标题： 基于卫星的印度水稻田地块制图：特伦甘纳邦案例研究 显示英文标题

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

摘要： 精准的水稻面积监测对于小农户农业区域的粮食安全和农业政策至关重要，然而传统的遥感方法在应对碎片化农业景观特有的时空异质性方面存在困难。 本研究开发了一个基于物候的分类框架，该框架系统地适应了印度特伦甘纳邦32个地区在2018-19年冬季水稻季节中的当地农业生态变化。 研究表明存在显著的时空多样性，不同地区的物候时间差异可达50天，田块大小从0.01到2.94公顷不等。 我们的地区特定校准方法总体准确率达到93.3%，比传统区域聚类方法提高了8.0个百分点，并且与官方政府统计数据有很强的一致性（R^2 = 0.981），表明遥感数据与地面真实数据之间具有良好的一致性。 该框架成功地通过适应农业气候变化绘制了732,345公顷的区域，北部地区需要延长土地准备阶段（最多55天），而南部地区则表现出压缩的种植周期。 田块大小分析显示，从中型田块到微型田块，准确性下降了6.8个百分点，为碎片化景观中的操作性监测提供了见解。 这些发现表明，遥感框架必须接受而非简化景观复杂性，推动保持科学严谨性的区域性农业监测方法，同时服务于实际政策和粮食安全应用。 显示英文摘要

摘要： 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.