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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >A Bayesian Change Detection Approach for Retrieval of Soil Moisture Variations Under Different Roughness Conditions
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A Bayesian Change Detection Approach for Retrieval of Soil Moisture Variations Under Different Roughness Conditions

机译：不同粗糙度条件下土壤水分变化的贝叶斯变化检测方法

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摘要

A Bayesian approach for soil moisture change detection under different roughness conditions is proposed in this letter. The main objective of this approach is to exploit the changes in backscattering signals and relate them to soil moisture variations over agricultural fields by considering also the possible changes in the radar signal due to roughness variability. The method is trained and tested on two data sets acquired during SMEX'02 experiment. One data set considers AirSAR P-band data for which the soil can be considered bare and the second data set considers the correspondent L-band data for which the influence of vegetation cannot be considered negligible. The results indicate that the approach is able to detect soil moisture changes both for P-band and L-band data. In case of L-band one main problem is indeed the presence of vegetation which reduces the backscattering coefficients dynamics.
机译：本文提出了一种在不同粗糙度条件下检测土壤水分变化的贝叶斯方法。这种方法的主要目的是利用反向散射信号的变化，并将其与农田中土壤水分的变化联系起来，同时还要考虑由于粗糙度变化而引起的雷达信号的可能变化。在SMEX'02实验期间获取的两个数据集上对该方法进行了训练和测试。一个数据集考虑了可以认为土壤是裸露的AirSAR P波段数据，第二个数据集考虑了对植被的影响不能忽略的相应L波段数据。结果表明，该方法能够检测P波段和L波段数据的土壤湿度变化。在L波段的情况下，一个主要问题确实是植被的存在，这降低了后向散射系数的动态性。

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