Abstract
ABSTRACT Tea, as a key cash crop, demands year-round monitoring including the rainy months. Our study in Dehradun, Uttarakhand, India, leverages Synthetic Aperture Radar (SAR) data for its cloud-penetrating capabilities. Radar Vegetation Index (RVI) from Sentinel-1 and the Normalized Difference Vegetation Index (NDVI) from Sentinel-2 on the Google Earth Engine (GEE) platform was used. Results indicate that RVI can detect stages of tea plantation growth, even during the rainy season, while NDVI provides detailed insights into cultural operations, except rainy seasons. This method supports effective tea plantation monitoring and supply chain management across seasons.
Published Version
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