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首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >Multiyear Crop Monitoring Using Polarimetric RADARSAT-2 Data
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Multiyear Crop Monitoring Using Polarimetric RADARSAT-2 Data

机译：使用极化RADARSAT-2数据进行多年作物监测

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This paper studies the feasibility of monitoring crop growth based on a trend analysis of three elementary radar scattering mechanisms using three consecutive years (2008–2010) of RADARSAT-2 (R-2) Fine Quad Mode data. The polarimetric synthetic aperture radar analysis is based on the Pauli decomposition. Multitemporal analysis is applied to RGB images constructed using surface scattering, double-bounce, and volume scattering, along with intensity analysis of these scattering mechanisms. The test site is located in Eastern Ontario, Canada where the cropping system is dominated by corn, spring wheat, and soybeans. Each crop has unique physical structural characteristics which provide different responses for these scattering mechanisms. Significant changes occur in these scattering mechanisms as the crops move from one phenological stage to the next. By monitoring these changes over the season, the crop growth cycle from emergence to harvest can be observed. When harvest occurs, the backscatter intensities change significantly, and these changes aid in identifying crops. The temporal evaluation of the intensity of the scattering mechanisms generally track the measured leaf area index and observed phenological plant development. Changes in growth stage are crop type specific. Thus, to monitor changes in crop phenology and the occurrence of harvest activities, knowledge of the crop grown in any particular field is required. To accommodate this requirement, a maximum likelihood classification was performed on the R-2 data to produce a crop map. An overall classification accuracy of 85$%$ was achieved.
机译：本文基于对三个基本雷达散射机制的趋势分析，使用连续三年（2008-2010年）的RADARSAT-2（R-2）精细四模模式数据进行趋势分析，研究了监测作物生长的可行性。极化合成孔径雷达分析基于Pauli分解。多时间分析应用于使用表面散射，双反射和体积散射构造的RGB图像，以及对这些散射机制的强度分析。测试地点位于加拿大安大略省东部，那里的种植系统以玉米，春小麦和大豆为主。每种作物都有独特的物理结构特征，可以为这些散射机制提供不同的响应。随着农作物从一个物候期转移到另一个物候期，这些散射机制发生了重大变化。通过监测整个季节的这些变化，可以观察到从出苗到收获的农作物生长周期。当收获发生时，反向散射强度会发生很大变化，这些变化有助于识别农作物。散射机制强度的时间评估通常跟踪测得的叶面积指数和观察到的物候植物发育。生长阶段的变化是特定于作物类型的。因此，为了监测作物物候的变化和收获活动的发生，需要了解在任何特定领域种植的作物的知识。为了满足此要求，对R-2数据执行了最大似然分类，以生成作物图。总体分类精度达到85 %% $。

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