Monitoring Rice Crop Dynamics in Koppal District using Time-Series Sentinel-1 Data on Google Earth Engine

Lokesh Naik; Nagabhushana; N Omprakash Naik  lowast; Iwansukshisha Marbaniang

doi:10.53989/bu.ge.v14.i1.25.naik

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DOI: 10.53989/bu.ge.v14.i1.25.naik

Year: 2025, Volume: 14, Issue: 1, Pages: 47-53

Original Article

Monitoring Rice Crop Dynamics in Koppal District using Time-Series Sentinel-1 Data on Google Earth Engine

Lokesh Naik¹, Nagabhushana², N Omprakash Naik^3∗, Iwansukshisha Marbaniang¹

¹M.Sc. Scholar, Bangalore University, Bangalore, 560056, Karnataka, India
²Lecturer, Department of Geography, Jnana Bharathi Campus, Bangalore University, Bangalore, 560056, Karnataka, India
³Assistant Professor, Department of Economics, Government First Grade College, Yellapur, Affiliated to Karnataka University, Dharwad, 581359, Karnataka, India

Received Date:12 March 2025, Accepted Date:20 June 2025

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Accurate estimation of crop area and timely assessment of crop losses are essential for empowering farmers, especially as technological advancements transform methods of area estimation, crop production, and crop insurance. This study focuses on mapping rice crop dynamics during the Kharif season (June to October) using satellite-based time-series data. We employed data from Sentinel-1 and Sentinel-2 satellites, combined with the Random Forest classification technique, to identify late transplantation and monitor rice growth at 15-day intervals. The study analyzed data from 2019 to 2023 and classified six crop categories using a robust sampling method. The methodology demonstrated high classification accuracy, ranging from 84.53% to 91.23% across different datasets and phases of crop growth. Results show that the total rice crop area in Koppal district fluctuated moderately between 2020 and 2023, ranging from 53,121.36 ha to 59,129.33 ha, indicating overall stability in rice cultivation despite seasonal variations and the effectiveness of spatiotemporal analysis for yield estimation, risk forecasting, and agricultural planning. This approach can contribute significantly to the development of sustainable agricultural strategies and policies, especially in densely populated countries like India.

Keywords: Crop mapping, Satellite remote sensing, Random Forest, Rice monitoring, Climate risk, Agricultural planning

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Copyright

© 2025 Naik et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published By Bangalore University, Bengaluru, Karnataka