Soil Moisture Retrieval Using Polarimetric SAR Data and Experimental Observations in an Arid Environment

ISPRS International Journal of Geo-Information

Josaphat Tetuko Sri Sumantyo¹  Ryutaro Tateishi¹  Saeid Gharechelou²  Brian Alan Johnson³ 

[1] Center for Environmental Remote Sensing (CEReS), Chiba University, Chiba 263-8522, Japan;Faculty of Civil Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran;Natural Resources and Ecosystem Services Area, Institute for Global Environmental Strategies (IGES), Hayama 240-0115, Japan;

关键词: soil moisture;    polarimetric SAR data;    Oh;    Dubois;    ALOS;   

DOI  :  10.3390/ijgi10100711

来源: DOAJ

【 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.

【 授权许可】

Unknown