Metadata-Version: 2.5
Name: modis-majortom
Version: 0.1.0
Summary: MODIS satellite data collection, cloud masking, and MajorTom grid utilities
Author: Riccardo D'Ercole
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License-File: LICENSE.txt
Requires-Python: <3.14,>=3.11
Requires-Dist: dask
Requires-Dist: dotenv>=0.9.9
Requires-Dist: earthaccess>=0.15.1
Requires-Dist: earthengine-api>=1.6.10
Requires-Dist: eumetsearch[transform]>=0.1.3
Requires-Dist: geemap>=0.36.4
Requires-Dist: geopandas>=1.1.1
Requires-Dist: h5py>=3.12.1
Requires-Dist: huggingface-hub>=0.23.0
Requires-Dist: joblib>=1.5.3
Requires-Dist: matplotlib>=3.10.6
Requires-Dist: minio>=7.2.20
Requires-Dist: numpy>=2.3.3
Requires-Dist: pandas
Requires-Dist: pyhdf>=0.11.6
Requires-Dist: pyproj
Requires-Dist: pystac-client>=0.9.0
Requires-Dist: rasterio>=1.4.3
Requires-Dist: rioxarray>=0.19.0
Requires-Dist: satpy>=0.58.0
Requires-Dist: scipy
Requires-Dist: tqdm>=4.67.1
Requires-Dist: xarray>=2025.8.0
Requires-Dist: zarr>=3.1.3
Provides-Extra: viz
Requires-Dist: cartopy>=0.25.0; extra == 'viz'
Description-Content-Type: text/markdown

# modis-majortom

A Python package for downloading, processing, and archiving MODIS satellite data with cloud masking capabilities and MajorTom grid integration. Handles data acquisition from NASA Earthdata, applies advanced cloud detection algorithms, performs reprojection, and exports data in Zarr or GeoTIFF format.

This README covers the data acquisition/processing package. For the NDVI latent diffusion model
built on top of this data (architecture, model version lineage, and how to reproduce a training
run), see the sibling [`ndvi-diffusion`](../ndvi-diffusion) repository, which consumes this
package as a dependency (`docs/DIFFUSION_MODEL.md` there has the full write-up).

## Features

- Download MODIS/VIIRS products via NASA Earthdata (`earthaccess`)
- Mosaic multi-tile HDF files using GDAL VRT pipelines
- Reproject to EPSG:6933, UTM, or any target CRS
- Product-specific DN → physical units conversions (LST, NDVI, reflectance, night lights)
- Multi-sensor cloud masking (MOD09 QA + MOD35 + spectral veto + morphological processing)
- Optional ML-based cloud confidence scoring using a DINOv2-backed adapter model
- Export to chunked Zarr stores or cloud-optimized GeoTIFFs
- Optional cloud storage via MinIO/S3
- Google Earth Engine integration for land fraction filtering and auxiliary products
- Incremental processing: skips already-processed dates per tile, safe to resume interrupted runs
- Global tiled downloads: divide the globe into n×m tiles and append all patches into one shared Zarr

## Supported Products

| Name | Product ID | Resolution | Description |
|---|---|---|---|
| `reflectance_250m` | MOD09GQ | 250 m | Surface reflectance (bands 1–2) |
| `reflectance_500m` | MOD09GA | 500 m | Surface reflectance + state QA flags |
| `LST` | MOD11A1 | 1 km | Land Surface Temperature |
| `NDVI_1km_monthly` | MOD13A3 | 1 km | Monthly NDVI/EVI |
| `VIIRS_500m_night_daily` | VNP46A2 | 500 m | Daily night lights |
| `VIIRS_500m_night_monthly` | VNP46A3 | 500 m | Monthly night lights |
| `modis_cloud_mask` | MOD35_L2 | 1 km | Cloud detection confidence |

## Installation

Requires Python >= 3.11. Install with [uv](https://github.com/astral-sh/uv):

```bash
uv sync
```

Or with pip:

```bash
pip install -e .
```

The `majortom` dependency is pulled directly from ESA PhiLab's GitHub repository:

```toml
majortom = { git = "https://github.com/ESA-PhiLab/Major-TOM.git" }
```

## Authentication

**NASA Earthdata** — required for downloading MODIS data. Set credentials as environment variables or in a `.env` file:

```
EARTHDATA_USERNAME=your_username
EARTHDATA_PASSWORD=your_password
```

Alternatively, a bearer token can be used (generate one at [urs.earthdata.nasa.gov](https://urs.earthdata.nasa.gov) → My Profile → Generate Token):

```
EARTHDATA_TOKEN=eyJ...
```

Note: tokens expire after ~60 days. Username + password authentication handles renewal automatically and is preferred for long-running pipelines.

**Google Earth Engine** — required for land fraction filtering. Authenticate once via:

```bash
earthengine authenticate
```

and set project-ID as environment variables or in a `.env` file:

```
EE_PROJECT=your_gee_project_id
```

**MinIO/S3** (optional) — required only when using `--store_cloud`:

```
MINIO_ENDPOINT=your_endpoint
MINIO_ACCESS_KEY=your_key
MINIO_SECRET_KEY=your_secret
MINIO_BUCKET=your_bucket
```

## Usage

### CLI

```bash
python -m modis_majortom.eo_data.pipeline_data \
  --product reflectance_250m \
  --start_date 2024-01-01 \
  --end_date 2024-03-31 \
  --lon_min -10 --lon_max 40 \
  --lat_min -35 --lat_max 38 \
  --n_lon 5 --n_lat 5 \
  --output_format zarr \
  --reproj_lib rioxarray \
  --reproj_method bilinear
```

**Global download to a single MajorTOM Zarr:**

```bash
python -m modis_majortom.eo_data.pipeline_data \
  --product reflectance_250m \
  --start_date 2025-06-01 \
  --end_date 2025-06-02 \
  --lon_min -180 --lon_max 180 \
  --lat_min -80  --lat_max 80 \
  --n_lon 12 --n_lat 8 \
  --batch_days 2 \
  --majortom_grid \
  -d
```

This splits the globe into 96 tiles (8×12), filters out ocean-only tiles via GEE (≥30% land), and streams all patches from every land tile into a single shared `data/modis/MOD09GQ_dataset.zarr`. Each tile tracks its own processed-date index (`*.index.json`) so the run is safe to interrupt and resume.

#### Key Arguments

| Argument | Default | Description |
|---|---|---|
| `--product` | `reflectance_250m` | MODIS product to download |
| `--start_date` | — | Start date (`YYYY-MM-DD`) |
| `--end_date` | — | End date (`YYYY-MM-DD`) |
| `--lon_min/max` | — | Longitude bounds |
| `--lat_min/max` | — | Latitude bounds |
| `--n_lon` / `--n_lat` | — | Number of tiles along each axis |
| `--batch_days` | `30` | Days per download batch |
| `--output_format` | `zarr` | Output format: `zarr` or `tiff` |
| `--reproj_lib` | `rioxarray` | Reprojection library: `rioxarray` or `xesmf` |
| `--reproj_method` | `nearest` | Interpolation: `nearest` or `bilinear` |
| `--majortom_grid` | `False` | Align output to MajorTom grid spec |
| `--store_cloud` | `False` | Upload output to MinIO/S3 |
| `--add_variables` | `False` | Add variables to existing Zarr without re-downloading |
| `--variables_override` | — | Comma-separated list of specific bands |
| `-d` / `--delete_temp` | `False` | Delete raw HDF files after each tile to save disk |

### Python API

```python
from modis_majortom.eo_data.modis import EarthAccessDownloader

downloader = EarthAccessDownloader(
    short_name="MOD11A1",
    variable=["LST_Day_1km", "QC_Day"],
    bbox=[-10, -35, 40, 38],
    start_date="2024-01-01",
    end_date="2024-03-31",
    output_format="zarr",
)
downloader.run()
```

```python
from modis_majortom.transform.cloud_adapter import generate_cloud_mask

result = generate_cloud_mask(
    mod09qa_bits=state_1km_array,       # uint16 QA flags
    mod35_confidence=cloud_conf_array,  # uint8 confidence
    blue_band=band3_reflectance,        # float32
)

print(result.cleaned_mask)   # bool (H, W) — True = cloudy
print(result.soft_score)     # float32 (H, W) — clearness confidence
```

## Architecture

```
pipeline_data.py (CLI)
  ├── generate_bboxes_fixed()  — divide AOI into n×m tiles
  ├── tile_has_min_land_fraction() (GEE)  — drop ocean tiles
  │     └── result cached in data/water_min.npy
  └── for each tile → EarthAccessDownloader (modis.py)
        ├── _get_processed_dates()
        │     └── reads per-tile <zarr>.<bbox>.index.json
        │           → returns empty set if index absent (tile never run)
        │           → does NOT probe shared Zarr store (avoids false skips)
        ├── Search & Download (earthaccess)
        ├── Parse & Mosaic (GDAL)
        │     ├── Open HDF subdatasets
        │     ├── Reproject to target CRS (EPSG:6933)
        │     └── Merge via VRT + GDAL Translate
        ├── Product Preprocessing
        │     └── DN → physical units (LST, NDVI, night lights)
        ├── Cloud Masking (optional)
        │     └── generate_cloud_mask() (cloud_adapter.py)
        ├── Export to shared Zarr (MajorTOM sparse format)
        │     └── patches/<variable>/<date>/<grid_id>/data
        └── _update_zarr_index()  — persist processed dates to per-tile index
```

## Cloud Masking

`generate_cloud_mask()` fuses three sources of cloud information:

1. **MOD09 QA flags** — state bits 0–1 from `MOD09GA`
2. **MOD35 confidence** — 4-level cloud confidence from `MOD35_L2`
3. **Blue band reflectance** — spectral veto: pixels with reflectance > 0.15 flagged as cloudy

Processing steps:
1. Extract cloud flags from each source
2. Apply spectral veto for disagreeing pixels
3. Morphological dilation (5×5 kernel) to buffer cloud edges
4. Union of dilated masks + morphological closing to fill holes
5. Soft confidence scoring: weighted blend (50% MOD35 + 30% MOD09 + 20% blue penalty)

The `meta_mask_channel` output is designed as a conditioning input for cloud inpainting models.

A learning-based cloud probability model (`CloudAdapterModel`: DINOv2 ViT-S/14 backbone +
lightweight adapter + upsampling decoder) built on top of these functions lives in the sibling
[`ndvi-diffusion`](../ndvi-diffusion) repository (`ndvi_diffusion.datasets.cloud_adapter_model`).

## Output Structure

**Zarr (default)**:
```
data/modis/
├── MOD09GQ_dataset.zarr/
│   └── patches/
│       ├── sur_refl_b01/
│       │   └── <date>/
│       │       └── <grid_id>/   ← MajorTOM cell, e.g. "23D_51L"
│       │           └── data     ← array (256, 256) float32
│       └── sur_refl_b02/
│           └── ...
├── MOD09GQ_dataset.zarr.<lon_min>_<lat_min>_<lon_max>_<lat_max>.index.json
│   ← per-tile skip-list: tracks which dates each tile has already written
└── MOD09GQ_061/
    └── <bbox>/
        └── raw_data/            ← temporary HDF files (deleted with -d)
```

Each tile writes its patches directly into the shared `MOD09GQ_dataset.zarr` under the corresponding `<date>/<grid_id>` groups. Tiles do not overlap in grid space, so concurrent appends are safe.

**GeoTIFF**:
```
data/modis/
└── MOD09GQ_061/
    └── <bbox>/
        └── <date>/
            └── <grid_id>.tif
```

## Utilities

- `generate_bboxes_fixed()` — divide a region into n×m equal tiles
- `tile_has_min_land_fraction()` — filter tiles by land coverage (GEE-based); results cached in `data/water_min.npy` — delete this file when changing the AOI or tile grid
- `CalculationsMajorTom` — lat/lon ↔ MODIS sinusoidal ↔ tile (h,v) ↔ pixel conversions
- `compute_ndvi()` — NDVI from red and NIR arrays
- `minio_client()` — initialise MinIO S3 client from environment

## Known Behaviours and Gotchas

**`water_min.npy` is bbox-specific.** The land-fraction filter caches its results to `data/water_min.npy`. If you change `--lon_min/max`, `--lat_min/max`, `--n_lat`, or `--n_lon`, delete this file first so it is recomputed for the new grid.

**Per-tile processed-date indexes.** Each tile writes its skip-list to `<zarr>.<lon_min>_<lat_min>_<lon_max>_<lat_max>.index.json` alongside the Zarr. These files are what make resume-after-crash safe: a tile whose index is absent is treated as entirely unprocessed, regardless of what other tiles have written into the shared Zarr. Do not delete them mid-run.

**Shared Zarr, isolated skip-lists.** All tiles append patches into the same `MOD09GQ_dataset.zarr`. The per-tile index isolation prevents a date written by tile A from being mistakenly counted as "done" for tile B — a bug that would cause most tiles to silently skip all downloads.

## License

See [LICENSE.txt](LICENSE.txt) for details.
