"NDVI and SPI Correlation Analysis in Google Earth Engine | MODIS & CHIRPS Tutorial"

🌍 Learn how to analyze the correlation between vegetation health and rainfall anomalies using **Google Earth Engine (GEE)**. This step-by-step tutorial walks you through calculating the **Standardized Precipitation Index (SPI)** from **CHIRPS precipitation data** and comparing it with **MODIS NDVI** values.

🛰️ Datasets Used:
- MODIS MOD13A2 NDVI (16-day composite)
- CHIRPS Daily Precipitation Data (Climate Hazards Group)

📌 In this video, you'll learn:
- How to load and scale MODIS NDVI in GEE
- How to calculate SPI using CHIRPS precipitation
- How to visualize drought and vegetation stress
- How to create a SPI–NDVI scatter plot in GEE

📊 Perfect for remote sensing analysts, climate researchers, ecologists, and GEE learners who want to study drought-vegetation dynamics.

🔧 Tools: Google Earth Engine (JavaScript API)

🎓 Keywords: NDVI, SPI, CHIRPS, MODIS, Remote Sensing, Google Earth Engine Tutorial, Drought Analysis, Climate Change, Vegetation Monitoring, Rainfall Anomaly, Scatter Plot, GEE JavaScript, MOD13A2, CHG CHIRPS

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Видео "NDVI and SPI Correlation Analysis in Google Earth Engine | MODIS & CHIRPS Tutorial" канала Geospatial Analysis