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Update documentation for Sentinel-2 optimized conversion (#83)
* add option for using new multiscales convention in optimized conversion * remove OOP converter and use plain functions * use pydantic models for fingerprinting sentinel2 product * fix multiscales to use da.coarsen and propagate encoding * ensure that dtype is preserved after resampling * add new multiscales JSON example * add mypy pydantic plugin * lint * add s1 and s2 demo data to tests, and don't test against remote urls * fix e2e tests * remove network test workflow from CI * remove extra type definition and update tests * remove explicit zarr groups in favor of dynamic test fixtures * docstrings * Enhance CRS initialization and update S2 optimization commands - Added `initialize_crs_from_dataset` function to extract CRS from dataset metadata. - Updated S2 optimization commands to include new CRS handling. - Removed unused arguments related to geometry and meteorology groups. - Added comprehensive tests for CRS initialization from various sources. * Refactor code formatting for clarity in S2 optimization functions * fix failing / warning tests * add strict JSON schema equality check to e2e tests * support both flavors of multiscale metadata * dont manage return codes in cli functions * add s2 optimized test * add optimized geozarr exmaple hierarchies * format JSON documents * mid-debug of e2e tests * WIP e2e fixes * make cf standard name validator become a pass-through when no internet connection * update example schemas * narrow type to just tuples in types.py * refactor consolidation * use consolidated=False in conversion * update tests * lint * add both multiscales types to output * update comments in tests * add Sentinel-2 optimization functions and update documentation * update documentation for Sentinel-2 optimized conversion, detailing V1 approach and differences from V0 * update Sentinel-2 band analysis examples to include V1 approach and deprecate V0 structure * update quickstart guide to reflect V1 converter structure and usage for Sentinel-2 data * update documentation for multiscale dataset creation and resolution levels in GeoZarr converter --------- Co-authored-by: Davis Vann Bennett <[email protected]> Co-authored-by: Loïc Houpert <[email protected]>
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‎.vscode/launch.json‎

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"args": [
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"convert-s2-optimized",
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// "https://objects.eodc.eu/e05ab01a9d56408d82ac32d69a5aae2a:202509-s02msil2a/08/products/cpm_v256/S2A_MSIL2A_20250908T100041_N0511_R122_T32TQM_20250908T115116.zarr",
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"https://objects.eodc.eu/e05ab01a9d56408d82ac32d69a5aae2a:202511-s02msil2a-eu/15/products/cpm_v262/S2B_MSIL2A_20251115T091139_N0511_R050_T35SLU_20251115T111807.zarr",
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// "https://objects.eodc.eu/e05ab01a9d56408d82ac32d69a5aae2a:202511-s02msil2a-eu/15/products/cpm_v262/S2B_MSIL2A_20251115T091139_N0511_R050_T35SLU_20251115T111807.zarr",
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"https://objects.eodc.eu/e05ab01a9d56408d82ac32d69a5aae2a:202511-s02msil2a-eu/16/products/cpm_v262/S2A_MSIL2A_20251116T085431_N0511_R107_T35SQD_20251116T103813.zarr",
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// "s3://esa-zarr-sentinel-explorer-fra/tests-output/sentinel-2-l2a-opt/S2A_MSIL2A_20250908T100041_N0511_R122_T32TQM_20250908T115116.zarr",
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"s3://esa-zarr-sentinel-explorer-fra/tests-output/sentinel-2-l2a-pr75/S2B_MSIL2A_20251115T091139_N0511_R050_T35SLU_20251115T111807.zarr",
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// "s3://esa-zarr-sentinel-explorer-fra/tests-output/sentinel-2-l2a-pr75/S2B_MSIL2A_20251115T091139_N0511_R050_T35SLU_20251115T111807.zarr",
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"s3://esa-zarr-sentinel-explorer-fra/tests-output/sentinel-2-l2a-opt/S2A_MSIL2A_20251116T085431_N0511_R107_T35SQD_20251116T103813.zarr",
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// "./tests-output/eopf_geozarr/s2l2_optimized.zarr",
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"--spatial-chunk", "512",
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"--compression-level", "5",

‎docs/api-reference.md‎

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)
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```
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## Sentinel-2 Optimization Functions
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### convert_s2_optimized
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Main function for optimized Sentinel-2 conversion with multiscale pyramid generation.
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```python
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# test: skip
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def convert_s2_optimized(
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dt_input: xr.DataTree,
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output_path: str,
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enable_sharding: bool = True,
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spatial_chunk: int = 256,
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compression_level: int = 3,
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validate_output: bool = True,
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max_retries: int = 3
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) -> xr.DataTree
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```
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**Parameters:**
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- `dt_input` (xr.DataTree): Input Sentinel-2 DataTree
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- `output_path` (str): Output path for optimized dataset
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- `enable_sharding` (bool, optional): Enable Zarr v3 sharding. Default: True
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- `spatial_chunk` (int, optional): Spatial chunk size. Default: 256
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- `compression_level` (int, optional): Compression level 1-9. Default: 3
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- `validate_output` (bool, optional): Validate output after conversion. Default: True
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- `max_retries` (int, optional): Maximum retry attempts for operations. Default: 3
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**Returns:**
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- `xr.DataTree`: Optimized DataTree with multiscale pyramid
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**Example:**
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```python
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# test: skip
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from eopf_geozarr.s2_optimization.s2_converter import convert_s2_optimized
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import xarray as xr
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dt = xr.open_datatree("s2_product.zarr", engine="zarr")
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dt_optimized = convert_s2_optimized(
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dt_input=dt,
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output_path="s2_optimized.zarr",
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enable_sharding=True,
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spatial_chunk=256
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)
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```
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### create_multiscale_from_datatree
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Creates multiscale pyramid from DataTree, reusing native resolution groups.
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```python
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# test: skip
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def create_multiscale_from_datatree(
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dt_input: xr.DataTree,
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output_path: str,
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enable_sharding: bool,
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spatial_chunk: int,
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crs: CRS | None = None
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) -> dict[str, dict]
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```
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**Parameters:**
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- `dt_input` (xr.DataTree): Input DataTree containing native resolution groups (e.g., r10m, r20m, r60m)
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- `output_path` (str): Output path for the multiscale dataset
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- `enable_sharding` (bool): Enable Zarr v3 sharding for improved performance
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- `spatial_chunk` (int): Spatial chunk size for arrays
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- `crs` (CRS | None, optional): Coordinate reference system. If None, CRS is extracted from input
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**Returns:**
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- `dict[str, dict]`: Nested dictionary structure organizing the multiscale levels:
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```python
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{
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"measurements": {
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"reflectance": {
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"r10m": Dataset, # Native 10m resolution
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"r20m": Dataset, # Native 20m resolution
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"r60m": Dataset, # Native 60m resolution
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"r120m": Dataset, # Computed 120m overview
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"r360m": Dataset, # Computed 360m overview
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"r720m": Dataset # Computed 720m overview
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}
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}
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}
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```
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**Example:**
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```python
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# test: skip
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from eopf_geozarr.s2_optimization.s2_multiscale import create_multiscale_from_datatree
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from pyproj import CRS
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import xarray as xr
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# Load Sentinel-2 DataTree with native resolutions
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dt = xr.open_datatree("s2_input.zarr", engine="zarr")
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# Create multiscale pyramid
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multiscale_dict = create_multiscale_from_datatree(
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dt_input=dt,
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output_path="s2_multiscale.zarr",
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enable_sharding=True,
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spatial_chunk=256,
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crs=CRS.from_epsg(32633) # UTM Zone 33N
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)
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# Access specific resolution level
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r360m_reflectance = multiscale_dict["measurements"]["reflectance"]["r360m"]
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```
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**Note:** The S2 optimization uses xarray's built-in `.coarsen()` method for efficient downsampling operations, providing better integration with lazy evaluation and memory management.
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## Conversion Functions
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### setup_datatree_metadata_geozarr_spec_compliant

‎docs/architecture.md‎

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**Downsampling:**
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The library uses xarray's built-in `.coarsen()` method for efficient downsampling operations, providing better integration with lazy evaluation and memory management.
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**Sentinel-2 Optimization:**
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The S2 optimization module uses a functional programming approach with stateless functions for improved testability and maintainability:
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```python
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# test: skip
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def downsample_2d_array(
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data: np.ndarray,
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factor: int = 2
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) -> np.ndarray
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def convert_s2_optimized(
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dt_input: xr.DataTree,
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output_path: str,
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**kwargs
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) -> xr.DataTree
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```
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### 4. Command Line Interface (`cli.py`)
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- Real dataset processing
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- Cloud environment testing
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### 3. Validation Tests
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### 3. Local Test Data
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The library uses an efficient testing approach with **lightweight JSON-based Zarr groups** that contain only the structure and metadata (no chunked array data). This provides:
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- **Faster Test Execution**: Tests run locally without downloading large datasets
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- **No Remote Dependencies**: Eliminates need for network access during testing
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- **Lightweight Fixtures**: JSON files define Zarr group structure using `pydantic-zarr`
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Test fixtures are created from JSON schemas stored in `tests/test_data_api/{s1_examples,s2_examples}/` directories, making the test suite both comprehensive and efficient.
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### 4. Validation Tests
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- GeoZarr specification compliance
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- Metadata accuracy verification

‎docs/converter.md‎

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The converter maintains the native coordinate reference system (CRS) of the dataset, avoiding reprojection to Web Mercator.
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## Sentinel-2 Optimized Conversion (V1)
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The Sentinel-2 optimized converter (`convert_s2_optimized`) represents the refined approach (V1) that creates an efficient multiscale pyramid by **reusing the original multi-resolution data** (r10m, r20m, r60m) without duplication, and adding coarser overview levels (r120m, r360m, r720m) for efficient visualization at lower resolutions.
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### V0 vs V1 Converter: Key Differences
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Understanding the structural differences between the old (V0) and new (V1) converter approaches:
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#### V0 Approach (Deprecated - `create_geozarr_dataset`)
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Creates **pyramids within each resolution group**:
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```
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output.zarr/
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└── measurements/
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└── reflectance/
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├── r10m/
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│ ├── 0/ # Native 10m data
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│ ├── 1/ # Downsampled to 20m
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│ ├── 2/ # Downsampled to 40m
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│ ├── 3/ # Downsampled to 80m
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│ ├── 4/ # Downsampled to 160m
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│ └── 5/ # Downsampled to 320m
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├── r20m/
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│ ├── 0/ # Native 20m data
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│ ├── 1/ # Downsampled to 40m
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│ ├── 2/ # Downsampled to 80m
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│ ├── 3/ # Downsampled to 160m
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│ └── 4/ # Downsampled to 320m
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└── r60m/
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├── 0/ # Native 60m data
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├── 1/ # Downsampled to 120m
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└── 2/ # Downsampled to 240m
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```
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**Issues with V0:**
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- Creates redundant data at overlapping resolutions (e.g., r10m/1 ≈ r20m/0)
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- Inefficient storage due to duplication
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- Complex hierarchy with nested levels within each resolution group
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#### V1 Approach (Current - `convert_s2_optimized`)
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Creates a **consolidated pyramid** by reusing native resolutions and adding coarser levels:
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```
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output.zarr/
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└── measurements/
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└── reflectance/
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├── r10m/ # Native 10m data (reused as-is)
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├── r20m/ # Native 20m data (reused as-is)
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├── r60m/ # Native 60m data (reused as-is)
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├── r120m/ # Computed from r60m (2x downsampling)
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├── r360m/ # Computed from r120m (3x downsampling)
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└── r720m/ # Computed from r360m (2x downsampling)
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```
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**Why these specific resolution levels?**
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The resolution levels are chosen to balance data preservation with storage optimization:
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- **Native ESA resolutions (10m, 20m, 60m)**: These are the original resolutions delivered by ESA for Sentinel-2 data and are reused as-is to preserve the source data without any loss
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- **Computed overview levels (120m, 360m, 720m)**: These additional levels were specifically chosen because their downsampling factors allow the data to be chunked and sharded in complete pieces, ensuring:
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- **120m** (2x from 60m): Standard doubling for the first computed overview
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- **360m** (3x from 120m): Selected for optimal chunking alignment
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- **720m** (2x from 360m): Final level for global-scale visualization
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This approach maintains the integrity of ESA's original multi-resolution data while adding computationally efficient overview levels for performance at coarser scales.
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**Benefits of V1:**
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- No data duplication - native resolutions are reused directly
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- More efficient storage
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- Simpler, flatter hierarchy
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- Natural fit for Sentinel-2's multi-resolution data model
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### Key Capabilities
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- **Smart Resolution Consolidation**: Combines Sentinel-2's native multi-resolution structure (10m, 20m, 60m) into a unified multiscale pyramid
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- **Non-Duplicative Downsampling**: Reuses original resolution data instead of recreating it, adding only the coarser levels (120m, 360m, 720m)
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- **Variable-Aware Processing**: Applies appropriate resampling methods for different data types (reflectance, classification, quality masks, probabilities)
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- **Efficient Testing**: Improved test infrastructure for faster local development
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> **Note:** The V0 converter (`create_geozarr_dataset`) is deprecated and will be removed in future versions. All new projects should use the V1 converter (`convert_s2_optimized`).
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### Usage Example
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```python
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from eopf_geozarr.s2_optimization.s2_converter import convert_s2_optimized
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import xarray as xr
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# Load Sentinel-2 DataTree
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dt_input = xr.open_datatree("path/to/s2/product.zarr", engine="zarr")
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# Convert to optimized multiscale structure
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dt_optimized = convert_s2_optimized(
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dt_input=dt_input,
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output_path="path/to/output/optimized.zarr",
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enable_sharding=True,
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spatial_chunk=256,
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compression_level=3,
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validate_output=True
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)
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```
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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.
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## Error Handling
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The converter includes robust error handling and retry logic for network operations, ensuring reliable processing even in challenging environments.

‎docs/examples.md‎

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### Sentinel-2 Band Analysis
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Access and analyze specific bands from the converted dataset:
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> **Note on V0 vs V1:** This example shows both V0 (deprecated) and V1 (current) approaches. See [converter documentation](converter.md#v0-vs-v1-converter-key-differences) for structural differences.
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#### V1 Approach (Recommended - `convert_s2_optimized`)
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Access bands from the consolidated pyramid structure:
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```python
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import xarray as xr
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import matplotlib.pyplot as plt
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from eopf_geozarr.s2_optimization.s2_converter import convert_s2_optimized
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# Convert using V1 optimizer (recommended)
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dt_input = xr.open_datatree("s2_l2a_input.zarr", engine="zarr")
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dt = convert_s2_optimized(
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dt_input=dt_input,
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output_path="s2_l2a_v1.zarr",
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spatial_chunk=256
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)
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# Open converted GeoZarr dataset
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dt = xr.open_datatree("s2_l2a_geozarr.zarr", engine="zarr")
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# Access 10m resolution native data
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ds_10m = dt["/measurements/r10m/0"].ds
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# Access data from different resolution levels
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ds_10m = dt["/measurements/reflectance/r10m"].ds # Native 10m
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ds_20m = dt["/measurements/reflectance/r20m"].ds # Native 20m
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ds_60m = dt["/measurements/reflectance/r60m"].ds # Native 60m
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ds_120m = dt["/measurements/reflectance/r120m"].ds # Computed 120m
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# Extract RGB bands for visualization
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# Extract RGB bands for visualization (10m resolution)
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red = ds_10m["b04"] # Red band
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green = ds_10m["b03"] # Green band
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blue = ds_10m["b02"] # Blue band
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# Create RGB composite
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rgb = xr.concat([red, green, blue], dim="band")
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# Plot the result
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
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# Plot comparison of different resolutions
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fig, axes = plt.subplots(1, 3, figsize=(18, 6))
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rgb.plot.imshow(ax=ax1, robust=True)
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ax1.set_title("Native Resolution (10m)")
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# 10m resolution
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rgb.plot.imshow(ax=axes[0], robust=True)
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axes[0].set_title("10m Resolution (Native)")
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# Overview level 1
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ds_overview = dt["/measurements/r10m/1"].ds
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rgb_overview = xr.concat([ds_overview["b04"], ds_overview["b03"], ds_overview["b02"]], dim="band")
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rgb_overview.plot.imshow(ax=ax2, robust=True)
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ax2.set_title("Overview Level 1 (20m)")
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# 20m resolution (reused native data)
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rgb_20m = xr.concat([ds_20m["b04"], ds_20m["b03"], ds_20m["b02"]], dim="band")
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rgb_20m.plot.imshow(ax=axes[1], robust=True)
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axes[1].set_title("20m Resolution (Native)")
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# 60m resolution (reused native data)
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rgb_60m = xr.concat([ds_60m["b04"], ds_60m["b03"], ds_60m["b02"]], dim="band")
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rgb_60m.plot.imshow(ax=axes[2], robust=True)
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axes[2].set_title("60m Resolution (Native)")
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plt.tight_layout()
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plt.show()
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```
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#### V0 Approach (Deprecated - `create_geozarr_dataset`)
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For reference, the V0 structure with nested pyramid levels:
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```python
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import xarray as xr
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import matplotlib.pyplot as plt
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# Open V0 converted GeoZarr dataset (deprecated structure)
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dt = xr.open_datatree("s2_l2a_v0.zarr", engine="zarr")
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# Access 10m resolution with nested pyramid levels
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ds_10m_native = dt["/measurements/r10m/0"].ds # Level 0: native 10m
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ds_10m_level1 = dt["/measurements/r10m/1"].ds # Level 1: downsampled to ~20m
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ds_10m_level2 = dt["/measurements/r10m/2"].ds # Level 2: downsampled to ~40m
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# Note: This creates redundant data since r10m/1 ≈ r20m/0
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```
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> **Migration Note:** V0 is deprecated. Use V1 (`convert_s2_optimized`) for new projects.
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## Cloud Storage Examples
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### AWS S3 Integration

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