Satellite Data Fusion Pipeline

A Python pipeline for downloading, processing, and fusing Sentinel-2 and Sentinel-3 satellite imagery to generate high-resolution NDVI time series.

Features

• Data Download: Downloads Sentinel-2 L2A (via AWS Earth Search) and Sentinel-3 OLCI (via OpenEO/Copernicus)
• Cloud Detection: Identifies cloud-covered images using NDVI analysis
• EFAST Fusion: Combines S2 and S3 data using the EFAST algorithm for enhanced temporal resolution
• NDVI Calculation: Generates Normalized Difference Vegetation Index from raw and fused data
• Web Visualization: Interactive web viewer for exploring NDVI time series and imagery

Installation

pip install -r requirements.txt
pip install git+https://github.com/DHI-GRAS/efast.git

Configuration

Set environment variables for Copernicus Data Space authentication:

• CDSE_USER: Copernicus Data Space username
• CDSE_PASSWORD: Copernicus Data Space password

Usage

from run import run_pipeline

run_pipeline(season=2024, site_position=(47.116171, 11.320308), site_name="innsbruck")

The pipeline processes data in stages:

1. Download S2/S3 imagery
2. Generate NDVI from raw data
3. Detect clouds
4. Prepare data for fusion
5. Run EFAST fusion
6. Generate NDVI from fused outputs

Data Structure

data/
  {site_name}/
    {season}/
      raw/
        s2/          # Sentinel-2 GeoTIFFs
        s3/          # Sentinel-3 GeoTIFFs
        ndvi/        # NDVI from raw data
      prepared/
        s2/          # Prepared S2 data
        s3/          # Prepared S3 data
        fusion/      # EFAST fusion outputs
        ndvi/        # NDVI from prepared/fused data
      clouds.json    # Cloud detection results

Web Viewer

Run a local HTTP server to view the web interface:

cd webapp
python3 -m http.server 8000

Then open http://localhost:8000/webapp in your browser to visualize NDVI time series and compare S2, S3, and fusion outputs.