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Use of radar and optical remotely sensed data for soil moisture retrieval over vegetated areas

机译:利用雷达和光学遥感数据在植被覆盖的地区获取土壤水分

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This work assesses the possibility of obtaining soil moisture maps of vegetated fields using information derived from radar and optical images. The sensor and field data were acquired during the SMEX'02 experiment. The retrieval was obtained by using a Bayesian approach, where the key point is the evaluation of probability density functions (pdfs) based on the knowledge of soil parameter measurements and of the corresponding remotely sensing data. The purpose is to determine a useful parameterization of vegetation backscattering effects through suitable pdfs to be later used in the inversion algorithm. The correlation coefficients between measured and extracted soil moisture values are R=0.68 for C-band and R=0.60 for L-band. The pdf parameters have been found to be correlated to the vegetation water content estimated from a Landsat image with correlation coefficients of R=0.65 and 0.91 for C- and L-bands, respectively. In consideration of these correlations, a second run of the Bayesian procedure has been performed where the pdf parameters are variable with vegetation water content. This second procedure allows the improvement of inversion results for the L-band. The results derived from the Bayesian approach have also been compared with a classical inversion method that is based on a linear relationship between soil moisture and the backscattering coefficients for horizontal and vertical polarizations.
机译:这项工作评估了使用从雷达和光学图像获得的信息获得植被田间土壤湿度图的可能性。在SMEX'02实验期间获取了传感器和现场数据。检索是通过贝叶斯方法进行的,其中关键点是根据土壤参数测量和相应遥感数据的知识对概率密度函数(pdfs)进行评估。目的是通过适当的pdf来确定植被反向散射效应的有用参数化,以供以后在反演算法中使用。测得的土壤水分值与提取的土壤水分值之间的相关系数对于C波段为R = 0.68,对于L波段为R = 0.60。 pdf参数已发现与Landsat图像估计的植被含水量相关,C波段和L波段的相关系数分别为R = 0.65和0.91。考虑到这些相关性,已经执行了第二次贝叶斯方法,其中pdf参数随植被含水量而变化。第二个过程可以改善L波段的反演结果。从贝叶斯方法得出的结果也已与经典反演方法进行了比较,该方法基于土壤水分与水平极化和垂直极化的反向散射系数之间的线性关系。

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