期刊名称：ISPRS International Journal of Geo-Information
电子版ISSN：2220-9964
出版年度：2021
卷号：10
期号：10
页码：711
DOI：10.3390/ijgi10100711
语种：English
出版社：MDPI AG
摘要：Soil moisture is a critical component for Earth science studies, and Synthetic Aperture Radar (SAR) data have high potential for retrieving soil moisture using backscattering models. In this study, polarimetric SAR (PALSAR: Phased Array type L-band Synthetic Aperture Radar) data and polarimetric decompositions including span, entropy/H/alpha, and anisotropy, in combination with surface properties resulting from field and laboratory measurements, are used to categorize the natural surface condition and discriminate the backscatter parameter in the test site for applying the inversion soil moisture retrieval. The work aims to introduce the better of two examined models in the research for soil moisture retrieval over the bare land and sparse vegetation in arid regions. After soil moisture retrieval using the two different models, the results of comparison and validation by field measurement of soil moisture have shown that the Oh model has a more realiable accuracy for soil moisture mapping, although it was very difficult to find the best model due to different characteristics in land cover. It seems the inversion model, with the field observation and polarimetric SAR data, has a good potential for extracting surface natural conditions such as surface roughness and soil moisture; however, over- and under-estimation are observed due to land cover variability. The estimation of accurate roughness and moisture data for each type of land cover can increase the accuracy of the results.
