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首页> 外文会议>Agriculture and Hydrology Applications of Remote Sensing; Proceedings of SPIE-The International Society for Optical Engineering; vol.6411 >Monitoring of wet season rice crop at state and national level in India using multi-date Synthetic Aperture Radar data
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Monitoring of wet season rice crop at state and national level in India using multi-date Synthetic Aperture Radar data

机译：使用多日期合成孔径雷达数据监测印度州和国家级湿季水稻作物

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摘要

Rice crop grown during the monsoon (wet) season is the most important food grain in India. The crop is grown under varied cultural and management practices. The present paper highlights the results of rice monitoring being carried out for the past five years (2001-02 to 2005-06) using multi-date RADARSAT ScanSAR Narrow-B data. 30 ScanSAR scenes covering thirteen states account for 95 percent of national crop area. 90 scenes are analysed to assess the national wet season rice crop. A stratified sampling plan is used to analyse 5*5 km segments accounting for 15 per cent of the crop area in each of the study states. A decision-rule classifier has been developed based on a Radiative Transfer (RT) model developed and calibrated using large number of rice sites in India and controlled field experiments. This procedure accounts for change in backscatter as a result of transplanting of rice and crop growth in multi-date data to classify rice areas. Results indicate more than 93 per cent accuracy of area estimation at state level and 97 per cent at national level. It is feasible to assess deviations in crop planting operation (late or early) for a given area.
机译：季风（湿）季节种植的水稻是印度最重要的粮食。该农作物是根据不同的文化和管理方式种植的。本文重点介绍了使用多日期RADARSAT ScanSAR Narrow-B数据在过去五年（2001-02至2005-06）进行的水稻监测结果。覆盖13个州的30个ScanSAR场景占全国作物面积的95％。分析了90个场景，以评估全国湿季水稻作物。分层抽样计划用于分析5 * 5公里的路段，这些路段在每个研究州均占作物面积的15％。根据印度大量稻田和受控田间试验开发并校准的辐射转移（RT）模型，开发了决策规则分类器。该程序在多日期数据中对稻米移栽和作物生长造成的反向散射变化进行了分类，以对稻米区域进行分类。结果表明，在州一级，面积估计的准确性超过93％，在国家级，则为97％。评估给定区域的作物种植操作（后期或早期）的偏差是可行的。

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