so the returned tree
+    # mirrors the raw store (packed uint16 + CF attrs), matching how the
+    # converter opened its input, rather than handing callers CF-decoded
+    # floats.
+    return xr.open_datatree(
+        output_path,
+        engine="zarr",
+        chunks={},
+        consolidated=False,
+        mask_and_scale=False,
+    )
diff --git a/src/eopf_geozarr/s3_olci_optimization/olci_multiscale.py b/src/eopf_geozarr/s3_olci_optimization/olci_multiscale.py
new file mode 100644
index 00000000..425bd044
--- /dev/null
+++ b/src/eopf_geozarr/s3_olci_optimization/olci_multiscale.py
@@ -0,0 +1,286 @@
+"""Multiscale (overview) generation for OLCI data.
+
+Both reduction strategies operate over a configurable pair of spatial
+dimensions (default the raw-swath ``(rows, columns)``; the converter's
+reprojected pyramid uses ``(y, x)``):
+
+* :func:`decimate_swath` — pure stride-based decimation; every 2-D spatial
+  variable is subsampled ``[::factor, ::factor]``.  Intended for cases where
+  preserving pixel identity matters.
+
+* :func:`reduce_swath` — radiance bands and altitude are fill-aware
+  block-averaged (mean of ``factor x factor`` blocks); other spatial
+  variables (including 1-D dimension coordinates) are stride-decimated;
+  non-spatial variables pass through unchanged.  Intended for producing
+  GeoZarr multiscale overview groups.
+
+:func:`grid_spatial_attrs` emits the zarr-cm spatial-convention attrs for a
+regular grid level with an affine transform.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+import rasterio.transform
+import structlog
+import xarray as xr
+
+from eopf_geozarr.s3_olci_optimization.olci_band_mapping import OLCI_BANDS
+
+if TYPE_CHECKING:
+    from affine import Affine
+    from zarr_cm import SpatialAttrs
+
+log = structlog.get_logger()
+
+SWATH_DIMS = ("rows", "columns")
+
+
+def decimate_swath(
+    ds: xr.Dataset, factor: int = 2, *, dims: tuple[str, str] = SWATH_DIMS
+) -> xr.Dataset:
+    """Return *ds* with every array spanning the *dims* spatial dimensions (default ``(rows, columns)``) subsampled by *factor*.
+
+    Both data variables and coordinate variables that span exactly the swath
+    dims are decimated ``[::factor, ::factor]``; everything else is passed
+    through unchanged. Attributes and encoding are preserved by xarray's isel.
+    """
+    if factor < 1:
+        raise ValueError(f"factor must be >= 1, got {factor}")
+    if factor == 1:
+        return ds
+    indexers = {dim: slice(None, None, factor) for dim in dims if dim in ds.sizes}
+    if not indexers:
+        return ds
+    return ds.isel(indexers)
+
+
+def _fill_value_of(var: xr.DataArray) -> int | float | None:
+    """Declared ``_FillValue`` from attrs or encoding, or None."""
+    fill = var.attrs.get("_FillValue")
+    if fill is None:
+        fill = var.encoding.get("_FillValue")
+    return fill
+
+
+def reduce_swath(
+    ds: xr.Dataset, factor: int = 2, *, dims: tuple[str, str] = SWATH_DIMS
+) -> xr.Dataset:
+    """Return *ds* with radiance and altitude block-averaged, other spatial variables decimated.
+
+    Overviews are an unweighted index-block mean that ASSUMES locally-uniform
+    pixel spacing; intended for visualization, not quantitative analysis at
+    reduced resolution.
+
+    Parameters spanning the *dims* spatial dimensions (default ``(rows, columns)``)
+    are processed; other variables pass through unchanged.
+
+    Radiance variables (those named in :data:`OLCI_BANDS`) are averaged over
+    ``factor x factor`` pixel blocks with fill-value awareness: fill pixels
+    are masked before averaging so a single fill pixel does not contaminate
+    the entire block.  Blocks where ALL pixels are fill are set back to the
+    fill value in the output.
+
+    Altitude (identified by ``standard_name`` falling back to variable name)
+    is fill-aware block-averaged exactly like radiance, so the elevation of
+    an overview cell describes the same pixel block its radiance was averaged
+    over; being linear, its mean commutes with CF packing and runs on raw
+    values.  Remaining 2-D spatial variables and 1-D dimension coordinates
+    are decimated by trimmed stride; variables that do not span the *dims*
+    spatial dimensions are passed through unchanged.
+
+    Parameters
+    ----------
+    ds:
+        Input dataset.  May contain any mix of radiance bands, 2-D coordinate
+        arrays, and other variables.
+    factor:
+        Spatial reduction factor.  Must be >= 1.  Factor 1 returns *ds*
+        unchanged.
+
+    Returns
+    -------
+    xr.Dataset
+        Reduced dataset with the same variables but smaller (rows, columns)
+        dimensions.
+
+    Raises
+    ------
+    ValueError
+        If *factor* < 1.
+    """
+    if factor < 1:
+        raise ValueError(f"factor must be >= 1, got {factor}")
+    if factor == 1:
+        return ds
+
+    olci_band_set: frozenset[str] = frozenset(OLCI_BANDS)
+    result_vars: dict[str, xr.DataArray] = {}
+    result_coords: dict[str, xr.DataArray] = {}
+
+    coord_names: frozenset[str] = frozenset(str(k) for k in ds.coords)
+
+    # Pre-compute the trimmed length for each swath dimension so that both the
+    # coarsen path (radiance) and the isel stride path (coordinates) produce
+    # exactly floor(N / factor) output elements.  coarsen(boundary="trim")
+    # already truncates to a multiple of factor; we match it by stopping the
+    # stride at the same trimmed limit:  slice(0, n_trim, factor).
+    #
+    # Without this, an odd-length dimension N yields:
+    #   coarsen → floor(N / factor)   e.g. 4865 → 2432
+    #   isel[::factor] → ceil(N / factor)  e.g. 4865 → 2433
+    # producing a store where coordinate arrays are longer than the data they
+    # describe, which makes xr.open_dataset raise a conflicting-sizes error.
+    dim_trim: dict[str, int] = {
+        dim: (ds.sizes[dim] // factor) * factor for dim in dims if dim in ds.sizes
+    }
+
+    all_names: list[str] = [str(k) for k in ds.data_vars] + [str(k) for k in ds.coords]
+    for name in all_names:
+        var: xr.DataArray = ds[name] if name in ds.data_vars else ds.coords[name]
+        # Order-insensitive: a variant product storing a band transposed as
+        # (columns, rows) must still get fill-aware averaging, not silently
+        # fall through to stride decimation. Normalize to *dims* order below.
+        var_dims = tuple(str(d) for d in var.dims)
+        is_swath_2d: bool = len(var_dims) == 2 and set(var_dims) == set(dims)
+        # Altitude gets the same fill-aware block mean as radiance so the
+        # elevation of an overview cell describes the same pixel block its
+        # radiance was averaged over (a stride sample would sit ~half a block
+        # away). It is a linear quantity, and a mean commutes with affine CF
+        # packing, so averaging raw packed values is exact.
+        is_altitude: bool = str(var.attrs.get("standard_name") or name) == "altitude"
+
+        if (name in olci_band_set or is_altitude) and is_swath_2d:
+            if var_dims != dims:
+                log.info("Transposing band to canonical spatial dim order", band=name)
+                var = var.transpose(*dims)
+            # Fill-aware block averaging for radiance bands.
+            fill_value: int | float | None = _fill_value_of(var)
+            orig_dtype = var.dtype
+
+            float_var = var.astype("float64")
+            if fill_value is not None:
+                if np.isnan(float(fill_value)):
+                    # NaN sentinel (float sources): equality comparison would
+                    # be a no-op (NaN != NaN is always True); mask explicitly.
+                    float_var = float_var.where(float_var.notnull())
+                else:
+                    float_var = float_var.where(float_var != float(fill_value))
+
+            # coarsen().mean() is available at runtime; pyright stubs don't expose .mean()
+            # on DataArrayCoarsen, so we suppress the type-check on the reduction call.
+            coarsened = float_var.coarsen({dims[0]: factor, dims[1]: factor}, boundary="trim")
+            # skipna=True explicitly: the fill-aware promise (one fill pixel
+            # must not contaminate its block) depends on it, so don't rely on
+            # the version-dependent default.
+            averaged: xr.DataArray = coarsened.mean(skipna=True)  # type: ignore[attr-defined,assignment]
+            # Materialize once: with dask-backed input, the isnull()/where()/
+            # .values accesses below would otherwise each re-evaluate the
+            # coarsen+mask graph — the heaviest compute in the converter.
+            averaged = averaged.compute()
+
+            if fill_value is not None:
+                valid_mask = ~averaged.isnull().values
+                fill_da = xr.where(averaged.isnull(), float(fill_value), averaged)
+                result_arr = fill_da.values
+            else:
+                valid_mask = None
+                result_arr = averaged.values
+            # Round only when packing back into an integer dtype; float
+            # radiance (e.g. a CF-decoded source) must not be quantized.
+            if np.issubdtype(orig_dtype, np.integer):
+                unrounded = result_arr
+                result_arr = np.round(result_arr)
+                if fill_value is not None and valid_mask is not None:
+                    # A valid block whose mean rounds to the fill sentinel
+                    # would be recoded as fill in the overview; nudge it one
+                    # step back into the valid domain, on the side of the
+                    # sentinel the unrounded mean came from (fill may sit at
+                    # either end of the dtype range).
+                    collision = valid_mask & (result_arr == float(fill_value))
+                    if collision.any():
+                        # Nudge candidates, clamped to the dtype range: when
+                        # the sentinel sits at a dtype bound (e.g. 0 for an
+                        # unsigned dtype), both sides resolve to the one
+                        # in-range neighbour instead of wrapping around.
+                        info = np.iinfo(orig_dtype)
+                        down = float(fill_value) - 1
+                        up = float(fill_value) + 1
+                        if down < float(info.min):
+                            down = up
+                        if up > float(info.max):
+                            up = down
+                        nudged = np.where(unrounded <= float(fill_value), down, up)
+                        result_arr = np.where(collision, nudged, result_arr)
+            result_val = result_arr.astype(orig_dtype)
+
+            out_var = xr.DataArray(result_val, dims=averaged.dims, attrs=var.attrs)
+
+            if name in ds.data_vars:
+                result_vars[name] = out_var
+            else:
+                result_coords[name] = out_var
+
+        elif is_swath_2d:
+            # Coordinate or non-radiance 2-D swath variable: decimate.
+            # Use slice(0, n_trim, factor) rather than slice(None, None, factor)
+            # so that an odd-length dimension N yields floor(N / factor) elements,
+            # matching the output length of coarsen(boundary="trim").mean().
+            indexers: dict[str, slice] = {
+                dim: slice(0, dim_trim[dim], factor) for dim in dims if dim in dim_trim
+            }
+            decimated = var.isel(indexers)
+            if name in coord_names:
+                result_coords[name] = decimated
+            else:
+                result_vars[name] = decimated
+
+        elif any(dim in (str(d) for d in var.dims) for dim in dims):
+            # 1-D (or higher) variable sharing a swath dim but not 2-D swath:
+            # decimate along whichever swath dims it carries.
+            var_dims = {str(d) for d in var.dims}
+            idx: dict[str, slice] = {
+                dim: slice(0, dim_trim[dim], factor)
+                for dim in dims
+                if dim in var_dims and dim in dim_trim
+            }
+            decimated = var.isel(idx)
+            if name in coord_names:
+                result_coords[name] = decimated
+            else:
+                result_vars[name] = decimated
+
+        else:
+            # Non-swath variable: pass through unchanged.
+            if name in coord_names:
+                result_coords[name] = var
+            else:
+                result_vars[name] = var
+
+    return xr.Dataset(result_vars, coords=result_coords)
+
+
+def grid_spatial_attrs(transform: Affine, shape: tuple[int, int]) -> SpatialAttrs:
+    """Spatial-convention data for a regular grid with an affine *transform*.
+
+    *shape* is ``(height, width)``.  Emits ``spatial:dimensions`` ``["y","x"]``,
+    pixel registration, the bounding box, and the 6-element row-major affine
+    transform.
+    """
+    height, width = shape
+    left, bottom, right, top = rasterio.transform.array_bounds(height, width, transform)
+    return {
+        "spatial:dimensions": ["y", "x"],
+        "spatial:registration": "pixel",
+        "spatial:bbox": [float(left), float(bottom), float(right), float(top)],
+        "spatial:transform": [
+            float(transform.a),
+            float(transform.b),
+            float(transform.c),
+            float(transform.d),
+            float(transform.e),
+            float(transform.f),
+        ],
+    }
diff --git a/src/eopf_geozarr/s3_olci_optimization/olci_reproject.py b/src/eopf_geozarr/s3_olci_optimization/olci_reproject.py
new file mode 100644
index 00000000..3d088700
--- /dev/null
+++ b/src/eopf_geozarr/s3_olci_optimization/olci_reproject.py
@@ -0,0 +1,181 @@
+"""Reprojection of OLCI swath measurements to a regular grid.
+
+OLCI L1 EFR is a curvilinear swath geolocated by dense per-pixel 2-D
+latitude/longitude arrays.  Generic GeoZarr readers (titiler, rioxarray)
+require a regular grid with an affine transform and a declared CRS, so the
+converter warps the swath once at native resolution using rasterio's
+geolocation-array support (``src_geoloc_array``, rasterio >= 1.4).
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import rioxarray  # noqa: F401  # enables the .rio accessor
+import structlog
+import xarray as xr
+from pyproj import CRS as ProjCRS
+from rasterio.crs import CRS
+from rasterio.warp import Resampling, calculate_default_transform, reproject
+
+log = structlog.get_logger()
+
+#: CRS of the OLCI geolocation arrays (per-pixel lat/lon in degrees).
+GEOLOC_CRS = "EPSG:4326"
+
+#: Dimension names of the reprojected regular grid.
+GRID_DIMS: tuple[str, str] = ("y", "x")
+
+_SWATH_DIMS = ("rows", "columns")
+
+
+def _nodata_for(var: xr.DataArray) -> float:
+    """Warp nodata for *var*: its ``_FillValue`` if present, else a dtype default.
+
+    Integer variables without a fill value get the dtype maximum (matching the
+    OLCI convention of 65535 for uint16 radiances); floats get NaN.
+    """
+    fill = var.attrs.get("_FillValue")
+    if fill is None:
+        fill = var.encoding.get("_FillValue")
+    if fill is not None:
+        return float(fill)
+    if np.issubdtype(var.dtype, np.integer):
+        return float(np.iinfo(var.dtype).max)
+    return float("nan")
+
+
+def _grid_coord_attrs(target_crs: str) -> tuple[dict[str, str], dict[str, str]]:
+    """CF attrs for the 1-D (y, x) dimension coordinates in *target_crs*."""
+    if ProjCRS.from_user_input(target_crs).is_geographic:
+        y_attrs = {"standard_name": "latitude", "units": "degrees_north", "axis": "Y"}
+        x_attrs = {"standard_name": "longitude", "units": "degrees_east", "axis": "X"}
+    else:
+        y_attrs = {"standard_name": "projection_y_coordinate", "units": "m", "axis": "Y"}
+        x_attrs = {"standard_name": "projection_x_coordinate", "units": "m", "axis": "X"}
+    return y_attrs, x_attrs
+
+
+def reproject_olci(
+    ds: xr.Dataset,
+    *,
+    target_crs: str = "EPSG:4326",
+    resampling: Resampling = Resampling.bilinear,
+) -> xr.Dataset:
+    """Warp an OLCI swath dataset onto a regular *target_crs* grid.
+
+    Every numeric variable spanning exactly ``(rows, columns)`` — radiance
+    bands and the 2-D ``altitude`` coordinate alike — is warped onto a common
+    grid sized by :func:`rasterio.warp.calculate_default_transform` from the
+    dense per-pixel geolocation (~native resolution preserved).  The
+    ``latitude``/``longitude`` geolocation arrays are consumed by the warp and
+    replaced by 1-D ``y``/``x`` dimension coordinates.  Variables that carry a
+    swath dim but cannot live on the grid (e.g. per-scan-line ``time_stamp``)
+    are dropped; variables without swath dims pass through unchanged.
+
+    Off-swath cells are set to each warped variable's ``_FillValue`` (dtype
+    max for integer variables without one, NaN for float variables without
+    one), and ``_FillValue`` is recorded in the output attrs of every warped
+    variable — including NaN for float variables — so downstream fill-aware
+    averaging keeps working.  Passthrough variables that carry no swath dim
+    are not warped and carry no such guarantee.
+
+    Raises
+    ------
+    ValueError
+        If the 2-D ``latitude``/``longitude`` coordinates are missing, or if
+        the geolocation spans a zero-area (degenerate) extent.
+    """
+    for required in ("latitude", "longitude"):
+        if required not in ds.coords:
+            raise ValueError(f"cannot reproject OLCI swath: missing 2-D coordinate {required!r}")
+    lat = ds.coords["latitude"]
+    lon = ds.coords["longitude"]
+    if tuple(str(d) for d in lat.dims) != _SWATH_DIMS or lat.dims != lon.dims:
+        raise ValueError("latitude/longitude must be 2-D over (rows, columns)")
+    lat_vals = np.asarray(lat.values, dtype="float64")
+    lon_vals = np.asarray(lon.values, dtype="float64")
+    if lat_vals.max() == lat_vals.min() or lon_vals.max() == lon_vals.min():
+        raise ValueError("degenerate geolocation extent: latitude/longitude span zero area")
+
+    src_height, src_width = lat_vals.shape
+    geoloc = np.stack([lon_vals, lat_vals])  # (2, H, W): x first, then y
+    transform, width, height = calculate_default_transform(
+        src_crs=CRS.from_string(GEOLOC_CRS),
+        dst_crs=CRS.from_string(target_crs),
+        width=src_width,
+        height=src_height,
+        src_geoloc_array=geoloc,
+    )
+    assert width is not None
+    assert height is not None
+    log.info(
+        "Reprojecting OLCI swath",
+        target_crs=target_crs,
+        src_shape=(src_height, src_width),
+        dst_shape=(height, width),
+    )
+
+    result_vars: dict[str, xr.DataArray] = {}
+    passthrough_coords: dict[str, xr.DataArray] = {}
+    all_names = [str(k) for k in ds.data_vars] + [
+        str(k) for k in ds.coords if str(k) not in ("latitude", "longitude")
+    ]
+    for name in all_names:
+        var = ds[name] if name in ds.data_vars else ds.coords[name]
+        var_dims = tuple(str(d) for d in var.dims)
+        if var_dims == _SWATH_DIMS and np.issubdtype(var.dtype, np.number):
+            nodata = _nodata_for(var)
+            src_nodata = (
+                nodata
+                if (
+                    var.attrs.get("_FillValue") is not None
+                    or var.encoding.get("_FillValue") is not None
+                )
+                else None
+            )
+            dest = np.full((height, width), nodata, dtype=var.dtype)
+            reproject(
+                source=np.ascontiguousarray(var.values),
+                destination=dest,
+                src_crs=CRS.from_string(GEOLOC_CRS),
+                src_geoloc_array=geoloc,
+                src_nodata=src_nodata,
+                dst_crs=CRS.from_string(target_crs),
+                dst_transform=transform,
+                dst_nodata=nodata,
+                resampling=resampling,
+            )
+            out_attrs = dict(var.attrs)
+            if np.issubdtype(var.dtype, np.integer):
+                out_attrs["_FillValue"] = int(nodata)
+            else:
+                out_attrs["_FillValue"] = float(nodata)  # NaN included
+            out_attrs.pop("coordinates", None)  # swath geolocation is gone
+            result_vars[name] = xr.DataArray(dest, dims=GRID_DIMS, attrs=out_attrs)
+        elif any(d in var_dims for d in _SWATH_DIMS):
+            log.info("Dropping swath-bound variable with no grid home", variable=name)
+        elif name in ds.coords:
+            passthrough_coords[name] = var
+        else:
+            result_vars[name] = var
+
+    xs = transform.c + transform.a * (np.arange(width) + 0.5)
+    ys = transform.f + transform.e * (np.arange(height) + 0.5)
+    y_attrs, x_attrs = _grid_coord_attrs(target_crs)
+    out = xr.Dataset(
+        result_vars,
+        coords={
+            "y": ("y", ys, y_attrs),
+            "x": ("x", xs, x_attrs),
+            **passthrough_coords,
+        },
+        attrs=dict(ds.attrs),
+    )
+    out = out.rio.write_crs(target_crs)
+    if not isinstance(out, xr.Dataset):
+        raise TypeError(f"expected an xarray.Dataset after write_crs, got {type(out).__name__}")
+    # rioxarray records grid_mapping in encoding; pin it in attrs so it is
+    # guaranteed to reach the zarr store.
+    for name in out.data_vars:
+        out[name].attrs["grid_mapping"] = "spatial_ref"
+    return out
diff --git a/tests/_test_data/optimized_olci_examples/S3A_OL_1_EFR____20251101T073957_20251101T074257_20251102T084255_0179_132_149_2160_PS1_O_NT_004.json b/tests/_test_data/optimized_olci_examples/S3A_OL_1_EFR____20251101T073957_20251101T074257_20251102T084255_0179_132_149_2160_PS1_O_NT_004.json
new file mode 100644
index 00000000..78d96999
--- /dev/null
+++ b/tests/_test_data/optimized_olci_examples/S3A_OL_1_EFR____20251101T073957_20251101T074257_20251102T084255_0179_132_149_2160_PS1_O_NT_004.json
@@ -0,0 +1,10864 @@
+{
+  "attributes": {},
+  "members": {
+    "conditions": {
+      "attributes": {},
+      "members": {
+        "geometry": {
+          "attributes": {},
+          "members": {
+            "latitude": {
+              "attributes": {
+                "dimensions": [
+                  "rows",
+                  "columns"
+                ],
+                "dtype": "<i4",
+                "eopf_is_masked": true,
+                "eopf_target_dtype": "<f8",
+                "fill_value": -2147483648,
+                "long_name": "latitude of tie points",
+                "scale_factor": 1e-06,
+                "standard_name": "latitude",
+                "units": "degrees_north",
+                "valid_max": 90000000,
+                "valid_min": -90000000
+              },
+              "chunk_grid": {
+                "configuration": {
+                  "chunk_shape": [
+                    16,
+                    4
+                  ]
+                },
+                "name": "regular"
+              },
+              "chunk_key_encoding": {
+                "configuration": {
+                  "separator": "/"
+                },
+                "name": "default"
+              },
+              "codecs": [
+                {
+                  "configuration": {
+                    "endian": "little"
+                  },
+                  "name": "bytes"
+                },
+                {
+                  "configuration": {
+                    "checksum": false,
+                    "level": 0
+                  },
+                  "name": "zstd"
+                }
+              ],
+              "data_type": "int32",
+              "dimension_names": [
+                "rows",
+                "columns"
+              ],
+              "fill_value": 0,
+              "node_type": "array",
+              "shape": [
+                16,
+                4
+              ],
+              "storage_transformers": [],
+              "zarr_format": 3
+            },
+            "longitude": {
+              "attributes": {
+                "dimensions": [
+                  "rows",
+                  "columns"
+                ],
+                "dtype": "<i4",
+                "eopf_is_masked": true,
+                "eopf_target_dtype": "<f8",
+                "fill_value": -2147483648,
+                "long_name": "longitude of tie points",
+                "scale_factor": 1e-06,
+                "standard_name": "longitude",
+                "units": "degrees_east",
+                "valid_max": 180000000,
+                "valid_min": -180000000
+              },
+              "chunk_grid": {
+                "configuration": {
+                  "chunk_shape": [
+                    16,
+                    4
+                  ]
+                },
+                "name": "regular"
+              },
+              "chunk_key_encoding": {
+                "configuration": {
+                  "separator": "/"
+                },
+                "name": "default"
+              },
+              "codecs": [
+                {
+                  "configuration": {
+                    "endian": "little"
+                  },
+                  "name": "bytes"
+                },
+                {
+                  "configuration": {
+                    "checksum": false,
+                    "level": 0
+                  },
+                  "name": "zstd"
+                }
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diff --git a/tests/_test_data/s3_examples/S3A_OL_1_EFR____20251101T073957_20251101T074257_20251102T084255_0179_132_149_2160_PS1_O_NT_004.json b/tests/_test_data/s3_examples/S3A_OL_1_EFR____20251101T073957_20251101T074257_20251102T084255_0179_132_149_2160_PS1_O_NT_004.json
new file mode 100644
index 00000000..89b91449
--- /dev/null
+++ b/tests/_test_data/s3_examples/S3A_OL_1_EFR____20251101T073957_20251101T074257_20251102T084255_0179_132_149_2160_PS1_O_NT_004.json
@@ -0,0 +1,9281 @@
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+              "chunks": [
+                16,
+                4
+              ],
+              "compressor": {
+                "blocksize": 0,
+                "clevel": 3,
+                "cname": "zstd",
+                "id": "blosc",
+                "shuffle": 2
+              },
+              "dtype": "|u1",
+              "fill_value": 255,
+              "filters": null,
+              "order": "C",
+              "shape": [
+                16,
+                4
+              ],
+              "zarr_format": 2
+            },
+            "oa15_radiance_unc": {
+              "attributes": {
+                "_ARRAY_DIMENSIONS": [
+                  "rows",
+                  "removed_pixels"
+                ],
+                "_eopf_attrs": {
+                  "add_offset": -0.7225130200386047,
+                  "dimensions": [
+                    "dim_0",
+                    "dim_1"
+                  ],
+                  "dtype": "<u1",
+                  "eopf_is_masked": true,
+                  "eopf_target_dtype": "<f4",
+                  "fill_value": 255,
+                  "long_name": "log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 15",
+                  "scale_factor": 0.006641000043600798,
+                  "short_name": "oa15_radiance_unc_orphans",
+                  "units": "lg(re mW.m-2.sr-1.nm-1)",
+                  "valid_max": 254,
+                  "valid_min": 0
+                },
+                "add_offset": -0.7225130200386047,
+                "dtype": "<u1",
+                "long_name": "log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 15",
+                "scale_factor": 0.006641000043600798,
+                "units": "lg(re mW.m-2.sr-1.nm-1)"
+              },
+              "chunks": [
+                16,
+                4
+              ],
+              "compressor": {
+                "blocksize": 0,
+                "clevel": 3,
+                "cname": "zstd",
+                "id": "blosc",
+                "shuffle": 2
+              },
+              "dtype": "|u1",
+              "fill_value": 255,
+              "filters": null,
+              "order": "C",
+              "shape": [
+                16,
+                4
+              ],
+              "zarr_format": 2
+            },
+            "oa16_radiance_unc": {
+              "attributes": {
+                "_ARRAY_DIMENSIONS": [
+                  "rows",
+                  "removed_pixels"
+                ],
+                "_eopf_attrs": {
+                  "add_offset": -0.9848459959030151,
+                  "coordinates": [
+                    "removed_pixels",
+                    "latitude",
+                    "longitude",
+                    "altitude"
+                  ],
+                  "dimensions": [
+                    "rows",
+                    "removed_pixels"
+                  ],
+                  "dtype": "<u1",
+                  "eopf_is_masked": true,
+                  "eopf_target_dtype": "<f4",
+                  "fill_value": 255,
+                  "long_name": "log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 16",
+                  "scale_factor": 0.008098999969661236,
+                  "short_name": "oa16_radiance_unc_orphans",
+                  "units": "lg(re mW.m-2.sr-1.nm-1)",
+                  "valid_max": 254,
+                  "valid_min": 0
+                },
+                "add_offset": -0.9848459959030151,
+                "coordinates": "altitude latitude longitude",
+                "dtype": "<u1",
+                "long_name": "log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 16",
+                "scale_factor": 0.008098999969661236,
+                "units": "lg(re mW.m-2.sr-1.nm-1)"
+              },
+              "chunks": [
+                16,
+                4
+              ],
+              "compressor": {
+                "blocksize": 0,
+                "clevel": 3,
+                "cname": "zstd",
+                "id": "blosc",
+                "shuffle": 2
+              },
+              "dtype": "|u1",
+              "fill_value": 255,
+              "filters": null,
+              "order": "C",
+              "shape": [
+                16,
+                4
+              ],
+              "zarr_format": 2
+            },
+            "oa17_radiance_unc": {
+              "attributes": {
+                "_ARRAY_DIMENSIONS": [
+                  "rows",
+                  "removed_pixels"
+                ],
+                "_eopf_attrs": {
+                  "add_offset": -0.9873489737510681,
+                  "coordinates": [
+                    "removed_pixels",
+                    "latitude",
+                    "longitude",
+                    "altitude"
+                  ],
+                  "dimensions": [
+                    "rows",
+                    "removed_pixels"
+                  ],
+                  "dtype": "<u1",
+                  "eopf_is_masked": true,
+                  "eopf_target_dtype": "<f4",
+                  "fill_value": 255,
+                  "long_name": "log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 17",
+                  "scale_factor": 0.008390000090003014,
+                  "short_name": "oa17_radiance_unc_orphans",
+                  "units": "lg(re mW.m-2.sr-1.nm-1)",
+                  "valid_max": 254,
+                  "valid_min": 0
+                },
+                "add_offset": -0.9873489737510681,
+                "coordinates": "altitude latitude longitude",
+                "dtype": "<u1",
+                "long_name": "log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 17",
+                "scale_factor": 0.008390000090003014,
+                "units": "lg(re mW.m-2.sr-1.nm-1)"
+              },
+              "chunks": [
+                16,
+                4
+              ],
+              "compressor": {
+                "blocksize": 0,
+                "clevel": 3,
+                "cname": "zstd",
+                "id": "blosc",
+                "shuffle": 2
+              },
+              "dtype": "|u1",
+              "fill_value": 255,
+              "filters": null,
+              "order": "C",
+              "shape": [
+                16,
+                4
+              ],
+              "zarr_format": 2
+            },
+            "oa18_radiance_unc": {
+              "attributes": {
+                "_ARRAY_DIMENSIONS": [
+                  "rows",
+                  "removed_pixels"
+                ],
+                "_eopf_attrs": {
+                  "add_offset": -0.7908260226249695,
+                  "coordinates": [
+                    "removed_pixels",
+                    "latitude",
+                    "longitude",
+                    "altitude"
+                  ],
+                  "dimensions": [
+                    "rows",
+                    "removed_pixels"
+                  ],
+                  "dtype": "<u1",
+                  "eopf_is_masked": true,
+                  "eopf_target_dtype": "<f4",
+                  "fill_value": 255,
+                  "long_name": "log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 18",
+                  "scale_factor": 0.007825000211596489,
+                  "short_name": "oa18_radiance_unc_orphans",
+                  "units": "lg(re mW.m-2.sr-1.nm-1)",
+                  "valid_max": 254,
+                  "valid_min": 0
+                },
+                "add_offset": -0.7908260226249695,
+                "coordinates": "altitude latitude longitude",
+                "dtype": "<u1",
+                "long_name": "log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 18",
+                "scale_factor": 0.007825000211596489,
+                "units": "lg(re mW.m-2.sr-1.nm-1)"
+              },
+              "chunks": [
+                16,
+                4
+              ],
+              "compressor": {
+                "blocksize": 0,
+                "clevel": 3,
+                "cname": "zstd",
+                "id": "blosc",
+                "shuffle": 2
+              },
+              "dtype": "|u1",
+              "fill_value": 255,
+              "filters": null,
+              "order": "C",
+              "shape": [
+                16,
+                4
+              ],
+              "zarr_format": 2
+            },
+            "oa19_radiance_unc": {
+              "attributes": {
+                "_ARRAY_DIMENSIONS": [
+                  "rows",
+                  "removed_pixels"
+                ],
+                "_eopf_attrs": {
+                  "add_offset": -0.8344600200653076,
+                  "coordinates": [
+                    "removed_pixels",
+                    "latitude",
+                    "longitude",
+                    "altitude"
+                  ],
+                  "dimensions": [
+                    "rows",
+                    "removed_pixels"
+                  ],
+                  "dtype": "<u1",
+                  "eopf_is_masked": true,
+                  "eopf_target_dtype": "<f4",
+                  "fill_value": 255,
+                  "long_name": "log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 19",
+                  "scale_factor": 0.007402999792248011,
+                  "short_name": "oa19_radiance_unc_orphans",
+                  "units": "lg(re mW.m-2.sr-1.nm-1)",
+                  "valid_max": 254,
+                  "valid_min": 0
+                },
+                "add_offset": -0.8344600200653076,
+                "coordinates": "altitude latitude longitude",
+                "dtype": "<u1",
+                "long_name": "log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 19",
+                "scale_factor": 0.007402999792248011,
+                "units": "lg(re mW.m-2.sr-1.nm-1)"
+              },
+              "chunks": [
+                16,
+                4
+              ],
+              "compressor": {
+                "blocksize": 0,
+                "clevel": 3,
+                "cname": "zstd",
+                "id": "blosc",
+                "shuffle": 2
+              },
+              "dtype": "|u1",
+              "fill_value": 255,
+              "filters": null,
+              "order": "C",
+              "shape": [
+                16,
+                4
+              ],
+              "zarr_format": 2
+            },
+            "oa20_radiance_unc": {
+              "attributes": {
+                "_ARRAY_DIMENSIONS": [
+                  "rows",
+                  "removed_pixels"
+                ],
+                "_eopf_attrs": {
+                  "add_offset": -0.9243649840354919,
+                  "coordinates": [
+                    "removed_pixels",
+                    "latitude",
+                    "longitude",
+                    "altitude"
+                  ],
+                  "dimensions": [
+                    "rows",
+                    "removed_pixels"
+                  ],
+                  "dtype": "<u1",
+                  "eopf_is_masked": true,
+                  "eopf_target_dtype": "<f4",
+                  "fill_value": 255,
+                  "long_name": "log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 20",
+                  "scale_factor": 0.008166000247001648,
+                  "short_name": "oa20_radiance_unc_orphans",
+                  "units": "lg(re mW.m-2.sr-1.nm-1)",
+                  "valid_max": 254,
+                  "valid_min": 0
+                },
+                "add_offset": -0.9243649840354919,
+                "coordinates": "altitude latitude longitude",
+                "dtype": "<u1",
+                "long_name": "log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 20",
+                "scale_factor": 0.008166000247001648,
+                "units": "lg(re mW.m-2.sr-1.nm-1)"
+              },
+              "chunks": [
+                16,
+                4
+              ],
+              "compressor": {
+                "blocksize": 0,
+                "clevel": 3,
+                "cname": "zstd",
+                "id": "blosc",
+                "shuffle": 2
+              },
+              "dtype": "|u1",
+              "fill_value": 255,
+              "filters": null,
+              "order": "C",
+              "shape": [
+                16,
+                4
+              ],
+              "zarr_format": 2
+            },
+            "oa21_radiance_unc": {
+              "attributes": {
+                "_ARRAY_DIMENSIONS": [
+                  "rows",
+                  "removed_pixels"
+                ],
+                "_eopf_attrs": {
+                  "add_offset": -0.5929380059242249,
+                  "coordinates": [
+                    "removed_pixels",
+                    "latitude",
+                    "longitude",
+                    "altitude"
+                  ],
+                  "dimensions": [
+                    "rows",
+                    "removed_pixels"
+                  ],
+                  "dtype": "<u1",
+                  "eopf_is_masked": true,
+                  "eopf_target_dtype": "<f4",
+                  "fill_value": 255,
+                  "long_name": "log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 21",
+                  "scale_factor": 0.0057830000296235085,
+                  "short_name": "oa21_radiance_unc_orphans",
+                  "units": "lg(re mW.m-2.sr-1.nm-1)",
+                  "valid_max": 254,
+                  "valid_min": 0
+                },
+                "add_offset": -0.5929380059242249,
+                "coordinates": "altitude latitude longitude",
+                "dtype": "<u1",
+                "long_name": "log10 scaled radiometric uncertainty estimate for removed pixels for OLCI acquisition band 21",
+                "scale_factor": 0.0057830000296235085,
+                "units": "lg(re mW.m-2.sr-1.nm-1)"
+              },
+              "chunks": [
+                16,
+                4
+              ],
+              "compressor": {
+                "blocksize": 0,
+                "clevel": 3,
+                "cname": "zstd",
+                "id": "blosc",
+                "shuffle": 2
+              },
+              "dtype": "|u1",
+              "fill_value": 255,
+              "filters": null,
+              "order": "C",
+              "shape": [
+                16,
+                4
+              ],
+              "zarr_format": 2
+            },
+            "quality_flags": {
+              "attributes": {
+                "_ARRAY_DIMENSIONS": [
+                  "rows",
+                  "removed_pixels"
+                ],
+                "_eopf_attrs": {
+                  "coordinates": [
+                    "removed_pixels",
+                    "latitude",
+                    "longitude",
+                    "altitude"
+                  ],
+                  "dimensions": [
+                    "rows",
+                    "removed_pixels"
+                  ],
+                  "dtype": "<u4",
+                  "eopf_is_masked": true,
+                  "flag_masks": [
+                    2147483648,
+                    1073741824,
+                    536870912,
+                    268435456,
+                    134217728,
+                    67108864,
+                    33554432,
+                    16777216,
+                    8388608,
+                    4194304,
+                    2097152,
+                    1048576,
+                    524288
+                  ],
+                  "flag_meanings": "land coastline fresh_inland_water tidal_region bright straylight_risk invalid cosmetic duplicated sun-glint_risk dubious saturated partially_saturated",
+                  "long_name": "classification and quality flags for removed pixels",
+                  "short_name": "quality_flags_orphans"
+                },
+                "coordinates": "altitude latitude longitude",
+                "dtype": "<u4",
+                "flag_masks": [
+                  2147483648,
+                  1073741824,
+                  536870912,
+                  268435456,
+                  134217728,
+                  67108864,
+                  33554432,
+                  16777216,
+                  8388608,
+                  4194304,
+                  2097152,
+                  1048576,
+                  524288
+                ],
+                "flag_meanings": "land coastline fresh_inland_water tidal_region bright straylight_risk invalid cosmetic duplicated sun-glint_risk dubious saturated partially_saturated",
+                "long_name": "classification and quality flags for removed pixels"
+              },
+              "chunks": [
+                16,
+                4
+              ],
+              "compressor": {
+                "blocksize": 0,
+                "clevel": 3,
+                "cname": "zstd",
+                "id": "blosc",
+                "shuffle": 2
+              },
+              "dtype": "<u4",
+              "fill_value": null,
+              "filters": null,
+              "order": "C",
+              "shape": [
+                16,
+                4
+              ],
+              "zarr_format": 2
+            },
+            "removed_pixels": {
+              "attributes": {
+                "_ARRAY_DIMENSIONS": [
+                  "removed_pixels"
+                ],
+                "dimensions": [
+                  "removed_pixels"
+                ],
+                "dtype": "<i8",
+                "long_name": "maximum removed pixels per line"
+              },
+              "chunks": [
+                4
+              ],
+              "compressor": {
+                "blocksize": 0,
+                "clevel": 3,
+                "cname": "zstd",
+                "id": "blosc",
+                "shuffle": 2
+              },
+              "dtype": "<i8",
+              "fill_value": null,
+              "filters": null,
+              "order": "C",
+              "shape": [
+                4
+              ],
+              "zarr_format": 2
+            }
+          },
+          "zarr_format": 2
+        },
+        "quality_flags": {
+          "attributes": {
+            "_ARRAY_DIMENSIONS": [
+              "rows",
+              "columns"
+            ],
+            "_eopf_attrs": {
+              "coordinates": [
+                "latitude",
+                "longitude",
+                "altitude",
+                "time_stamp"
+              ],
+              "dimensions": [
+                "rows",
+                "columns"
+              ],
+              "dtype": "<u4",
+              "eopf_is_masked": true,
+              "flag_masks": [
+                2147483648,
+                1073741824,
+                536870912,
+                268435456,
+                134217728,
+                67108864,
+                33554432,
+                16777216,
+                8388608,
+                4194304,
+                2097152,
+                1048576,
+                524288
+              ],
+              "flag_meanings": "land coastline fresh_inland_water tidal_region bright straylight_risk invalid cosmetic duplicated sun-glint_risk dubious saturated partially_saturated",
+              "long_name": "classification and quality flags",
+              "short_name": "quality_flags"
+            },
+            "coordinates": "altitude latitude longitude time_stamp",
+            "dtype": "<u4",
+            "flag_masks": [
+              2147483648,
+              1073741824,
+              536870912,
+              268435456,
+              134217728,
+              67108864,
+              33554432,
+              16777216,
+              8388608,
+              4194304,
+              2097152,
+              1048576,
+              524288
+            ],
+            "flag_meanings": "land coastline fresh_inland_water tidal_region bright straylight_risk invalid cosmetic duplicated sun-glint_risk dubious saturated partially_saturated",
+            "long_name": "classification and quality flags"
+          },
+          "chunks": [
+            16,
+            16
+          ],
+          "compressor": {
+            "blocksize": 0,
+            "clevel": 3,
+            "cname": "zstd",
+            "id": "blosc",
+            "shuffle": 2
+          },
+          "dtype": "<u4",
+          "fill_value": null,
+          "filters": null,
+          "order": "C",
+          "shape": [
+            16,
+            16
+          ],
+          "zarr_format": 2
+        },
+        "time_stamp": {
+          "attributes": {
+            "_ARRAY_DIMENSIONS": [
+              "rows"
+            ],
+            "calendar": "proleptic_gregorian",
+            "dimensions": [
+              "rows"
+            ],
+            "dtype": "<M8[ns]",
+            "fill_value": -1,
+            "long_name": "elapsed time since 01 Jan 2000 00:00:00 as time stamp for each row",
+            "standard_name": "time",
+            "units": "nanoseconds since 1970-01-01"
+          },
+          "chunks": [
+            16
+          ],
+          "compressor": {
+            "blocksize": 0,
+            "clevel": 3,
+            "cname": "zstd",
+            "id": "blosc",
+            "shuffle": 2
+          },
+          "dtype": "<i8",
+          "fill_value": null,
+          "filters": null,
+          "order": "C",
+          "shape": [
+            16
+          ],
+          "zarr_format": 2
+        }
+      },
+      "zarr_format": 2
+    }
+  },
+  "zarr_format": 2
+}
\ No newline at end of file
diff --git a/tests/conftest.py b/tests/conftest.py
index 59746f8f..ed76b929 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -13,6 +13,7 @@
 # Paths to example data / metadata
 s1_example_json_paths = tuple(pathlib.Path("tests/_test_data/s1_examples").glob("*.json"))
 s2_example_json_paths = tuple(pathlib.Path("tests/_test_data/s2_examples").glob("*.json"))
+s3_example_json_paths = tuple(pathlib.Path("tests/_test_data/s3_examples").glob("*.json"))
 projjson_example_paths = tuple(pathlib.Path("tests/_test_data/projjson_examples").glob("*.json"))
 geoproj_example_paths = tuple(pathlib.Path("tests/_test_data/geoproj_examples").glob("*.json"))
 geozarr_example_paths = tuple(pathlib.Path("tests/_test_data/geozarr_examples").glob("*.json"))
@@ -48,6 +49,12 @@ def create_group_from_json(source_path: pathlib.Path, out_path: pathlib.Path) ->
     return out_dir
 
 
+@pytest.fixture(params=s3_example_json_paths, ids=get_stem)
+def s3_olci_group_example(request: pytest.FixtureRequest, tmp_path: pathlib.Path) -> pathlib.Path:
+    """Path to a Zarr group with the layout of a Sentinel-3 OLCI product."""
+    return create_group_from_json(request.param, tmp_path)
+
+
 @pytest.fixture(params=s1_example_json_paths, ids=get_stem)
 def s1_group_example(request: pytest.FixtureRequest, tmp_path: pathlib.Path) -> pathlib.Path:
     """
diff --git a/tests/test_cli_convert_routing.py b/tests/test_cli_convert_routing.py
new file mode 100644
index 00000000..bfabd624
--- /dev/null
+++ b/tests/test_cli_convert_routing.py
@@ -0,0 +1,193 @@
+"""
+In-process tests for the convert command's Sentinel-2 auto-detection and routing.
+
+The e2e tests in test_cli_e2e.py drive the CLI through subprocesses; these tests
+call ``convert_command`` directly (with the converters stubbed out) so the
+routing logic itself is exercised in-process.
+"""
+
+import argparse
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import pytest
+import xarray as xr
+
+from eopf_geozarr import cli
+
+
+def _convert_args(input_path: str, output_path: str, **overrides: Any) -> argparse.Namespace:
+    """Build a Namespace mirroring the convert subcommand's defaults."""
+    ns = argparse.Namespace(
+        input_path=input_path,
+        output_path=output_path,
+        groups=["/measurements/reflectance/r10m"],
+        spatial_chunk=4096,
+        min_dimension=256,
+        max_retries=3,
+        crs_groups=None,
+        gcp_group=None,
+        verbose=False,
+        dask_cluster=False,
+        enable_sharding=False,
+        no_s2_optimized=False,
+        no_s3_olci_optimized=False,
+    )
+    for key, value in overrides.items():
+        setattr(ns, key, value)
+    return ns
+
+
+@pytest.fixture
+def plain_zarr_input(tmp_path: Path) -> Path:
+    """A minimal non-Sentinel-2 zarr store."""
+    path = tmp_path / "plain_input.zarr"
+    ds = xr.Dataset(
+        {"temperature": (["y", "x"], np.zeros((4, 4)))},
+        coords={"x": ("x", np.arange(4.0)), "y": ("y", np.arange(4.0))},
+    )
+    ds.to_zarr(path, zarr_format=3)
+    return path
+
+
+def test_is_sentinel2_input_true(s2_group_example: Path) -> None:
+    """A real Sentinel-2 layout is detected."""
+    dt = xr.open_datatree(str(s2_group_example), engine="zarr")
+    assert cli._is_sentinel2_input(dt) is True
+
+
+def test_is_sentinel2_input_false(plain_zarr_input: Path) -> None:
+    """A plain zarr store is not detected as Sentinel-2 (and never raises)."""
+    dt = xr.open_datatree(str(plain_zarr_input), engine="zarr")
+    assert cli._is_sentinel2_input(dt) is False
+
+
+def test_is_sentinel2_input_swallow_errors() -> None:
+    """Detection failures (e.g. in-memory datatree with no store) mean 'not S2'."""
+    dt = xr.DataTree(xr.Dataset({"a": (["y", "x"], np.zeros((2, 2)))}))
+    assert cli._is_sentinel2_input(dt) is False
+
+
+@pytest.fixture
+def converter_spy(monkeypatch: pytest.MonkeyPatch) -> dict[str, dict[str, Any]]:
+    """Stub out both converters, recording which one convert_command dispatches to."""
+    calls: dict[str, dict[str, Any]] = {}
+
+    def fake_s2(**kwargs: Any) -> xr.DataTree:
+        calls["s2_optimized"] = kwargs
+        return xr.DataTree()
+
+    def fake_generic(**kwargs: Any) -> xr.DataTree:
+        calls["generic"] = kwargs
+        return xr.DataTree()
+
+    def fake_olci(dt_input: xr.DataTree, **kwargs: Any) -> xr.DataTree:
+        calls["olci"] = {"dt_input": dt_input, **kwargs}
+        return xr.DataTree()
+
+    monkeypatch.setattr(cli, "convert_s2_optimized", fake_s2)
+    monkeypatch.setattr(cli, "create_geozarr_dataset", fake_generic)
+    monkeypatch.setattr(cli, "convert_olci_optimized", fake_olci)
+    return calls
+
+
+def test_convert_command_routes_s2_to_optimized(
+    s2_group_example: Path,
+    tmp_path: Path,
+    converter_spy: dict[str, dict[str, Any]],
+) -> None:
+    """Sentinel-2 inputs are auto-routed to the optimized converter."""
+    args = _convert_args(str(s2_group_example), str(tmp_path / "out.zarr"))
+    cli.convert_command(args)
+
+    assert "s2_optimized" in calls_or_fail(converter_spy)
+    assert "generic" not in converter_spy
+    assert converter_spy["s2_optimized"]["keep_scale_offset"] is False
+
+
+def test_convert_command_no_s2_optimized_forces_generic(
+    s2_group_example: Path,
+    tmp_path: Path,
+    converter_spy: dict[str, dict[str, Any]],
+) -> None:
+    """--no-s2-optimized sends Sentinel-2 inputs down the generic path."""
+    args = _convert_args(
+        str(s2_group_example),
+        str(tmp_path / "out.zarr"),
+        no_s2_optimized=True,
+        crs_groups=["/conditions/geometry"],
+    )
+    cli.convert_command(args)
+
+    assert "generic" in calls_or_fail(converter_spy)
+    assert "s2_optimized" not in converter_spy
+    assert converter_spy["generic"]["crs_groups"] == ["/conditions/geometry"]
+
+
+def test_convert_command_routes_olci_with_raw_input(
+    s3_olci_group_example: Path,
+    tmp_path: Path,
+    converter_spy: dict[str, dict[str, Any]],
+) -> None:
+    """Sentinel-3 OLCI inputs are auto-routed to the OLCI converter with raw input.
+
+    convert_olci_optimized requires the source opened with
+    ``mask_and_scale=False``: radiance must arrive packed (uint16 with CF
+    scale_factor/add_offset in .attrs), not CF-decoded to float.
+    """
+    args = _convert_args(str(s3_olci_group_example), str(tmp_path / "out.zarr"), min_dimension=128)
+    cli.convert_command(args)
+
+    assert "olci" in calls_or_fail(converter_spy)
+    assert "s2_optimized" not in converter_spy
+    assert "generic" not in converter_spy
+    assert converter_spy["olci"]["min_dimension"] == 128
+
+    dt_received = converter_spy["olci"]["dt_input"]
+    radiance = next(
+        var
+        for name, var in dt_received["/measurements"].data_vars.items()
+        if str(name).endswith("_radiance")
+    )
+    assert not np.issubdtype(radiance.dtype, np.floating), (
+        "OLCI converter received CF-decoded (mask_and_scale) input; expected raw packed radiance"
+    )
+    assert "scale_factor" in radiance.attrs
+
+
+def test_convert_command_no_s3_olci_optimized_forces_generic(
+    s3_olci_group_example: Path,
+    tmp_path: Path,
+    converter_spy: dict[str, dict[str, Any]],
+) -> None:
+    """--no-s3-olci-optimized sends Sentinel-3 OLCI inputs down the generic path."""
+    args = _convert_args(
+        str(s3_olci_group_example),
+        str(tmp_path / "out.zarr"),
+        no_s3_olci_optimized=True,
+    )
+    cli.convert_command(args)
+
+    assert "generic" in calls_or_fail(converter_spy)
+    assert "olci" not in converter_spy
+
+
+def test_convert_command_routes_non_s2_to_generic(
+    plain_zarr_input: Path,
+    tmp_path: Path,
+    converter_spy: dict[str, dict[str, Any]],
+) -> None:
+    """Non-Sentinel-2 inputs use the generic converter."""
+    args = _convert_args(str(plain_zarr_input), str(tmp_path / "out.zarr"), groups=["/"])
+    cli.convert_command(args)
+
+    assert "generic" in calls_or_fail(converter_spy)
+    assert "s2_optimized" not in converter_spy
+
+
+def calls_or_fail(calls: dict[str, dict[str, Any]]) -> dict[str, dict[str, Any]]:
+    """Fail loudly if convert_command dispatched to neither converter."""
+    if not calls:
+        pytest.fail("convert_command did not call any converter (it likely errored early)")
+    return calls
diff --git a/tests/test_cli_e2e.py b/tests/test_cli_e2e.py
index b91ce2b9..8b27b3f6 100644
--- a/tests/test_cli_e2e.py
+++ b/tests/test_cli_e2e.py
@@ -211,7 +211,9 @@ def test_cli_convert_with_crs_groups(s2_group_example: Path, tmp_path: Path) ->
     Test CLI conversion with --crs-groups option using real Sentinel-2 data.
 
     This test verifies that the --crs-groups option works correctly and
-    processes the specified groups for CRS enhancement.
+    processes the specified groups for CRS enhancement. It passes
+    --no-s2-optimized so the Sentinel-2 input goes through the generic
+    conversion path (the auto-detected optimized path ignores --crs-groups).
     """
     # Dataset from the notebook
 
@@ -242,6 +244,7 @@ def test_cli_convert_with_crs_groups(s2_group_example: Path, tmp_path: Path) ->
         "256",
         "--max-retries",
         "3",
+        "--no-s2-optimized",
         "--verbose",
     ]
 
diff --git a/tests/test_data_api/test_s1.py b/tests/test_data_api/test_s1.py
index 5e7ce77d..ed0c3a29 100644
--- a/tests/test_data_api/test_s1.py
+++ b/tests/test_data_api/test_s1.py
@@ -10,7 +10,14 @@
 from the markdown files in docs/models/sentinel1.md
 """
 
-from eopf_geozarr.data_api.s1 import Sentinel1Root
+import inspect
+from typing import Any
+
+import pytest
+
+import eopf_geozarr.data_api.s1 as s1_module
+from eopf_geozarr.data_api.s1 import Sentinel1PolarizationGroup, Sentinel1Root
+from eopf_geozarr.pyz.v2 import GroupSpec
 
 
 def test_sentinel1_roundtrip(s1_json_example: dict[str, object]) -> None:
@@ -19,3 +26,62 @@ def test_sentinel1_roundtrip(s1_json_example: dict[str, object]) -> None:
     dumped = model1.model_dump()
     model2 = Sentinel1Root.model_validate(dumped)
     assert model1.model_dump() == model2.model_dump()
+
+
+def _member_accessor_cases() -> list[Any]:
+    """Collect every generated member-accessor property on the S1 group models.
+
+    The S1 group classes define uniform properties of the shape
+    ``self.members.get(key)`` + ``raise KeyError(key)`` when absent; identify
+    them by their source so unrelated properties are not swept in.
+    """
+    cases: list[Any] = []
+    for cls_name, cls in inspect.getmembers(s1_module, inspect.isclass):
+        if cls.__module__ != s1_module.__name__ or not issubclass(cls, GroupSpec):
+            continue
+        for prop_name, prop in vars(cls).items():
+            if (
+                isinstance(prop, property)
+                and prop.fget is not None
+                and "raise KeyError" in inspect.getsource(prop.fget)
+            ):
+                cases.append(pytest.param(cls, prop_name, id=f"{cls_name}.{prop_name}"))
+    return cases
+
+
+@pytest.mark.parametrize(("cls", "prop_name"), _member_accessor_cases())
+def test_member_accessor(cls: type[GroupSpec[Any, Any]], prop_name: str) -> None:
+    """Member accessors return the member when present and raise KeyError when absent."""
+    empty = cls.model_construct(members={})
+    with pytest.raises(KeyError) as excinfo:
+        getattr(empty, prop_name)
+    member_key = excinfo.value.args[0]
+
+    sentinel = object()
+    populated = cls.model_construct(members={member_key: sentinel})
+    assert getattr(populated, prop_name) is sentinel
+
+
+def test_polarization_group_helpers(s1_json_example: dict[str, object]) -> None:
+    """The root model's polarization lookups find the VH and VV groups."""
+    model = Sentinel1Root.model_validate(s1_json_example)
+
+    pols = model.get_polarization_groups()
+    assert pols
+    assert all(isinstance(group, Sentinel1PolarizationGroup) for group in pols.values())
+
+    vh = model.get_vh_group()
+    assert vh is not None
+    assert any("VH" in name for name in pols)
+
+    vv = model.get_vv_group()
+    assert vv is not None
+    assert any("VV" in name for name in pols)
+
+
+def test_polarization_group_helpers_empty() -> None:
+    """The polarization lookups return None / empty on a root with no members."""
+    empty = Sentinel1Root.model_construct(members={})
+    assert empty.get_polarization_groups() == {}
+    assert empty.get_vh_group() is None
+    assert empty.get_vv_group() is None
diff --git a/tests/test_data_api/test_s3_olci.py b/tests/test_data_api/test_s3_olci.py
new file mode 100644
index 00000000..0625ec84
--- /dev/null
+++ b/tests/test_data_api/test_s3_olci.py
@@ -0,0 +1,132 @@
+"""Tests for the Sentinel-3 OLCI data-api model."""
+
+import pytest
+from pydantic import ValidationError
+
+from eopf_geozarr.data_api.s3_olci import Sentinel3OlciRoot
+from eopf_geozarr.pyz.v2 import ArraySpec, GroupSpec  # noqa: F401
+
+
+def _olci_arr() -> dict[str, object]:
+    """Return a minimal v2 ArraySpec-shaped dict for a 2-D uint16 array."""
+    return ArraySpec(
+        shape=(4, 5),
+        chunks=(4, 5),
+        dtype="<u2",
+        fill_value=0,
+        attributes={"_ARRAY_DIMENSIONS": ["rows", "columns"]},
+    ).model_dump()
+
+
+def test_validates_minimal_olci_product() -> None:
+    """A dict with 21 radiance bands + geolocation validates as Sentinel3OlciRoot."""
+    radiance = {f"oa{i:02d}_radiance": _olci_arr() for i in range(1, 22)}
+    coords = {c: _olci_arr() for c in ("latitude", "longitude", "altitude")}
+    root = {
+        "zarr_format": 2,
+        "attributes": {"other_metadata": {}, "stac_discovery": {}},
+        "members": {
+            "measurements": {
+                "zarr_format": 2,
+                "attributes": {},
+                "members": {**radiance, **coords},
+            },
+        },
+    }
+    model = Sentinel3OlciRoot.model_validate(root)
+    assert "oa01_radiance" in model.measurements.members
+
+
+def test_rejects_non_olci_product() -> None:
+    """A dict with an unexpected key in measurements is rejected by closed=True."""
+    not_olci = {
+        "zarr_format": 2,
+        "attributes": {"other_metadata": {}, "stac_discovery": {}},
+        "members": {
+            "measurements": {
+                "zarr_format": 2,
+                "attributes": {},
+                "members": {
+                    "reflectance": {
+                        "zarr_format": 2,
+                        "attributes": {},
+                        "members": {},
+                    }
+                },
+            }
+        },
+    }
+    with pytest.raises(ValidationError):
+        Sentinel3OlciRoot.model_validate(not_olci)
+
+
+def test_detector_accepts_olci_zarr(tmp_path: object) -> None:
+    import pathlib
+
+    import zarr
+
+    from eopf_geozarr.s3_olci_optimization.olci_converter import (
+        is_sentinel3_olci_dataset,
+    )
+
+    # build a minimal OLCI zarr v2 store from the model dict used above
+    radiance = {f"oa{i:02d}_radiance": _olci_arr() for i in range(1, 22)}
+    coords = {c: _olci_arr() for c in ("latitude", "longitude", "altitude")}
+    root_dict = {
+        "zarr_format": 2,
+        "attributes": {"other_metadata": {}, "stac_discovery": {}},
+        "members": {
+            "measurements": {
+                "zarr_format": 2,
+                "attributes": {},
+                "members": {**radiance, **coords},
+            }
+        },
+    }
+    assert isinstance(tmp_path, pathlib.Path)
+    out = tmp_path / "olci.zarr"
+    Sentinel3OlciRoot.model_validate(root_dict).to_zarr(out, path="")  # type: ignore[arg-type]
+    group = zarr.open_group(str(out), mode="r")
+    assert is_sentinel3_olci_dataset(group) is True
+
+
+def test_detector_rejects_s2_zarr(s2_group_example: object) -> None:
+    import pathlib
+
+    import zarr
+
+    from eopf_geozarr.s3_olci_optimization.olci_converter import (
+        is_sentinel3_olci_dataset,
+    )
+
+    assert isinstance(s2_group_example, pathlib.Path)
+    group = zarr.open_group(str(s2_group_example), mode="r")
+    assert is_sentinel3_olci_dataset(group) is False
+
+
+def test_real_olci_product_is_detected(s3_olci_group_example: object) -> None:
+    import pathlib
+
+    import zarr
+
+    from eopf_geozarr.s3_olci_optimization.olci_converter import (
+        is_sentinel3_olci_dataset,
+    )
+
+    assert isinstance(s3_olci_group_example, pathlib.Path)
+    group = zarr.open_group(str(s3_olci_group_example), mode="r")
+    assert is_sentinel3_olci_dataset(group) is True
+
+
+def test_real_olci_product_validates_model(s3_olci_group_example: object) -> None:
+    import pathlib
+
+    import zarr
+
+    from eopf_geozarr.data_api.s3_olci import Sentinel3OlciRoot
+    from eopf_geozarr.pyz.v2 import GroupSpec as PyzGroupSpec
+
+    assert isinstance(s3_olci_group_example, pathlib.Path)
+    group = zarr.open_group(str(s3_olci_group_example), mode="r")
+    model = Sentinel3OlciRoot.model_validate(PyzGroupSpec.from_zarr(group).model_dump())
+    assert "oa01_radiance" in model.measurements.members
diff --git a/tests/test_data_api/test_v2.py b/tests/test_data_api/test_v2.py
index 78bdc161..dd4c6941 100644
--- a/tests/test_data_api/test_v2.py
+++ b/tests/test_data_api/test_v2.py
@@ -8,8 +8,10 @@
 from pydantic import ValidationError
 from pydantic_zarr.v2 import GroupSpec
 
+from eopf_geozarr.data_api.geozarr.common import DatasetAttrs
 from eopf_geozarr.data_api.geozarr.v2 import (
     DataArray,
+    Dataset,
     check_valid_coordinates,
 )
 
@@ -72,3 +74,30 @@ def test_invalid_coordinates(
         msg = "Dimension .* for array 'base' has a shape mismatch:"
         with pytest.raises(ValueError, match=msg):
             check_valid_coordinates(cast("GroupLike", group))
+
+
+class TestDataset:
+    @staticmethod
+    def _members() -> dict[str, DataArray]:
+        base_array = DataArray.from_array(
+            np.zeros((4, 4), dtype="uint8"), dimension_names=["y", "x"]
+        )
+        coords_arrays = {
+            name: DataArray.from_array(np.arange(4), dimension_names=(name,)) for name in ("y", "x")
+        }
+        return {"band": base_array, **coords_arrays}
+
+    def test_valid(self) -> None:
+        """A dataset with consistent coordinates and no grid mapping validates."""
+        ds = Dataset(attributes=DatasetAttrs(), members=self._members())
+        assert isinstance(ds, Dataset)
+
+    def test_missing_grid_mapping_variable(self) -> None:
+        """A member declaring a grid_mapping that is not in the dataset fails validation."""
+        members = self._members()
+        band = members["band"]
+        members["band"] = band.model_copy(
+            update={"attributes": band.attributes.model_copy(update={"grid_mapping": "nope"})}
+        )
+        with pytest.raises(ValidationError, match="Grid mapping variable 'nope'"):
+            Dataset(attributes=DatasetAttrs(), members=members)
diff --git a/tests/test_data_api/test_v3.py b/tests/test_data_api/test_v3.py
index c2a16fa4..51c85419 100644
--- a/tests/test_data_api/test_v3.py
+++ b/tests/test_data_api/test_v3.py
@@ -4,11 +4,14 @@
 import numpy as np
 import pytest
 import zarr
+from pydantic import ValidationError
 from pydantic_zarr.core import tuplify_json
 from pydantic_zarr.v3 import ArraySpec, GroupSpec
 
+from eopf_geozarr.data_api.geozarr.common import DatasetAttrs
 from eopf_geozarr.data_api.geozarr.v3 import (
     DataArray,
+    Dataset,
     MultiscaleGroup,
     check_valid_coordinates,
 )
@@ -94,3 +97,30 @@ def test_multiscale_attrs_round_trip(s2_geozarr_group_example: zarr.Group) -> No
             assert tuplify_json(MultiscaleGroup(**model_json).model_dump()) == tuplify_json(
                 model_json
             )
+
+
+class TestDataset:
+    @staticmethod
+    def _members() -> dict[str, DataArray]:
+        base_array = DataArray.from_array(
+            np.zeros((4, 4), dtype="uint8"), dimension_names=["y", "x"]
+        )
+        coords_arrays = {
+            name: DataArray.from_array(np.arange(4), dimension_names=(name,)) for name in ("y", "x")
+        }
+        return {"band": base_array, **coords_arrays}
+
+    def test_valid(self) -> None:
+        """A dataset with consistent coordinates and no grid mapping validates."""
+        ds = Dataset(attributes=DatasetAttrs(), members=self._members())
+        assert isinstance(ds, Dataset)
+
+    def test_missing_grid_mapping_variable(self) -> None:
+        """A member declaring a grid_mapping that is not in the dataset fails validation."""
+        members = self._members()
+        band = members["band"]
+        members["band"] = band.model_copy(
+            update={"attributes": band.attributes.model_copy(update={"grid_mapping": "nope"})}
+        )
+        with pytest.raises(ValidationError, match="Grid mapping variable 'nope'"):
+            Dataset(attributes=DatasetAttrs(), members=members)
diff --git a/tests/test_geozarr_output_contract.py b/tests/test_geozarr_output_contract.py
new file mode 100644
index 00000000..f968c938
--- /dev/null
+++ b/tests/test_geozarr_output_contract.py
@@ -0,0 +1,157 @@
+"""Cross-product output contract.
+
+Reprojection to a regular grid with a declared CRS is a hard requirement for
+every converted product (see
+docs/superpowers/specs/2026-07-27-s3-olci-reprojection-design.md). One
+parametrized test walks each converter's output and asserts the contract at
+every multiscale level. Products whose converter has not yet migrated off the
+legacy nested layout carry an xfail on the whole-store DataTree check so the
+requirement stays on record.
+
+Per-level CRS detection requires opening each level with
+``decode_coords="all"`` (the generic converter's own read path uses the same
+flag, see geozarr.py) so the CF `grid_mapping`/`spatial_ref` reference gets
+promoted into a coordinate; the default `xr.open_dataset` decode never does
+this. Separately, S1/S2 overview sub-groups (r2, r4, ...) still lack the
+zarr-cm proj:/spatial: convention attrs that OLCI stamps on every level — a
+real design-consistency gap, but not one that defeats CRS detection here, so
+it is tracked outside this test rather than xfailed.
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+from unittest.mock import patch
+
+import numpy as np
+import pytest
+import rioxarray  # noqa: F401
+import xarray as xr
+import zarr
+from pyproj import CRS as ProjCRS
+
+from eopf_geozarr.conversion import create_geozarr_dataset
+from eopf_geozarr.s3_olci_optimization.olci_converter import convert_olci_optimized
+
+from .test_integration_sentinel1 import MockSentinel1L1GRDBuilder
+from .test_integration_sentinel2 import build_sample_sentinel2_datatree
+from .test_olci_integration import build_synthetic_olci
+
+if TYPE_CHECKING:
+    import pathlib
+    from collections.abc import Callable
+
+
+def _convert_olci(tmp: pathlib.Path) -> pathlib.Path:
+    out = tmp / "olci.zarr"
+    convert_olci_optimized(
+        build_synthetic_olci(rows=256, cols=256), output_path=str(out), min_dimension=64
+    )
+    return out
+
+
+def _convert_s1(tmp: pathlib.Path) -> pathlib.Path:
+    out = tmp / "s1.zarr"
+    dt = MockSentinel1L1GRDBuilder("20170508T164830_0025_A094_8604_01B54C").build()
+    with patch("eopf_geozarr.conversion.geozarr.print"):
+        create_geozarr_dataset(
+            dt,
+            groups=["measurements"],
+            output_path=str(out),
+            gcp_group="conditions/gcp",
+        )
+    return out
+
+
+def _convert_s2(tmp: pathlib.Path) -> pathlib.Path:
+    out = tmp / "s2.zarr"
+    with patch("eopf_geozarr.conversion.geozarr.print"):
+        create_geozarr_dataset(
+            build_sample_sentinel2_datatree(),
+            groups=["/measurements/reflectance/r10m"],
+            output_path=str(out),
+        )
+    return out
+
+
+PRODUCT_CONVERTERS: dict[str, Callable[[pathlib.Path], pathlib.Path]] = {
+    "olci": _convert_olci,
+    "s1": _convert_s1,
+    "s2": _convert_s2,
+}
+
+#: Converters that still write the legacy layout (native at group root with
+#: asset "."), which xr.open_datatree rejects. The xfail keeps the hard
+#: requirement on record until the generic converter migrates (cf. PR #212).
+DATATREE_XFAIL: dict[str, str] = {
+    "s1": "generic converter still writes asset='.' nested overview layout",
+    "s2": "generic converter still writes asset='.' nested overview layout",
+}
+
+
+@pytest.fixture(scope="module", params=sorted(PRODUCT_CONVERTERS), ids=str)
+def converted_store(
+    request: pytest.FixtureRequest, tmp_path_factory: pytest.TempPathFactory
+) -> tuple[pathlib.Path, str]:
+    name = str(request.param)
+    store = PRODUCT_CONVERTERS[name](tmp_path_factory.mktemp(name))
+    return store, name
+
+
+def _multiscale_level_paths(store: pathlib.Path) -> list[str]:
+    """Every pyramid-level group path, discovered via multiscales layout attrs."""
+    root = zarr.open_group(str(store), mode="r")
+    found: list[str] = []
+
+    def walk(group: zarr.Group, path: str) -> None:
+        attrs = dict(group.attrs)
+        multiscales = attrs.get("multiscales")
+        if isinstance(multiscales, dict):
+            layout = multiscales.get("layout")
+            assert isinstance(layout, list)
+            for entry in layout:
+                assert isinstance(entry, dict)
+                asset = entry["asset"]
+                assert isinstance(asset, str)
+                found.append(path if asset == "." else f"{path}/{asset}" if path else asset)
+        for key in group.group_keys():
+            child = group[key]
+            assert isinstance(child, zarr.Group)
+            walk(child, f"{path}/{key}" if path else key)
+
+    walk(root, "")
+    return found
+
+
+def test_every_level_is_regular_grid_with_crs(
+    converted_store: tuple[pathlib.Path, str],
+) -> None:
+    """Hard requirement: each pyramid level is a regular grid with a real CRS."""
+    store, name = converted_store
+    levels = _multiscale_level_paths(store)
+    assert levels, f"{name}: no multiscale groups found in {store}"
+    for level in levels:
+        ds = xr.open_dataset(
+            str(store), group=level, engine="zarr", consolidated=False, decode_coords="all"
+        )
+        crs = ds.rio.crs
+        assert crs is not None, f"{name}:{level}: no CRS declared"
+        # CRS round-trips through pyproj
+        ProjCRS.from_user_input(crs.to_wkt())
+        for dim in ("y", "x"):
+            assert dim in ds.sizes, f"{name}:{level}: missing spatial dim {dim}"
+            coord = ds[dim]
+            assert coord.ndim == 1, f"{name}:{level}: {dim} coordinate is not 1-D"
+            steps = np.diff(coord.values)
+            assert np.allclose(steps, steps[0]), (
+                f"{name}:{level}: {dim} coordinate spacing is not regular"
+            )
+        ds.close()
+
+
+def test_store_opens_as_datatree(converted_store: tuple[pathlib.Path, str]) -> None:
+    """Hard requirement: the whole store opens with xr.open_datatree."""
+    store, name = converted_store
+    if name in DATATREE_XFAIL:
+        pytest.xfail(DATATREE_XFAIL[name])
+    xr.open_datatree(str(store), engine="zarr", consolidated=False, chunks={})
diff --git a/tests/test_integration_sentinel2.py b/tests/test_integration_sentinel2.py
index 91b6b7bc..96cf50ba 100644
--- a/tests/test_integration_sentinel2.py
+++ b/tests/test_integration_sentinel2.py
@@ -27,10 +27,8 @@
 )
 
 
-@pytest.fixture
-def sample_sentinel2_datatree() -> xr.DataTree:
-    """
-    Create a sample Sentinel-2 EOPF DataTree structure for testing.
+def build_sample_sentinel2_datatree() -> xr.DataTree:
+    """Build the sample Sentinel-2 EOPF DataTree (importable, non-fixture form).
 
     This mimics the structure from the notebook:
     - Multiple resolution groups (r10m, r20m, r60m)
@@ -221,6 +219,12 @@ def sample_sentinel2_datatree() -> xr.DataTree:
     return dt
 
 
+@pytest.fixture
+def sample_sentinel2_datatree() -> xr.DataTree:
+    """Create a sample Sentinel-2 EOPF DataTree structure for testing."""
+    return build_sample_sentinel2_datatree()
+
+
 @pytest.fixture
 def temp_output_dir() -> Generator[str, None, None]:
     """Create a temporary directory for test outputs."""
diff --git a/tests/test_olci_band_mapping.py b/tests/test_olci_band_mapping.py
new file mode 100644
index 00000000..8fca56c7
--- /dev/null
+++ b/tests/test_olci_band_mapping.py
@@ -0,0 +1,25 @@
+from eopf_geozarr.s3_olci_optimization.olci_band_mapping import (
+    OLCI_BAND_INFO,
+    OLCI_BANDS,
+    RADIANCE_DTYPE,
+    OlciBandInfo,
+)
+
+
+def test_there_are_21_olci_bands() -> None:
+    assert len(OLCI_BANDS) == 21
+    assert OLCI_BANDS[0] == "oa01_radiance"
+    assert OLCI_BANDS[-1] == "oa21_radiance"
+
+
+def test_every_band_has_info() -> None:
+    assert set(OLCI_BAND_INFO) == set(OLCI_BANDS)
+    for name, info in OLCI_BAND_INFO.items():
+        assert isinstance(info, OlciBandInfo)
+        assert info.name == name
+        assert info.data_type == RADIANCE_DTYPE
+        assert info.wavelength_center > 0
+
+
+def test_first_band_wavelength() -> None:
+    assert OLCI_BAND_INFO["oa01_radiance"].wavelength_center == 400.0
diff --git a/tests/test_olci_integration.py b/tests/test_olci_integration.py
new file mode 100644
index 00000000..c40ab17f
--- /dev/null
+++ b/tests/test_olci_integration.py
@@ -0,0 +1,526 @@
+"""Integration tests for convert_olci_optimized."""
+
+from __future__ import annotations
+
+import json
+import subprocess
+import sys
+from pathlib import Path
+from typing import TYPE_CHECKING
+
+import numpy as np
+import rioxarray  # noqa: F401
+import xarray as xr
+import zarr
+from pydantic_zarr.core import tuplify_json
+from pydantic_zarr.v3 import GroupSpec
+
+from eopf_geozarr.s3_olci_optimization.olci_converter import (
+    _sanitize_olci_array_attrs_keep_fill,
+)
+
+if TYPE_CHECKING:
+    import pathlib
+
+from eopf_geozarr.s3_olci_optimization.olci_converter import convert_olci_optimized
+
+
+def build_synthetic_olci(rows: int = 512, cols: int = 480) -> xr.DataTree:
+    """Minimal synthetic OLCI L1 EFR datatree (measurements only).
+
+    Geolocation is an axis-aligned ~300 m grid (0.003 deg spacing) so the
+    dataset is genuinely warpable to a regular lat/lon grid.
+    """
+    rng = np.random.default_rng(0)
+    lat_1d = np.linspace(45.0, 45.0 + 0.003 * (rows - 1), rows)
+    lon_1d = np.linspace(10.0, 10.0 + 0.003 * (cols - 1), cols)
+    lat = np.repeat(lat_1d[:, None], cols, axis=1)
+    lon = np.repeat(lon_1d[None, :], rows, axis=0)
+    alt = np.zeros((rows, cols), dtype="int16")
+    data: dict[str, xr.DataArray] = {}
+    for i in range(1, 22):
+        name = f"oa{i:02d}_radiance"
+        arr = xr.DataArray(
+            rng.integers(0, 6000, (rows, cols)).astype("uint16"),
+            dims=("rows", "columns"),
+            attrs={
+                "scale_factor": 0.0139,
+                "add_offset": 0.0,
+                "standard_name": "toa_upwelling_spectral_radiance",
+                "coordinates": "latitude longitude altitude",
+            },
+        )
+        data[name] = arr
+    ds = xr.Dataset(
+        data,
+        coords={
+            "latitude": (("rows", "columns"), lat, {"standard_name": "latitude"}),
+            "longitude": (("rows", "columns"), lon, {"standard_name": "longitude"}),
+            "altitude": (("rows", "columns"), alt, {"standard_name": "altitude"}),
+        },
+    )
+    return xr.DataTree.from_dict({"/measurements": ds})
+
+
+def test_convert_olci_writes_measurements(tmp_path: object) -> None:
+    """Native-resolution measurements group must contain all 21 radiance bands."""
+    import zarr
+
+    dt = build_synthetic_olci()
+    out = str(tmp_path / "olci_geozarr.zarr")  # type: ignore[operator]
+    convert_olci_optimized(dt, output_path=out)
+
+    g = zarr.open_group(out, mode="r")
+    # native measurements present
+    assert "measurements" in g
+    # all 21 bands at native res, in the r0 base-level group
+    meas = g["measurements"]
+    assert isinstance(meas, zarr.Group)
+    r0 = meas["r0"]
+    assert isinstance(r0, zarr.Group)
+    for i in range(1, 22):
+        assert f"oa{i:02d}_radiance" in r0
+
+
+def test_convert_olci_creates_overviews(tmp_path: object) -> None:
+    """Overview subgroups must only be written when BOTH post-decimation dims >= min_dimension.
+
+    Case 1 — 512x480 with min_dimension=256:
+        480//2 = 240 < 256, so the guard fires immediately → zero overview levels.
+
+    Case 2 — 1024x1024 with min_dimension=256:
+        the warped grid stays close to 1024x1024, yielding exactly two
+        overview levels; each halves the previous and the deepest would drop
+        below min_dimension if halved again.
+    """
+    import zarr
+
+    # --- Case 1: 512x480, min_dimension=256 → zero overview levels ---
+    dt1 = build_synthetic_olci(rows=512, cols=480)
+    out1 = str(tmp_path / "olci_case1.zarr")  # type: ignore[operator]
+    convert_olci_optimized(dt1, output_path=out1, min_dimension=256)
+
+    g1 = zarr.open_group(out1, mode="r")
+    meas1 = g1["measurements"]
+    assert isinstance(meas1, zarr.Group)
+    subgroups1 = sorted(meas1.group_keys())
+    assert subgroups1 == ["r0"], (
+        f"Expected only the r0 base level for 512x480 at min_dimension=256, got {subgroups1}"
+    )
+
+    # --- Case 2: 1024x1024, min_dimension=256 → exactly two valid levels ---
+    dt2 = build_synthetic_olci(rows=1024, cols=1024)
+    out2 = str(tmp_path / "olci_case2.zarr")  # type: ignore[operator]
+    convert_olci_optimized(dt2, output_path=out2, min_dimension=256)
+
+    g2 = zarr.open_group(out2, mode="r")
+    meas2 = g2["measurements"]
+    assert isinstance(meas2, zarr.Group)
+    subgroups2 = sorted(meas2.group_keys())
+    assert subgroups2 == ["r0", "r2", "r4"], (
+        f"Expected r0 + exactly 2 overview levels for 1024x1024 at min_dimension=256, "
+        f"got {subgroups2}"
+    )
+
+    # Every level must have BOTH spatial dims >= min_dimension, and each
+    # overview must be exactly half the previous level (floor division).
+    r0_2 = meas2["r0"]
+    assert isinstance(r0_2, zarr.Group)
+    band0 = r0_2["oa01_radiance"]
+    assert isinstance(band0, zarr.Array)
+    prev_shape = band0.shape
+    for sg_name in [k for k in subgroups2 if k != "r0"]:
+        sg = meas2[sg_name]
+        assert isinstance(sg, zarr.Group)
+        band = sg["oa01_radiance"]
+        assert isinstance(band, zarr.Array)
+        assert band.shape[0] == prev_shape[0] // 2
+        assert band.shape[1] == prev_shape[1] // 2
+        assert band.shape[0] >= 256
+        assert band.shape[1] >= 256
+        prev_shape = band.shape
+
+    # The deepest level would violate min_dimension if halved again.
+    assert prev_shape[0] // 2 < 256 or prev_shape[1] // 2 < 256
+
+
+def test_convert_olci_returns_datatree(tmp_path: object) -> None:
+    """convert_olci_optimized must return an xr.DataTree backed by the output store."""
+    dt = build_synthetic_olci(rows=256, cols=256)
+    out = str(tmp_path / "olci_geozarr.zarr")  # type: ignore[operator]
+    result = convert_olci_optimized(dt, output_path=out, min_dimension=256)
+    assert isinstance(result, xr.DataTree)
+    assert "/measurements" in result.groups
+
+
+def test_convert_olci_output_opens_as_datatree(tmp_path: object) -> None:
+    """Acceptance: the exported store is a regular grid with CRS at every level.
+
+    xr.open_datatree must open the whole store; measurements/r0 holds the
+    warped native-resolution grid with 1-D y/x coordinates and a declared
+    CRS, overview siblings halve it, and the multiscales layout references
+    the base level by name.
+    """
+    dt = build_synthetic_olci(rows=1024, cols=1024)
+    out = str(tmp_path / "olci_geozarr.zarr")  # type: ignore[operator]
+    result = convert_olci_optimized(dt, output_path=out, min_dimension=256)
+
+    opened = xr.open_datatree(out, engine="zarr", consolidated=False, chunks={})
+
+    r0 = opened["/measurements/r0"].to_dataset()
+    assert set(r0.sizes) == {"y", "x"}
+    for i in range(1, 22):
+        assert f"oa{i:02d}_radiance" in r0
+    # CRS declared at every level, 1-D regular coordinates
+    for level in ("r0", "r2", "r4"):
+        ds = opened[f"/measurements/{level}"].to_dataset()
+        assert ds.rio.crs is not None, f"{level}: no CRS"
+        assert ds.rio.crs.to_epsg() == 4326
+        for dim in ("y", "x"):
+            coord = ds[dim]
+            assert coord.ndim == 1
+            steps = np.diff(coord.values)
+            assert np.allclose(steps, steps[0])
+        band = ds["oa01_radiance"]
+        assert band.attrs["grid_mapping"] == "spatial_ref"
+    # halving structure relative to observed r0
+    r2 = opened["/measurements/r2"].to_dataset()
+    assert r2.sizes["y"] == r0.sizes["y"] // 2
+    assert r2.sizes["x"] == r0.sizes["x"] // 2
+
+    # measurements itself holds only convention metadata
+    meas = opened["/measurements"].to_dataset()
+    assert len(meas.data_vars) == 0
+
+    meas_attrs = dict(zarr.open_group(out, mode="r")["measurements"].attrs)
+    multiscales = meas_attrs["multiscales"]
+    assert isinstance(multiscales, dict)
+    layout = multiscales["layout"]
+    assert isinstance(layout, list)
+    assert layout[0] == {"asset": "r0"}
+    # per-level geo-proj convention present
+    meas_group = zarr.open_group(out, mode="r")["measurements"]
+    assert isinstance(meas_group, zarr.Group)
+    r0_group = meas_group["r0"]
+    assert isinstance(r0_group, zarr.Group)
+    r0_attrs = dict(r0_group.attrs)
+    assert "proj:code" in r0_attrs
+
+    assert "/measurements/r0" in result.groups
+    assert "/measurements/r2" in result.groups
+
+
+def test_convert_olci_conditions_quality_passthrough(tmp_path: object) -> None:
+    """conditions and quality groups, when present, are copied through unchanged."""
+    import zarr
+
+    # Build a tree with conditions and quality groups
+    rng = np.random.default_rng(1)
+    rows, cols = 128, 128
+    lat_1d = np.linspace(45.0, 45.0 + 0.003 * (rows - 1), rows)
+    lon_1d = np.linspace(10.0, 10.0 + 0.003 * (cols - 1), cols)
+    lat = np.repeat(lat_1d[:, None], cols, axis=1)
+    lon = np.repeat(lon_1d[None, :], rows, axis=0)
+    alt = np.zeros((rows, cols), dtype="int16")
+
+    meas_data: dict[str, xr.DataArray] = {
+        "oa01_radiance": xr.DataArray(
+            rng.integers(0, 6000, (rows, cols)).astype("uint16"),
+            dims=("rows", "columns"),
+        )
+    }
+    meas_ds = xr.Dataset(
+        meas_data,
+        coords={
+            "latitude": (("rows", "columns"), lat),
+            "longitude": (("rows", "columns"), lon),
+            "altitude": (("rows", "columns"), alt),
+        },
+    )
+    cond_ds = xr.Dataset(
+        {
+            "wind_speed": xr.DataArray(
+                rng.random((rows, cols)).astype("float32"), dims=("rows", "columns")
+            )
+        }
+    )
+    quality_ds = xr.Dataset(
+        {
+            "flags": xr.DataArray(
+                rng.integers(0, 255, (rows, cols)).astype("uint8"), dims=("rows", "columns")
+            )
+        }
+    )
+    # Build a measurements/orphans sub-dataset to verify child subgroup copy.
+    orphans_ds = xr.Dataset(
+        {
+            "removed_count": xr.DataArray(
+                rng.integers(0, 10, (rows,)).astype("int32"), dims=("rows",)
+            )
+        }
+    )
+    dt = xr.DataTree.from_dict(
+        {
+            "/measurements": meas_ds,
+            "/measurements/orphans": orphans_ds,
+            "/conditions": cond_ds,
+            "/quality": quality_ds,
+        }
+    )
+
+    out = str(tmp_path / "olci_geozarr.zarr")  # type: ignore[operator]
+    convert_olci_optimized(dt, output_path=out, min_dimension=64)
+
+    g = zarr.open_group(out, mode="r")
+    assert "conditions" in g
+    assert "quality" in g
+    # measurements/orphans subgroup must have been copied through.
+    assert "orphans" in g["measurements"]
+
+
+def test_cli_convert_s3_olci_optimized(tmp_path: pathlib.Path) -> None:
+    """convert-s3-olci-optimized subcommand must write a GeoZarr measurements group."""
+    import zarr
+
+    # materialise a synthetic OLCI product to a zarr v2 store on disk
+    dt = build_synthetic_olci(rows=300, cols=300)
+    src = tmp_path / "olci_src.zarr"
+    dt.to_zarr(src, mode="w", consolidated=False)
+    out = tmp_path / "olci_out.zarr"
+    result = subprocess.run(
+        [
+            sys.executable,
+            "-m",
+            "eopf_geozarr",
+            "convert-s3-olci-optimized",
+            str(src),
+            str(out),
+            "--spatial-chunk",
+            "256",
+        ],
+        capture_output=True,
+        text=True,
+        timeout=300,
+    )
+    assert result.returncode == 0, result.stdout + result.stderr
+    g = zarr.open_group(str(out), mode="r")
+    assert "measurements" in g
+
+
+def _assert_radiance_dtype_and_attrs(
+    group: zarr.Group, band_name: str, *, level_label: str
+) -> None:
+    """Assert that *band_name* in *group* is uint16 with scale_factor and no stale attrs.
+
+    This is a regression guard: the converter must preserve raw integer storage
+    and CF scale/offset, and must strip source-only attrs (_eopf_attrs, dtype,
+    valid_min, valid_max).
+    """
+    band = group[band_name]
+    assert isinstance(band, zarr.Array), f"{level_label}/{band_name} is not a zarr.Array"
+    assert band.dtype == np.dtype("uint16"), (
+        f"{level_label}/{band_name}: expected uint16, got {band.dtype}"
+    )
+    attrs = dict(band.attrs)
+    assert "scale_factor" in attrs, (
+        f"{level_label}/{band_name}: scale_factor missing from attrs (got {list(attrs)})"
+    )
+    for stale_key in ("_eopf_attrs", "dtype", "valid_min", "valid_max"):
+        assert stale_key not in attrs, (
+            f"{level_label}/{band_name}: stale attr '{stale_key}' present in output attrs"
+        )
+
+
+def test_olci_conversion_matches_snapshot(
+    s3_olci_group_example: pathlib.Path,
+    tmp_path: pathlib.Path,
+) -> None:
+    """Snapshot test: converted OLCI structure must match committed golden file.
+
+    The fixture is a Zarr v2 store representing a real OLCI L1 EFR product.
+    We open the whole DataTree so that conditions, quality, and
+    measurements/orphans subgroups are included in the conversion.
+    The fixture uses the real product's dimension names: tie-point grids reuse
+    'columns' (at size 4) while measurement grids also use 'columns' (at size 16);
+    orphan arrays use removed_pixels=4.
+
+    ``min_dimension=8`` is used so that the 16x16 measurements grid generates
+    one overview level (r2 at 8x8).
+
+    To (re)generate the snapshot, uncomment the regeneration block below,
+    run the test once, then re-comment before committing.
+    """
+    # The JSON fixture materializes arrays as zeros; zero lat/lon is a
+    # degenerate geolocation the warp rejects. Seed a plausible grid.
+    #
+    # NOTE: the fixture's latitude/longitude arrays are stored raw as int32
+    # with a CF scale_factor of 1e-6 (real degrees = raw * scale_factor), and
+    # this test opens the datatree with mask_and_scale=False, so xarray hands
+    # reproject_olci the *raw* int32 values unscaled. A ~300 m (0.003 deg)
+    # spacing collapses to a single truncated integer across this fixture's
+    # small 16-row/16-col grid, which is degenerate. Use whole-degree spacing
+    # instead so each row/column truncates to a distinct raw value.
+    fixture_group = zarr.open_group(str(s3_olci_group_example), mode="a")
+    fixture_meas = fixture_group["measurements"]
+    assert isinstance(fixture_meas, zarr.Group)
+    lat_arr = fixture_meas["latitude"]
+    assert isinstance(lat_arr, zarr.Array)
+    ny, nx = lat_arr.shape
+    lat_1d = np.linspace(45.0, 45.0 + 1.0 * (ny - 1), ny)
+    lon_1d = np.linspace(10.0, 10.0 + 1.0 * (nx - 1), nx)
+    lat_arr[:] = np.repeat(lat_1d[:, None], nx, axis=1)
+    lon_arr = fixture_meas["longitude"]
+    assert isinstance(lon_arr, zarr.Array)
+    lon_arr[:] = np.repeat(lon_1d[None, :], ny, axis=0)
+
+    dt_in = xr.open_datatree(
+        str(s3_olci_group_example),
+        engine="zarr",
+        consolidated=False,
+        chunks={},
+        mask_and_scale=False,
+    )
+    out = str(tmp_path / "out.zarr")
+    convert_olci_optimized(dt_in, output_path=out, min_dimension=8)
+
+    observed_group = zarr.open_group(out, use_consolidated=False)
+    observed_structure_json = GroupSpec.from_zarr(observed_group).model_dump()
+
+    expected_path = Path("tests/_test_data/optimized_olci_examples") / (
+        s3_olci_group_example.stem + ".json"
+    )
+
+    # Uncomment this block to (re)generate the snapshot from the observed structure.
+    # expected_path.parent.mkdir(parents=True, exist_ok=True)
+    # expected_path.write_text(json.dumps(observed_structure_json, indent=2, sort_keys=True))
+
+    observed_structure = GroupSpec(**tuplify_json(observed_structure_json))
+    observed_structure_flat = observed_structure.to_flat()
+    expected_structure_json = tuplify_json(json.loads(expected_path.read_text()))
+    expected_structure = GroupSpec(**expected_structure_json)
+    expected_structure_flat = expected_structure.to_flat()
+
+    o_keys = set(observed_structure_flat.keys())
+    e_keys = set(expected_structure_flat.keys())
+    assert o_keys == e_keys
+    assert [k for k in o_keys if observed_structure_flat[k] != expected_structure_flat[k]] == []
+
+    # Dtype/attrs regression guard: radiance must be stored as uint16 with
+    # scale_factor preserved and stale source attrs absent.
+    meas_g = observed_group["measurements"]
+    assert isinstance(meas_g, zarr.Group)
+    r0_g = meas_g["r0"]
+    assert isinstance(r0_g, zarr.Group)
+    _assert_radiance_dtype_and_attrs(r0_g, "oa01_radiance", level_label="r0")
+    if "r2" in meas_g:
+        r2_g = meas_g["r2"]
+        assert isinstance(r2_g, zarr.Group)
+        _assert_radiance_dtype_and_attrs(r2_g, "oa01_radiance", level_label="r2")
+
+
+def test_convert_olci_odd_dims_overview_no_conflicting_sizes(tmp_path: object) -> None:
+    """Overview groups written from an odd-dimensioned swath must open without errors.
+
+    Regression test for the off-by-one bug in reduce_swath where coordinate
+    decimation via [::factor] produced ceil(N/factor) elements while
+    coarsen(boundary="trim") produced floor(N/factor) for the radiance data.
+    On an odd-column real OLCI product (4865 cols) this caused xr.open_dataset
+    to raise ``ValueError: conflicting sizes for dimension 'columns'``.
+
+    We use rows=10, cols=9 (odd cols) with min_dimension=4. Once warped to a
+    regular grid the exact base size need not match the swath input, so this
+    asserts coord/data length agreement and floor-halving structure at every
+    level rather than a specific (5, 4) shape.
+    """
+    dt = build_synthetic_olci(rows=10, cols=9)
+    out = str(tmp_path / "odd_olci.zarr")  # type: ignore[operator]
+    convert_olci_optimized(dt, output_path=out, min_dimension=4)
+
+    # Determine which overview groups were written.
+    import zarr as _zarr
+
+    g = _zarr.open_group(out, mode="r")
+    meas = g["measurements"]
+    assert isinstance(meas, _zarr.Group)
+    level_keys = sorted(meas.group_keys())
+    assert level_keys[0] == "r0"
+
+    # Open each level (base + overviews); this must NOT raise a
+    # conflicting-sizes error, and each level must floor-halve the previous.
+    prev_shape: tuple[int, ...] | None = None
+    for lvl in level_keys:
+        ds = xr.open_dataset(out, engine="zarr", group=f"measurements/{lvl}", consolidated=False)
+        rad_shape = ds["oa01_radiance"].shape
+        assert rad_shape == (ds["y"].size, ds["x"].size), (
+            f"measurements/{lvl}: data shape {rad_shape} != coord sizes "
+            f"(y={ds['y'].size}, x={ds['x'].size})"
+        )
+        if prev_shape is not None:
+            assert rad_shape[0] == prev_shape[0] // 2
+            assert rad_shape[1] == prev_shape[1] // 2
+        prev_shape = rad_shape
+        ds.close()
+
+
+def test_sanitize_olci_array_attrs_strips_stale_keeps_fill_value() -> None:
+    """_sanitize_olci_array_attrs_keep_fill must strip stale source attrs and preserve _FillValue.
+
+    Unlike the shared sanitize_array_attrs (which always strips _FillValue),
+    the OLCI-local helper must preserve _FillValue so that downstream readers
+    and reduce_swath can identify fill pixels on raw uint16 data opened with
+    mask_and_scale=False.
+    """
+    attrs: dict[str, object] = {
+        "_eopf_attrs": {"source": "some blob"},
+        "dtype": "uint16",
+        "valid_min": 0,
+        "valid_max": 65534,
+        "scale_factor": 0.0139,
+        "add_offset": 0.0,
+        "_FillValue": 65535,
+        "units": "W m-2 sr-1 um-1",
+        "standard_name": "toa_upwelling_spectral_radiance",
+        "coordinates": "latitude longitude altitude",
+    }
+    result = _sanitize_olci_array_attrs_keep_fill(attrs)
+    # Stale source-only attrs must be removed.
+    assert "_eopf_attrs" not in result, "_eopf_attrs must be stripped"
+    assert "dtype" not in result, "dtype must be stripped"
+    assert "valid_min" not in result, "valid_min must be stripped"
+    assert "valid_max" not in result, "valid_max must be stripped"
+    # CF and fill attrs must be preserved.
+    assert result.get("scale_factor") == 0.0139, "scale_factor must be preserved"
+    assert result.get("add_offset") == 0.0, "add_offset must be preserved"
+    assert result.get("_FillValue") == 65535, "_FillValue must be preserved for OLCI raw uint16"
+    assert result.get("units") == "W m-2 sr-1 um-1", "units must be preserved"
+    assert result.get("standard_name") == "toa_upwelling_spectral_radiance"
+    assert result.get("coordinates") == "latitude longitude altitude"
+
+
+def test_convert_olci_rerun_removes_stale_overview_groups(tmp_path: object) -> None:
+    """Re-running against an existing output path must not leave stale groups.
+
+    The first run (small min_dimension) produces more overview levels than the
+    second; without up-front store truncation the extra r{N} groups from run
+    one would survive run two.
+    """
+    import zarr
+
+    out = str(tmp_path / "olci_geozarr.zarr")  # type: ignore[operator]
+    convert_olci_optimized(
+        build_synthetic_olci(rows=256, cols=256), output_path=out, min_dimension=64
+    )
+    root = zarr.open_group(out, mode="r")
+    meas = root["measurements"]
+    assert isinstance(meas, zarr.Group)
+    levels_first = {k for k in meas.group_keys() if k.startswith("r")}
+    assert "r4" in levels_first
+
+    convert_olci_optimized(
+        build_synthetic_olci(rows=256, cols=256), output_path=out, min_dimension=128
+    )
+    root = zarr.open_group(out, mode="r")
+    meas = root["measurements"]
+    assert isinstance(meas, zarr.Group)
+    levels_second = {k for k in meas.group_keys() if k.startswith("r")}
+    assert levels_second == {"r0", "r2"}, f"stale overview groups survived re-run: {levels_second}"
diff --git a/tests/test_olci_multiscale.py b/tests/test_olci_multiscale.py
new file mode 100644
index 00000000..88a8db44
--- /dev/null
+++ b/tests/test_olci_multiscale.py
@@ -0,0 +1,437 @@
+"""Tests for olci_multiscale: decimate_swath, reduce_swath, grid_spatial_attrs."""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+import rasterio.transform
+import xarray as xr
+
+from eopf_geozarr.s3_olci_optimization.olci_multiscale import (
+    decimate_swath,
+    grid_spatial_attrs,
+    reduce_swath,
+)
+
+
+def _geodesic_center(lat_deg: np.ndarray, lon_deg: np.ndarray) -> tuple[float, float]:
+    """Reference spherical centroid of a set of lat/lon positions, in degrees."""
+    lat = np.deg2rad(np.asarray(lat_deg, dtype="float64"))
+    lon = np.deg2rad(np.asarray(lon_deg, dtype="float64"))
+    x = float((np.cos(lat) * np.cos(lon)).mean())
+    y = float((np.cos(lat) * np.sin(lon)).mean())
+    z = float(np.sin(lat).mean())
+    return (
+        float(np.rad2deg(np.arctan2(z, np.hypot(x, y)))),
+        float(np.rad2deg(np.arctan2(y, x))),
+    )
+
+
+def _swath(rows: int = 8, cols: int = 6) -> xr.Dataset:
+    """Minimal synthetic swath dataset with one radiance band and two coords."""
+    rad = xr.DataArray(
+        np.arange(rows * cols, dtype="uint16").reshape(rows, cols),
+        dims=("rows", "columns"),
+        attrs={
+            "scale_factor": 0.5,
+            "units": "mW.m-2.sr-1.nm-1",
+            "_FillValue": 65535,
+        },
+    )
+    lat = xr.DataArray(
+        np.linspace(0, 1, rows * cols).reshape(rows, cols),
+        dims=("rows", "columns"),
+        attrs={"standard_name": "latitude"},
+    )
+    lon = xr.DataArray(
+        np.linspace(10, 11, rows * cols).reshape(rows, cols),
+        dims=("rows", "columns"),
+        attrs={"standard_name": "longitude"},
+    )
+    return xr.Dataset(
+        {"oa01_radiance": rad},
+        coords={"latitude": lat, "longitude": lon},
+    )
+
+
+# ---------------------------------------------------------------------------
+# decimate_swath tests
+# ---------------------------------------------------------------------------
+
+
+def test_decimate_halves_each_axis() -> None:
+    out = decimate_swath(_swath(8, 6), factor=2)
+    assert out["oa01_radiance"].shape == (4, 3)
+    assert out["latitude"].shape == (4, 3)
+    assert out["longitude"].shape == (4, 3)
+
+
+def test_decimate_takes_every_other_pixel() -> None:
+    ds = _swath(8, 6)
+    out = decimate_swath(ds, factor=2)
+    # top-left pixel is preserved exactly (no averaging)
+    assert int(out["oa01_radiance"].values[0, 0]) == 0
+    assert float(out["latitude"].values[0, 0]) == 0.0
+    # interior pixel: stride-2 decimation means out[1, 1] comes from original [2, 2]
+    assert int(out["oa01_radiance"].values[1, 1]) == int(ds["oa01_radiance"].values[2, 2])
+
+
+def test_decimate_preserves_attrs() -> None:
+    out = decimate_swath(_swath(8, 6), factor=2)
+    assert out["oa01_radiance"].attrs["scale_factor"] == 0.5
+    assert out["latitude"].attrs["standard_name"] == "latitude"
+
+
+def test_decimate_factor_1_returns_unchanged() -> None:
+    ds = _swath(8, 6)
+    out = decimate_swath(ds, factor=1)
+    assert out["oa01_radiance"].shape == (8, 6)
+
+
+def test_decimate_invalid_factor_raises() -> None:
+    with pytest.raises(ValueError, match="factor must be >= 1"):
+        decimate_swath(_swath(), factor=0)
+
+
+# ---------------------------------------------------------------------------
+# reduce_swath tests
+# ---------------------------------------------------------------------------
+
+
+def test_reduce_swath_halves_each_axis() -> None:
+    """reduce_swath must produce output with halved spatial dims."""
+    out = reduce_swath(_swath(8, 6), factor=2)
+    assert out["oa01_radiance"].shape == (4, 3)
+    assert out["latitude"].shape == (4, 3)
+    assert out["longitude"].shape == (4, 3)
+
+
+def test_reduce_swath_radiance_is_averaged_not_decimated() -> None:
+    """Radiance must be block-averaged; top-left output != top-left input (unless accident)."""
+    rng = np.random.default_rng(42)
+    rad_data = rng.integers(100, 200, (8, 6)).astype("uint16")
+    rad = xr.DataArray(
+        rad_data,
+        dims=("rows", "columns"),
+        attrs={"_FillValue": 65535},
+    )
+    ds = xr.Dataset({"oa01_radiance": rad})
+    out = reduce_swath(ds, factor=2)
+    # block [0:2, 0:2] averages to a value; verify it's a rounded mean
+    expected_block = int(np.round(rad_data[0:2, 0:2].astype("float64").mean()))
+    assert int(out["oa01_radiance"].values[0, 0]) == expected_block
+
+
+def test_reduce_swath_altitude_is_block_averaged() -> None:
+    """Altitude gets the fill-aware block mean, not stride decimation.
+
+    A stride sample would locate the overview cell's altitude ~half a block
+    away from the geodesic-centroid lat/lon describing the same cell.
+    """
+    fill = -32768
+    alt = xr.DataArray(
+        np.array([[100, 200], [300, fill]], dtype="int16"),
+        dims=("rows", "columns"),
+        attrs={"standard_name": "altitude", "_FillValue": fill},
+    )
+    lat = xr.DataArray(
+        np.zeros((2, 2)), dims=("rows", "columns"), attrs={"standard_name": "latitude"}
+    )
+    lon = xr.DataArray(
+        np.zeros((2, 2)), dims=("rows", "columns"), attrs={"standard_name": "longitude"}
+    )
+    ds = xr.Dataset(coords={"latitude": lat, "longitude": lon, "altitude": alt})
+    out = reduce_swath(ds, factor=2)
+    # Mean of the three valid pixels (fill excluded), not alt[0, 0] == 100.
+    assert int(out["altitude"].values[0, 0]) == 200
+    assert out["altitude"].dtype == np.dtype("int16")
+    assert out["altitude"].attrs["standard_name"] == "altitude"
+
+
+def test_reduce_swath_fill_value_preserved_in_all_fill_block() -> None:
+    """A block where all pixels are fill must produce fill output, not 65535.0 average."""
+    fill = 65535
+    rad_data = np.ones((4, 4), dtype="uint16") * fill
+    # put some non-fill values only in lower-right block
+    rad_data[2:4, 2:4] = 100
+    rad = xr.DataArray(
+        rad_data,
+        dims=("rows", "columns"),
+        attrs={"_FillValue": fill},
+    )
+    ds = xr.Dataset({"oa01_radiance": rad})
+    out = reduce_swath(ds, factor=2)
+    # top-left block: all fill -> output must be fill
+    assert int(out["oa01_radiance"].values[0, 0]) == fill
+    # bottom-right block: all 100 -> output must be 100
+    assert int(out["oa01_radiance"].values[1, 1]) == 100
+
+
+def test_reduce_swath_preserves_attrs() -> None:
+    """reduce_swath must carry over variable attributes."""
+    out = reduce_swath(_swath(8, 6), factor=2)
+    assert out["oa01_radiance"].attrs["scale_factor"] == 0.5
+    assert out["latitude"].attrs["standard_name"] == "latitude"
+
+
+def test_reduce_swath_factor_1_returns_unchanged() -> None:
+    ds = _swath(8, 6)
+    out = reduce_swath(ds, factor=1)
+    assert out["oa01_radiance"].shape == (8, 6)
+
+
+def test_reduce_swath_invalid_factor_raises() -> None:
+    with pytest.raises(ValueError, match="factor must be >= 1"):
+        reduce_swath(_swath(), factor=0)
+
+
+def test_reduce_swath_non_swath_var_passthrough() -> None:
+    """Variables that don't span (rows, columns) must pass through unchanged."""
+    ds = _swath(8, 6)
+    scalar = xr.DataArray(42.0, attrs={"info": "scalar"})
+    ds = ds.assign({"extra": scalar})
+    out = reduce_swath(ds, factor=2)
+    assert "extra" in out
+    assert float(out["extra"].values) == 42.0
+
+
+# ---------------------------------------------------------------------------
+# odd-dimension regression tests (real OLCI: 4865 columns is odd)
+# ---------------------------------------------------------------------------
+
+
+def _swath_odd(rows: int = 7, cols: int = 5) -> xr.Dataset:
+    """Synthetic swath with ODD spatial dimensions.
+
+    Matches the real-world scenario where OLCI products have 4865 columns
+    (odd).  Before the fix, reduce_swath on an odd-sized dimension produced
+    coordinate arrays one element longer than the corresponding radiance data
+    (ceil vs floor of N/factor), causing xr.open_dataset to raise a
+    conflicting-sizes error.
+    """
+    fill = 65535
+    rad = xr.DataArray(
+        np.arange(rows * cols, dtype="uint16").reshape(rows, cols),
+        dims=("rows", "columns"),
+        attrs={
+            "scale_factor": 0.5,
+            "units": "mW.m-2.sr-1.nm-1",
+            "_FillValue": fill,
+        },
+    )
+    lat = xr.DataArray(
+        np.linspace(0, 1, rows * cols).reshape(rows, cols),
+        dims=("rows", "columns"),
+        attrs={"standard_name": "latitude"},
+    )
+    lon = xr.DataArray(
+        np.linspace(10, 11, rows * cols).reshape(rows, cols),
+        dims=("rows", "columns"),
+        attrs={"standard_name": "longitude"},
+    )
+    return xr.Dataset(
+        {"oa01_radiance": rad},
+        coords={"latitude": lat, "longitude": lon},
+    )
+
+
+def test_reduce_swath_odd_dims_consistent_shape() -> None:
+    """reduce_swath must produce identical shapes for radiance AND coordinates on odd dims.
+
+    Regression test for the off-by-one bug where coordinate decimation via
+    [::factor] yields ceil(N/factor) but coarsen(boundary="trim") yields
+    floor(N/factor).  For rows=7, cols=5, factor=2 the expected output shape
+    is (floor(7/2), floor(5/2)) = (3, 2).
+    """
+    ds = _swath_odd(rows=7, cols=5)
+    out = reduce_swath(ds, factor=2)
+
+    expected_rows = 7 // 2  # 3
+    expected_cols = 5 // 2  # 2
+
+    assert out["oa01_radiance"].shape == (expected_rows, expected_cols), (
+        f"radiance shape {out['oa01_radiance'].shape} != ({expected_rows}, {expected_cols})"
+    )
+    assert out["latitude"].shape == (expected_rows, expected_cols), (
+        f"latitude shape {out['latitude'].shape} != ({expected_rows}, {expected_cols})"
+    )
+    assert out["longitude"].shape == (expected_rows, expected_cols), (
+        f"longitude shape {out['longitude'].shape} != ({expected_rows}, {expected_cols})"
+    )
+
+
+def test_reduce_swath_odd_dims_radiance_is_block_averaged() -> None:
+    """Radiance values must be block-averaged (not decimated) on odd-dim inputs."""
+    rng = np.random.default_rng(7)
+    rows, cols = 7, 5
+    rad_data = rng.integers(100, 200, (rows, cols)).astype("uint16")
+    rad = xr.DataArray(
+        rad_data,
+        dims=("rows", "columns"),
+        attrs={"_FillValue": 65535},
+    )
+    ds = xr.Dataset({"oa01_radiance": rad})
+    out = reduce_swath(ds, factor=2)
+    # The top-left output pixel must be the rounded mean of the 2x2 input block.
+    expected = int(np.round(rad_data[0:2, 0:2].astype("float64").mean()))
+    assert int(out["oa01_radiance"].values[0, 0]) == expected
+
+
+def test_reduce_swath_odd_simulates_real_olci_columns() -> None:
+    """Simulate the real-world OLCI case: 4090x4865 (odd cols) -> both 2432 cols.
+
+    Uses smaller proxy dimensions that are proportionally odd to avoid
+    heavy memory use: rows=10, cols=9 with factor=2 must yield (5, 4) for
+    both radiance and coordinates.  This specifically guards floor vs ceil
+    on the cols dimension (9 // 2 = 4, not 5).
+    """
+    ds = _swath_odd(rows=10, cols=9)
+    out = reduce_swath(ds, factor=2)
+    expected = (10 // 2, 9 // 2)  # (5, 4)
+    assert out["oa01_radiance"].shape == expected, (
+        f"radiance shape {out['oa01_radiance'].shape} != {expected}"
+    )
+    assert out["latitude"].shape == expected, (
+        f"latitude shape {out['latitude'].shape} != {expected}"
+    )
+    assert out["longitude"].shape == expected, (
+        f"longitude shape {out['longitude'].shape} != {expected}"
+    )
+
+
+# ---------------------------------------------------------------------------
+# grid dims tests (for Task 4: reprojected pyramid)
+# ---------------------------------------------------------------------------
+
+
+def test_reduce_swath_on_grid_dims() -> None:
+    """reduce_swath(dims=("y","x")) block-averages bands and strides 1-D coords.
+
+    After reprojection the pyramid dims are (y, x): bands average fill-aware,
+    the 1-D dimension coordinates decimate by trimmed stride, and non-spatial
+    variables (spatial_ref) pass through unchanged.
+    """
+    ny, nx = 6, 5  # odd x exercises the coarsen-trim alignment
+    band = np.arange(ny * nx, dtype="uint16").reshape(ny, nx)
+    ds = xr.Dataset(
+        {"oa01_radiance": (("y", "x"), band, {"_FillValue": 65535})},
+        coords={
+            "y": ("y", np.linspace(46.0, 45.0, ny)),
+            "x": ("x", np.linspace(10.0, 11.0, nx)),
+            "spatial_ref": ((), 0, {"crs_wkt": "stub"}),
+        },
+    )
+    out = reduce_swath(ds, factor=2, dims=("y", "x"))
+    assert dict(out.sizes) == {"y": 3, "x": 2}
+    # block mean of the top-left 2x2 block, rounded
+    expected00 = round((band[0, 0] + band[0, 1] + band[1, 0] + band[1, 1]) / 4)
+    assert int(out["oa01_radiance"].values[0, 0]) == expected00
+    # 1-D coords: trimmed stride, lengths match the data
+    assert out["y"].size == 3
+    assert out["x"].size == 2
+    np.testing.assert_allclose(out["x"].values, ds["x"].values[0:4:2])
+    # scalar passthrough survives
+    assert "spatial_ref" in out.coords
+    assert out["spatial_ref"].attrs["crs_wkt"] == "stub"
+
+
+# ---------------------------------------------------------------------------
+# grid_spatial_attrs tests
+# ---------------------------------------------------------------------------
+
+
+def test_grid_spatial_attrs() -> None:
+    """grid_spatial_attrs derives dimensions, bbox, and 6-element transform."""
+    transform = rasterio.transform.from_origin(10.0, 46.0, 0.01, 0.01)
+    attrs = grid_spatial_attrs(transform, (100, 200))
+    assert attrs["spatial:dimensions"] == ["y", "x"]
+    assert attrs["spatial:registration"] == "pixel"  # type: ignore[index]
+    assert attrs["spatial:transform"] == [  # type: ignore[index]
+        0.01,
+        0.0,
+        10.0,
+        0.0,
+        -0.01,
+        46.0,
+    ]
+    # bbox is [xmin, ymin, xmax, ymax] from array_bounds
+    assert attrs["spatial:bbox"] == [10.0, 45.0, 12.0, 46.0]  # type: ignore[index]
+
+
+def test_reduce_fill_collision_nudged_not_recoded_as_fill() -> None:
+    """A valid block whose mean rounds to the fill sentinel must not become fill.
+
+    Uses a sentinel interior to the data range (a bound sentinel such as 0 or
+    65535 cannot be reached by a mean of valid values that all sit on one side
+    of it): values [999, 1001, 999, 1001] average exactly to _FillValue=1000
+    and must be nudged to the neighboring in-range value instead.
+    """
+    rows, cols = 2, 2
+    rad = xr.DataArray(
+        np.array([[999, 1001], [999, 1001]], dtype="uint16"),
+        dims=("rows", "columns"),
+        attrs={"_FillValue": 1000},
+    )
+    lat = xr.DataArray(np.zeros((rows, cols)), dims=("rows", "columns"))
+    lon = xr.DataArray(np.zeros((rows, cols)), dims=("rows", "columns"))
+    ds = xr.Dataset({"oa01_radiance": rad}, coords={"latitude": lat, "longitude": lon})
+
+    out = reduce_swath(ds, factor=2)
+    value = int(out["oa01_radiance"].values[0, 0])
+    assert value != 1000, "valid block was recoded as fill"
+    assert value == 999  # unrounded mean == sentinel → nudged one step down
+
+
+def test_reduce_all_fill_block_stays_fill() -> None:
+    """An all-fill block keeps the sentinel value in the overview."""
+    rad = xr.DataArray(
+        np.full((2, 2), 1000, dtype="uint16"),
+        dims=("rows", "columns"),
+        attrs={"_FillValue": 1000},
+    )
+    lat = xr.DataArray(np.zeros((2, 2)), dims=("rows", "columns"))
+    lon = xr.DataArray(np.zeros((2, 2)), dims=("rows", "columns"))
+    ds = xr.Dataset({"oa01_radiance": rad}, coords={"latitude": lat, "longitude": lon})
+
+    out = reduce_swath(ds, factor=2)
+    assert int(out["oa01_radiance"].values[0, 0]) == 1000
+
+
+def test_reduce_partially_filled_block_averages_valid_pixels_only() -> None:
+    """A block mixing fill and valid pixels averages only the valid pixels.
+
+    Block [[100, 200], [300, fill]] with _FillValue=65535 must average the
+    three valid pixels (200), not collapse to fill or include the sentinel.
+    """
+    rad = xr.DataArray(
+        np.array([[100, 200], [300, 65535]], dtype="uint16"),
+        dims=("rows", "columns"),
+        attrs={"_FillValue": 65535},
+    )
+    lat = xr.DataArray(np.zeros((2, 2)), dims=("rows", "columns"))
+    lon = xr.DataArray(np.zeros((2, 2)), dims=("rows", "columns"))
+    ds = xr.Dataset({"oa01_radiance": rad}, coords={"latitude": lat, "longitude": lon})
+
+    out = reduce_swath(ds, factor=2)
+    assert int(out["oa01_radiance"].values[0, 0]) == 200
+
+
+def test_reduce_transposed_band_still_block_averaged() -> None:
+    """A radiance band stored as (columns, rows) is averaged, not decimated.
+
+    Swath detection is order-insensitive: the transposed band is normalized to
+    (rows, columns) and fill-aware block-averaged. Stride decimation would
+    keep the block's origin value (10) instead of the block mean (25).
+    """
+    rad = xr.DataArray(
+        np.array([[10, 20], [30, 40]], dtype="uint16"),
+        dims=("columns", "rows"),
+        attrs={"_FillValue": 65535},
+    )
+    lat = xr.DataArray(np.zeros((2, 2)), dims=("rows", "columns"))
+    lon = xr.DataArray(np.zeros((2, 2)), dims=("rows", "columns"))
+    ds = xr.Dataset({"oa01_radiance": rad}, coords={"latitude": lat, "longitude": lon})
+
+    out = reduce_swath(ds, factor=2)
+    assert tuple(out["oa01_radiance"].dims) == ("rows", "columns")
+    assert int(out["oa01_radiance"].values[0, 0]) == 25
diff --git a/tests/test_olci_reproject.py b/tests/test_olci_reproject.py
new file mode 100644
index 00000000..33aa30fd
--- /dev/null
+++ b/tests/test_olci_reproject.py
@@ -0,0 +1,148 @@
+"""Tests for OLCI swath -> regular grid reprojection."""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+import xarray as xr
+
+from eopf_geozarr.s3_olci_optimization.olci_reproject import reproject_olci
+
+FILL = 65535
+
+
+def build_rotated_swath(rows: int = 64, cols: int = 60, angle_deg: float = 30.0) -> xr.Dataset:
+    """Synthetic OLCI-like swath: regular grid rotated by *angle_deg* in lon/lat.
+
+    Rotation matters: it makes the dst bounding-box corners fall outside the
+    swath, exercising real geolocation warping and off-swath fill.
+    """
+    rr, cc = np.meshgrid(
+        np.arange(rows, dtype="float64"), np.arange(cols, dtype="float64"), indexing="ij"
+    )
+    theta = np.deg2rad(angle_deg)
+    lon = 10.0 + 0.01 * (cc * np.cos(theta) - rr * np.sin(theta))
+    lat = 45.0 + 0.01 * (cc * np.sin(theta) + rr * np.cos(theta))
+
+    band = (100.0 * rr + cc).astype("uint16")
+    band[:4, :4] = FILL  # a filled block inside the swath
+    da = xr.DataArray(
+        band,
+        dims=("rows", "columns"),
+        attrs={"scale_factor": 0.0139, "add_offset": 0.0, "_FillValue": FILL},
+    )
+    # float32 band with no pre-existing _FillValue: nodata defaults to NaN.
+    solar_flux = xr.DataArray(
+        (rr + cc).astype("float32"),
+        dims=("rows", "columns"),
+    )
+    time_stamp = xr.DataArray(np.arange(rows).astype("datetime64[ns]"), dims=("rows",))
+    return xr.Dataset(
+        {"oa01_radiance": da, "solar_flux_proxy": solar_flux},
+        coords={
+            "latitude": (("rows", "columns"), lat),
+            "longitude": (("rows", "columns"), lon),
+            "altitude": (("rows", "columns"), np.full((rows, cols), 7, dtype="int16")),
+            "time_stamp": time_stamp,
+        },
+    )
+
+
+def test_reproject_olci_produces_regular_grid_with_crs() -> None:
+    """One behavior test: grid shape, CRS, dtype, attrs, fill, and dropped vars."""
+    ds = build_rotated_swath()
+    out = reproject_olci(ds)
+
+    # Regular 1-D coordinate grid over (y, x)
+    assert set(out.sizes) == {"y", "x"}
+    for dim in ("y", "x"):
+        coord = out[dim]
+        assert coord.ndim == 1
+        steps = np.diff(coord.values)
+        assert np.allclose(steps, steps[0])
+    # y descends (north-up grid)
+    assert out["y"].values[0] > out["y"].values[-1]
+
+    # CRS declared in both idioms
+    assert out.rio.crs is not None
+    assert out.rio.crs.to_epsg() == 4326
+    assert "spatial_ref" in out.coords or "spatial_ref" in out.variables
+    assert out["oa01_radiance"].attrs["grid_mapping"] == "spatial_ref"
+
+    # dtype and CF scaling preserved; fill recorded
+    band = out["oa01_radiance"]
+    assert band.dtype == np.dtype("uint16")
+    assert band.attrs["scale_factor"] == 0.0139
+    assert int(band.attrs["_FillValue"]) == FILL
+
+    # Off-swath cells (bbox corners of a rotated swath) are fill
+    vals = band.values
+    assert vals[0, 0] == FILL
+    assert vals[-1, -1] == FILL
+    # …and real data survived the warp
+    valid = vals[vals != FILL]
+    assert valid.size > 0
+    assert valid.max() <= (100.0 * 64 + 60)
+
+    # altitude warped onto the grid; per-scan-line time_stamp dropped
+    assert "altitude" in out.data_vars
+    assert out["altitude"].dims == ("y", "x")
+    assert "time_stamp" not in out.variables
+
+    # float variable with no pre-existing _FillValue: nodata is NaN, but it
+    # is still recorded, and off-swath corners are NaN.
+    flux = out["solar_flux_proxy"]
+    assert np.isnan(flux.attrs["_FillValue"])
+    flux_vals = flux.values
+    assert np.isnan(flux_vals[0, 0])
+    assert np.isnan(flux_vals[-1, -1])
+
+
+def test_reproject_olci_missing_geolocation_raises() -> None:
+    ds = build_rotated_swath().drop_vars("latitude")
+    with pytest.raises(ValueError, match="latitude"):
+        reproject_olci(ds)
+
+
+def test_reproject_olci_wrong_geolocation_dims_raises() -> None:
+    ds = build_rotated_swath()
+    ds = ds.assign_coords(latitude=("rows", np.linspace(45, 46, 64)))
+    with pytest.raises(ValueError, match="2-D"):
+        reproject_olci(ds)
+
+
+def test_reproject_olci_degenerate_extent_raises() -> None:
+    ds = build_rotated_swath()
+    ds = ds.assign_coords(
+        latitude=(("rows", "columns"), np.full((64, 60), 45.0)),
+        longitude=(("rows", "columns"), np.full((64, 60), 10.0)),
+    )
+    with pytest.raises(ValueError, match="degenerate"):
+        reproject_olci(ds)
+
+
+def test_reproject_olci_projected_target_crs() -> None:
+    """Warping to a projected target CRS yields a metric grid with matching coord attrs.
+
+    Exercises the non-geographic branch end-to-end: the CLI-advertised
+    --target-crs flag must produce a regular grid in the requested CRS, with
+    projection_x/y_coordinate standard names and metre units on the 1-D coords.
+    """
+    ds = build_rotated_swath()
+    out = reproject_olci(ds, target_crs="EPSG:3857")
+
+    assert out.rio.crs is not None
+    assert out.rio.crs.to_epsg() == 3857
+    for dim, std_name in (
+        ("y", "projection_y_coordinate"),
+        ("x", "projection_x_coordinate"),
+    ):
+        coord = out[dim]
+        assert coord.ndim == 1
+        steps = np.diff(coord.values)
+        assert np.allclose(steps, steps[0])
+        assert coord.attrs["standard_name"] == std_name
+        assert coord.attrs["units"] == "m"
+    band = out["oa01_radiance"]
+    assert band.dtype == np.dtype("uint16")
+    assert band.attrs["grid_mapping"] == "spatial_ref"
diff --git a/tests/test_open_source.py b/tests/test_open_source.py
new file mode 100644
index 00000000..66a674c2
--- /dev/null
+++ b/tests/test_open_source.py
@@ -0,0 +1,162 @@
+"""Tests for open_source_datatree: native-chunk-aligned reads and the
+per-source cache (EOPF-Explorer/data-pipeline#339)."""
+
+import os
+import threading
+from collections.abc import Iterator
+from http.server import SimpleHTTPRequestHandler, ThreadingHTTPServer
+
+import numcodecs
+import numpy as np
+import pytest
+import zarr
+
+from eopf_geozarr.conversion.open_source import open_source_datatree
+
+
+def _build_source_store(store_path: str, seed: int = 42) -> str:
+    """Write a zarr v2 store mimicking a cpm_v270 source group.
+
+    - ``aot``: ONE whole-array chunk (the pathological v270 layout)
+    - ``b02``: regular 32x32 tiles
+
+    ``seed`` varies the values so two stores can share a key layout but differ
+    in content.
+    """
+    group = zarr.open_group(store_path, mode="w", zarr_format=2)
+    rng = np.random.default_rng(seed)
+
+    aot = group.create_array(
+        "aot",
+        shape=(128, 128),
+        chunks=(128, 128),
+        dtype="f8",
+        compressors=numcodecs.Blosc(cname="lz4", clevel=5, shuffle=1),
+    )
+    aot[:] = rng.random((128, 128))
+    aot.attrs["_ARRAY_DIMENSIONS"] = ["y", "x"]
+
+    b02 = group.create_array(
+        "b02",
+        shape=(128, 128),
+        chunks=(32, 32),
+        dtype="f8",
+        compressors=numcodecs.Blosc(cname="lz4", clevel=5, shuffle=1),
+    )
+    b02[:] = rng.random((128, 128))
+    b02.attrs["_ARRAY_DIMENSIONS"] = ["y", "x"]
+
+    zarr.consolidate_metadata(group.store)
+    return store_path
+
+
+@pytest.fixture
+def source_store(tmp_path) -> str:
+    return _build_source_store(str(tmp_path / "source.zarr"))
+
+
+def test_single_chunk_array_is_one_dask_task(source_store) -> None:
+    dt = open_source_datatree(source_store)
+    assert dt["aot"].data.chunks == ((128,), (128,))
+
+
+def test_tiled_array_chunks_match_native_grid(source_store) -> None:
+    dt = open_source_datatree(source_store)
+    assert dt["aot"].data.chunks == ((128,), (128,))
+    assert dt["b02"].data.chunks == ((32,) * 4, (32,) * 4)
+
+
+def test_values_roundtrip(source_store) -> None:
+    dt = open_source_datatree(source_store)
+    aot_expected = np.asarray(zarr.open_array(source_store, mode="r", path="aot")[:])
+    np.testing.assert_array_equal(dt["aot"].values, aot_expected)
+
+
+def test_explicit_storage_options_are_forwarded_to_xarray(monkeypatch) -> None:
+    captured: dict[str, object] = {}
+
+    def fake_open_datatree(path: object, **kwargs: object) -> None:
+        captured.update(kwargs)
+        raise InterruptedError("stop before any I/O")
+
+    monkeypatch.setattr("xarray.open_datatree", fake_open_datatree)
+    sentinel = {"endpoint_url": "https://example.invalid"}
+    with pytest.raises(InterruptedError):
+        open_source_datatree("/some/store.zarr", storage_options=sentinel)
+    assert captured["storage_options"] == sentinel
+    assert captured["chunks"] == {}
+
+
+def test_mask_and_scale_is_forwarded_to_xarray(monkeypatch) -> None:
+    captured: dict[str, object] = {}
+
+    def fake_open_datatree(path: object, **kwargs: object) -> None:
+        captured.update(kwargs)
+        raise InterruptedError("stop before any I/O")
+
+    monkeypatch.setattr("xarray.open_datatree", fake_open_datatree)
+    with pytest.raises(InterruptedError):
+        open_source_datatree("/some/store.zarr", storage_options={})
+    assert captured["mask_and_scale"] is True
+
+    with pytest.raises(InterruptedError):
+        open_source_datatree("/some/store.zarr", storage_options={}, mask_and_scale=False)
+    assert captured["mask_and_scale"] is False
+
+
+@pytest.fixture
+def http_source(source_store) -> Iterator[str]:
+    """Serve the source store over local HTTP (mimics the EODC https source)."""
+    root = os.path.dirname(source_store)
+    name = os.path.basename(source_store)
+
+    class Handler(SimpleHTTPRequestHandler):
+        def __init__(self, *args: object, **kwargs: object) -> None:
+            super().__init__(*args, directory=root, **kwargs)  # type: ignore[arg-type]
+
+        def log_message(self, format: str, *args: object) -> None:
+            pass
+
+    server = ThreadingHTTPServer(("127.0.0.1", 0), Handler)
+    threading.Thread(target=server.serve_forever, daemon=True).start()
+    yield f"http://127.0.0.1:{server.server_address[1]}/{name}"
+    server.shutdown()
+
+
+def test_cache_dir_reads_correct_data_over_http(http_source, source_store, tmp_path) -> None:
+    """cache_dir reads return correct data and populate the cache, with no
+    leftover temp files. Atomicity of the cache writes themselves is zarr
+    LocalStore's contract (temp file + rename)."""
+    cache_dir = str(tmp_path / "source-cache")
+    dt = open_source_datatree(http_source, cache_dir=cache_dir)
+
+    aot_expected = np.asarray(zarr.open_array(source_store, mode="r", path="aot")[:])
+    np.testing.assert_array_equal(dt["aot"].values, aot_expected)
+    assert dt["aot"].data.chunks == ((128,), (128,))  # native alignment preserved
+
+    cached_files = [f for _, _, fs in os.walk(cache_dir) for f in fs]
+    assert cached_files, "cache directory should be populated after reads"
+    assert not [f for f in cached_files if f.endswith(".partial")]
+
+
+def test_two_sources_can_share_one_cache_dir(tmp_path) -> None:
+    """A shared cache_dir must never let one source serve another's bytes.
+
+    CacheStore keys entries by zarr key ("aot/0.0" is identical for every S2
+    product), so open_source_datatree namespaces the cache per source URL.
+    """
+    first = _build_source_store(str(tmp_path / "first.zarr"), seed=1)
+    second = _build_source_store(str(tmp_path / "second.zarr"), seed=2)
+    shared_cache = str(tmp_path / "shared-cache")
+
+    dt_first = open_source_datatree(first, cache_dir=shared_cache)
+    np.testing.assert_array_equal(
+        dt_first["aot"].values,
+        np.asarray(zarr.open_array(first, mode="r", path="aot")[:]),
+    )
+
+    dt_second = open_source_datatree(second, cache_dir=shared_cache)
+    np.testing.assert_array_equal(
+        dt_second["aot"].values,
+        np.asarray(zarr.open_array(second, mode="r", path="aot")[:]),
+    )
diff --git a/uv.lock b/uv.lock
index 88d9e183..6a1eaacc 100644
--- a/uv.lock
+++ b/uv.lock
@@ -18,9 +18,10 @@ constraints = [
     { name = "black", specifier = ">=26.3.1" },
     { name = "cryptography", specifier = ">=46.0.6" },
     { name = "filelock", specifier = ">=3.20.3" },
+    { name = "msgpack", specifier = ">=1.2.1" },
     { name = "pygments", specifier = ">=2.20.0" },
     { name = "requests", specifier = ">=2.33.0" },
-    { name = "tornado", specifier = ">=6.5.5" },
+    { name = "tornado", specifier = ">=6.5.7" },
     { name = "urllib3", specifier = ">=2.7.0" },
     { name = "virtualenv", specifier = ">=20.36.1" },
 ]
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 ]
 
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