Amara-Adam/crop-health-monitoring

# Crop Health Monitoring (Sentinel-2) Tools to turn Sentinel-2 L2A imagery into vegetation index maps (NDVI, NDRE, SAVI) for precision agriculture. Designed for small-to-medium fields with simple, reproducible steps on Windows, macOS, or Linux. ## ✨ What it does - Downloads or ingests Sentinel-2 **Level-2A** products (surface reflectance). - Resamples/aligns key bands to **10 m**. - Stacks Red, Red-edge, NIR bands into a single raster. - Computes **NDVI**, **NDRE**, **SAVI** and exports GeoTIFFs. - (Optional) Clips outputs to your **AOI** (GeoJSON/Shape) and produces basic stats. ## 🧠 Background (short) - Sentinel-2 MSI has 13 spectral bands (10–60 m). For crop vigor & early stress: - **B04 (Red, 10 m)**, **B08 (NIR, 10 m)** → NDVI - **B05 (Red-edge, 20 m)**, **B8A (Narrow NIR, 20 m)** → NDRE - We resample B05 and B8A to **10 m** so all math is pixel-aligned. _(For a concise domain overview used to shape this pipeline, see the attached internship report.)_ :contentReference[oaicite:0]{index=0} ## 📂 Project structure ├─ data/ │ ├─ S2/ │ │ └─ L2A/ / │ └─ indices/ │ ├─ R10m_tif/ # resampled 10 m bands live here │ └─ outputs/ # final indices + clipped rasters ├─ aoi/ │ └─ aoi.geojson ├─ scripts/ │ ├─ compute_indices.py │ └─ utils.py ├─ environment.yml └─ README.md ## 🛠️ Requirements - Python 3.10+ (Conda recommended) - GDAL, Rasterio, Numpy, GeoPandas, Shapely - (Optional) QGIS for visualization Create the environment: ```bash conda env create -f environment.yml conda activate s2_agro