Soil Moisture Estimation over Crop Fields Combined with Fully Polarimetric SAR and Passive Microwave Products Data

High spatial resolution soil moisture (SM) mapping is essential for a wide range of applications, especially for precision irrigation and crop management. This work proposes an SM estimation method combined with time series of L-band fully polarimetric synthetic aperture radar (PolSAR) and passive SM products over crop areas. Regarding the challenge of eliminating vegetation canopy scattering on SM estimation, model-based polarimetric decomposition is implemented as a pretreatment step in which the surface scattering component in both HH and VV channels are extracted. Afterward, dual-pol surface scattering information normalization is dealt with the cosine-squared incidence angle normalization method, which makes it possible for SM inversion with multiple tracks and multi-incidence SAR observations. With the time series of normalized surface scattering information, the alpha approximation-based change detection algorithm (AACD) is used for SM estimation. Since the AACD algorithm is reported with an underdetermined problem of parameter solution and the underestimation issue of soil moisture inversion, an extended AACD which incorporates dual-pol (HH and VV) SAR observations, namely the Dual-pol AACD algorithm, is proposed in this study. Besides, the minimum and maximum values of passive microwave soil moisture data of the whole study area and the entire study period are introduced as constraints in Dual-pol AACD when solving the unknown parameters of the real part of the soil dielectric constant. Finally, the obtained time series of soil dielectric constants are converted to volumetric soil water content using dielectric mixing model. 56 sets of collected UAVSAR L-band data with 4 different flight lines (#31603, #31604, #31605, #31606) of Winnipeg, Manitoba, Canada in 2012 (SMAPVEX12) are used to validate the Dual-pol AACD algorithm. Passive microwave SM constraints are collected from Soil Moisture and Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer 2 (AMSR-2) products. The performance of the proposed method is evaluated by comparing the in-situ measurements against the soil moisture estimates of wheat, corn, soybeans, bean, and canola fields at different phenological stages. Results show that the proposed method provides an accuracy of RMSE ≤ 6.5 cm³•cm^-3 over all the selected crop fields, which is better than that without the introduction of constraints from passive microwave SM products. This work also compares the SM estimation performance using constraints from SMOS and AMSR-2. In addition, SM estimates in different crop fields and growth stages are also provided regarding the variation of crop morphological characteristics and biophysical properties. It concludes that the proposed SM estimation method has great potential for local and global SM mapping in a high resolution with existing and upcoming L-band SAR data, such as ALOS-2 (Japan), LT-1 (China), NISAR (America and India) and Tandem-L (Germany), etc.