IDENTIFICATION AND ESTIMATION OF RICE SOWN AREA IN GUNTUR DISTRICT USING SENTINEL 1A SATELLITE DATA - M , SUNIL KUMAR (The Journal of Research ANGRAU, 2026)
Accurate estimation of rice-sown area is critical for informed agricultural planning,particularly in Andhra Pradesh’s complex irrigated landscapes. This study mapped rice cultivationin Guntur district using Sentinel-1A SAR (Synthetic Aperture Radar) data integrated with machinelearning classifiers. A total of 84 ground-truth observations supported supervised classificationusing Random Forest (RF) and K-Nearest Neighbours (KNN). Both models overestimated riceextent, but RF showed superior performance with 94% user’s accuracy, 96% producer’s accuracyand kappa coefficient of 0.76. Overestimation by KNN was largely due to confusion with waterloggedfallow areas. Results confirm the utility of SAR and RF for precise rice area assessment andunderscore the importance of localized model calibration in heterogeneous agroecosystems.