Abstract
ABSTRACTIn this paper, a soil moisture retrieval from full-polarimetric synthetic aperture radar (SAR) data is investigated for sparsely vegetated soil surfaces. An improved retrieval method adapting the variations in vegetation is proposed by incorporating the generalized volume model into the polarimetric two-scale two-component model (PTSTCM). The feasibility of the method, termed as the adaptive PTSTCM, has been tested for tropical peatland sites in Indonesia which exhibit a variety of sparse vegetation cover on soil after land clearing activities. The data were collected in March and August 2017 with the time domain reflectometry (TDR) probe for a total of 18 sample points over 11 regions. The method was applied to ALOS-2 L-band quad-pol SAR data that were acquired simultaneously with field measurements. We compared the results between the proposed adaptive PTSTCM and the original PTSTCM that utilizes specific types of volume model (i.e., randomly, horizontally, and vertically oriented volume models). Scatterplots of estimated versus measured results reveal that the adaptive PTSTCM yields a root-mean-square error (RMSE) of 5.1vol. and inversion rate of 35.0 and 58.5 for March and August data, respectively, which are found to be superior to those of the original PTSTCM.
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