Soil Moisture Estimation Using Sentinel-1 SAR Imagery in Google Earth Engine

In this video, we demonstrate soil moisture estimation using Sentinel-1 Synthetic Aperture Radar (SAR) imagery in Google Earth Engine (GEE). Learn how to process SAR data, apply noise reduction techniques, and generate soil moisture time-series charts for analysis.
Topics Covered:
Defining the study area in GEE. Filtering Sentinel-1 SAR data (Ascending & Descending orbits). Generating 10-day temporal composites. Converting SAR data from dB to linear scale. Applying speckle noise filtering. Masking out water and urban areas using land cover data. Computing the Soil Moisture Index (SMI). Generating time-series charts for soil moisture analysis.
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Exporting processed data to Google Drive
Resources:
Code link:
https://code.earthengine.google.com/c...
Sentinel-1 SAR dataset: https://developers.google.com/earth-e...
Dynamic World V1: https://developers.google.com/earth-e...
This video is perfect for environmental researchers, remote sensing specialists, and GIS professionals interested in aquatic ecosystem monitoring.
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#GoogleEarthEngine #Sentinel1 #SoilMoisture #RemoteSensing #GIS #SAR #EarthObservation