SAR & Scatterometer-Based Change Detection for Drought and Rainfall Monitoring

This project develops a workflow for detecting environmental changes (e.g., drought or heavy rainfall) using radar scatterometer (ASCAT, SCA) and SAR (Sentinel-1) data integrated with Google Earth Engine (GEE).

🌍 Project Overview

Satellite radar data are powerful for monitoring land surface moisture and vegetation structure under varying weather conditions. By analyzing time-series backscatter values, we can detect anomalies associated with drought, flooding, or precipitation changes.

Project Title

“SAR & Scatterometer-Based Change Detection for Drought and Rainfall Monitoring Using Google Earth Engine”

Short Description

This project demonstrates a workflow for detecting and analyzing environmental changes — such as drought or excessive rainfall — using radar scatterometer and SAR data. The pipeline combines Google Earth Engine (GEE) for data access and processing with Python-based analytics for trend detection and visualization.

🚀 Workflow

1. Data Collection: Access Sentinel-1 SAR and ASCAT data via Google Earth Engine.
2. Preprocessing: Apply orbit correction, speckle filtering, and terrain correction.
3. Change Detection: Compute backscatter anomalies (Δσ⁰) and moisture indices.
4. Validation: Compare with CHIRPS or ERA5 rainfall data.
5. Visualization: Generate change maps and time-series drought indicators.

🧠 Example Visualization

🛠️ Tools & Libraries

• Google Earth Engine (JavaScript & Python API)
• Python 3.10: geemap, earthengine-api, xarray, numpy, matplotlib, scipy
• Data Sources: Sentinel-1, ASCAT, CHIRPS, ERA5

📈 Outputs

• Surface moisture change maps
• Drought anomaly time-series
• Rainfall vs. backscatter correlation plots

👩‍💻 Author

[Vahid Arastuian]
Earth Observation & Remote Sensing Specialist
🔗 [https://varastuian.github.io/] | 📧 [varastuian@gmail.com]