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Crop Classification of Belle-Île Island using Sentinel-1 and Sentinel-2 Data

In this project, I used vegetation indices derived from both Sentinel-1 and Sentinel-2 imagery for the crop classification of Belle-Île Island, France. This end-to-end project demonstrates the complete workflow, including data acquisition, preprocessing, feature engineering, and modeling.

Key Features and Tools

  • Data Acquisition and Processing: Leveraged the Google Earth Engine (GEE) Python API for querying and processing satellite imagery.
  • Feature Engineering:
    • Sentinel-2 Vegetation Indices: NDVI, GNDVI, NBR, and EVI.
    • Sentinel-1 Indices: RVI, DPSVI, and DPSVIo.
  • Modeling: Implemented crop classification using Random Forest and XGBoost machine learning algorithms.
  • Visualization: Utilized geemap for interactive visualizations.