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Spatial and temporal soil moisture monitoring in semi-arid and humidareas with High Resolution ASAR images

机译:具有高分辨率ASAR图像的半干旱和Humidareas的空间和时间土壤水分监测

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The main aim of the analysis presented in this paper is to cross-compare two retrieval methodologies, one based on Neural Network and the other on Bayesian approach in different types of test areas and verify if they are able to retrieve the same spatial and temporal soil moisture features. The test areas are located in three regions in Italy in order to take into account different soil and meteorological conditions. The comparison of the backscattering coefficients as a function of soil moisture values indicate the same sensitivity to soil moisture variations but with a different bias which may depend on soil characteristics, vegetation presence and roughness effect. The results of the two retrieval methodologies indicate an overall good agreement. Only in one single date, the discrepancy between the results is around 8%. The algorithms are also compared in terms of processing times.
机译:本文提出的分析的主要目的是交叉比较两种检索方法,一个基于神经网络,另一个在不同类型的测试区域的贝叶斯方法上,并验证它们是否能够检索相同的空间和颞土壤水分特征。测试区域位于意大利的三个地区,以考虑不同的土壤和气象条件。作为土壤湿度值的函数的反向散射系数的比较表明对土壤水分变化的敏感性相同,但具有不同的偏差,这可能取决于土壤特征,植被存在和粗糙度效应。两种检索方法的结果表明整体良好的一致性。只在一个单一日期,结果之间的差异约为8%。还在处理时间方面进行比较算法。

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