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NEW MICROWAVE-BASED MISSIONS APPLICATIONS FOR RAINFED CROPS CHARACTERIZATION

机译:基于新的微波的任务应用雨量作物特征

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A multi-temporal/multi-sensor field experiment was conducted within the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) in Spain, in order to retrieve useful information from satellite Synthetic Aperture Radar (SAR) and upcoming Global Navigation Satellite Systems Reflectometry (GNSS-R) missions. The objective of the experiment was first to identify which radar observables are most sensitive to the development of crops, and then to define which crop parameters the most affect the radar signal. A wide set of radar variables (backscattering coefficients and polarimetric indicators) acquired by Radarsat-2 were analyzed and then exploited to determine variables characterizing the crops. Field measurements were fortnightly taken at seven cereals plots between February and July, 2015. This work also tried to optimize the crop characterization through Landsat-8 estimations, testing and validating parameters such as the leaf area index, the fraction of vegetation cover and the vegetation water content, among others. Some of these parameters showed significant and relevant correlation with the Landsat-derived Normalized Difference Vegetation Index (R>0.60). Regarding the radar observables, the parameters the best characterized were biomass and height, which may be explored for inversion using SAR data as an input. Moreover, the differences in the correlations found for the different crops under study types suggested a way to a feasible classification of crops.
机译:在西班牙萨拉曼卡大学(Remedhus)的土壤水分测量站网络中进行了多时间/多传感器场实验,以检索来自卫星合成孔径雷达(SAR)和即将到来的全球导航卫星系统的有用信息反射仪(GNSS-R)任务。首先是实验的目的是确定哪些雷达可观察到对作物的发展最敏感,然后定义最大影响雷达信号的作物参数。分析了由Radarsat-2获取的广泛雷达变量(反向散射系数和偏振指示器),然后利用以确定表征作物的变量。田间测量是在2015年2月和7月之间的七个谷物地块的每两周。这项工作还试图通过Landsat-8估计,测试和验证等参数优化叶子区域指数,植被覆盖的分数和植被含水量,等。其中一些参数显示出与Landsat衍生的归一化差异植被指数(R> 0.60)的显着和相关的相关性。关于雷达可观察到,最佳特征的参数是生物质和高度,可以使用SAR数据作为输入来探索的反演。此外,在研究类型下针对不同作物发现的相关性的差异表明了一种可行的作物分类方式。

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