Satellite-based Rabi rice paddy field mapping in India: a case study on Telangana state

TL;DR

This study presents a phenology-driven remote sensing framework for accurate rice paddy mapping in Telangana, India, adapting to local variations and achieving high accuracy in fragmented smallholder landscapes.

Contribution

It introduces a district-specific calibration approach that significantly improves rice mapping accuracy by accounting for local agro-ecological and phenological variations.

Findings

Achieved 93.3% overall accuracy in rice mapping.

Mapped 732,345 hectares of rice fields across Telangana.

Identified that field size impacts mapping accuracy, especially for tiny fields.

Abstract

Accurate rice area monitoring is critical for food security and agricultural policy in smallholder farming regions, yet conventional remote sensing approaches struggle with the spatiotemporal heterogeneity characteristic of fragmented agricultural landscapes. This study developed a phenology-driven classification framework that systematically adapts to local agro-ecological variations across 32 districts in Telangana, India during the 2018-19 Rabi rice season. The research reveals significant spatiotemporal diversity, with phenological timing varying by up to 50 days between districts and field sizes ranging from 0.01 to 2.94 hectares. Our district-specific calibration approach achieved 93.3% overall accuracy, an 8.0 percentage point improvement over conventional regional clustering methods, with strong validation against official government statistics (R^2 = 0.981) demonstrating excellent agreement between remotely sensed and ground truth data. The framework successfully mapped 732,345 hectares by adapting to agro-climatic variations, with Northern districts requiring extended land preparation phases (up to 55 days) while Southern districts showed compressed cultivation cycles. Field size analysis revealed accuracy declining 6.8 percentage points from medium to tiny fields, providing insights for operational monitoring in fragmented landscapes. These findings demonstrate that remote sensing frameworks must embrace rather than simplify landscape complexity, advancing region-specific agricultural monitoring approaches that maintain scientific rigor while serving practical policy and food security applications.