🌊 Flood Mapping using Sentinel-1 SAR Data (Google Earth Engine)

This Jupyter Notebook (Flood_MappingV2.ipynb) performs flood mapping and water body detection using Sentinel-1 SAR imagery processed through Google Earth Engine (GEE).
It leverages the Normalized Difference Water Index (NDWI) to delineate flooded or water-covered areas within a user-defined Area of Interest (AOI).

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🚀 Features

• 🔍 Interactive AOI selection on an embedded map
• 📅 Custom date range filtering for Sentinel-1 data
• 🛰️ Automatic preprocessing (orbit, polarization, and speckle filtering)
• 🌊 NDWI computation and thresholding for water detection
• 🗺️ Interactive visualization using geemap and folium
• 💾 Optional export of processed layers to Google Drive

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🧰 Requirements

Make sure you have the following Python libraries installed:

pip install geemap ee folium ipywidgets matplotlib pandas

💡 You will also need an active Google Earth Engine account:
https://earthengine.google.com/signup/

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⚙️ How to Use

1. Open the Notebook

• Launch Jupyter Notebook or Google Colab.
• Upload the file: Flood_MappingV2.ipynb.

2. Install Dependencies

• Run the first cell to install all required packages.

3. Authenticate Google Earth Engine

• Follow the link shown in the output.
• Log in with your Google account.
• Copy and paste the authentication code back into the notebook.

4. Define Area of Interest (AOI)

• Use the drawing tools on the interactive map to select your study region.

5. Set Date Range

• Input your desired start date and end date for Sentinel-1 imagery.

6. Run Analysis

• Execute the remaining cells to compute NDWI and visualize the water body detection results.

7. View Results

• Water bodies will appear as highlighted regions on the interactive map.
• Optionally, export the processed NDWI raster to your Google Drive.

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📦 Libraries Used

Library	Purpose
geemap	Interactive visualization and GEE integration
ee	Access to Google Earth Engine API
folium	Map rendering and interaction
ipywidgets	User interface controls
matplotlib, pandas	Charting and data handling

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🧠 Notes & Tips

• If maps fail to display, restart the kernel and re-run all cells.
• Use smaller AOIs and shorter date ranges for faster processing.
• Ensure your Google Earth Engine account is authenticated and active.
• Results can vary based on orbit direction and polarization selection.

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📁 Output

• Interactive Map: Displays Sentinel-1 NDWI layer highlighting flooded regions.
• Optional Export: NDWI raster to Google Drive in GeoTIFF format.

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👩‍💻 Author

Developed for flood detection and water resource mapping using open-source satellite data and cloud-based processing tools.

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📜 License

This notebook is released under the MIT License.