L-Band Radar Soil Moisture Retrieval Without Ancillary Information

Abstract

A radar-only retrieval algorithm for soil moisture mapping is applied to L-band scatterometer measurements from the Aquarius. The algorithm is based on a nonlinear relation between L-band backscatter and volumetric soil moisture and does not require ancillary information on the surface, e.g., land classification, vegetation canopy, surface roughness, etc. It is based on the definition of three limiting cases or end-members: 1) smooth bare soil; 2) rough bare soil; and 3) maximum vegetation condition. In this study, an estimation method is proposed for the end-member parameters that is iterative and only uses space-borne measurements. The major advantages of the algorithm include an analytic formulation (direct inversion possible), and the fact that there is no requirement for ancillary information. Ancillary data often misclassify the surface and the parameterizations linking surface classification to model parameter values are often highly uncertain. The retrieval algorithm is tested using 3 years of space-borne scatterometer observations from the Aquarius/SAC-D. Retrieved soil moisture accuracy is assessed in several ways: comparison of global spatial patterns with other available soil moisture products (two reanalysis modeling products and retrievals based on the Aquarius radiometer), extended triple collocation (ETC) and time series analysis over selected target areas. In general, low bias and standard deviation are observed with levels comparable to independent radiometer-based retrievals. The errors, however, increase across areas with high vegetation density. The results are promising and applicable to forthcoming L-band radar missions such as SMAP-NASA (2015) and SAOCOM-CONAE (2016).