IDENTIFICATION AND ESTIMATION OF RICE SOWN AREA IN GUNTUR DISTRICT USING SENTINEL 1A SATELLITE DATA
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Authors
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SUNIL KUMAR M
Geospatial Technology Centre, Regional Agricultural Research Station, Lam, Guntur Acharya N.G Ranga Agricultural University, Guntur, Andhra Pradesh, India
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ASHA JYOTHI B
Geospatial Technology Centre, Regional Agricultural Research Station, Lam, Guntur Acharya N.G Ranga Agricultural University, Guntur, Andhra Pradesh, India
Keywords:
Crop classification, Random Forest, SAR, Rice, Sentinel-1AAbstract
Accurate estimation of rice-sown area is critical for informed agricultural planning,
particularly in Andhra Pradesh’s complex irrigated landscapes. This study mapped rice cultivation
in Guntur district using Sentinel-1A SAR (Synthetic Aperture Radar) data integrated with machine
learning classifiers. A total of 84 ground-truth observations supported supervised classification
using Random Forest (RF) and K-Nearest Neighbours (KNN). Both models overestimated rice
extent, but RF showed superior performance with 94% user’s accuracy, 96% producer’s accuracy
and kappa coefficient of 0.76. Overestimation by KNN was largely due to confusion with waterlogged
fallow areas. Results confirm the utility of SAR and RF for precise rice area assessment and
underscore the importance of localized model calibration in heterogeneous agroecosystems.
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