Soil Moisture Retrieval in Winter Wheat Fields at Different Growth Stages: Integrating a Two-Component Polarimetric SAR Decomposition With CIEM - Wenxin Xue; Qinghua Xie; Xing Peng; J. David Ballester-Berman; Jinfei Wang; Jiali Shang; Haiqiang Fu; Jianjun Zhu (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing)
Polarimetric synthetic aperture radar (PolSAR) offers strong volume penetrability, high resolution, sensitivity to surface dielectric properties, and the ability to acquire abundant ground target information, thus having considerable advantages in soil moisture (SM) inversion at the agricultural field scale. In recent years, numerous PolSAR-model-based SM retrieval studies have been published. Nevertheless, there is no consensus within the synthetic aperture radar community when it comes to the identification of those existing physically based models and algorithms providing the best performances in terms of accuracy, reproducibility, complexity, and generalizability (i.e., their application to different crops and growth stages). This article contributes to the current body of the literature on radar-driven SM retrieval approaches by proposing an integrated C-band two-component polarimetric decomposition method applied on winter wheat fields. The proposed technique is built on existing vegetation and soil surface microwave models; therefore, the outcomes of this article shed light on the applicability and robustness of such models. Two X-Bragg surface scattering models with broader roughness adaptability and three generalized volume scattering models (namely, the generalized volume scattering model (GVSM), the simplified adaptive volume scattering model (SAVSM), and the simplified Neumann volume scattering model (SNVSM)) were incorporated into the decomposition framework to mitigate vegetation effects and isolate the surface scattering component. Then, the SM was obtained by combining the calibrated integral equation model with the derived optimal roughness parameter. The performance analysis was conducted by using C-band fully polarimetric RADARSAT-2 images acquired in 2019 over winter wheat crops in southwestern Ontario, Canada. Experimental results evidence that the proposed methodology achieves a reasonable accuracy in SM inversion. Notably, the X-Bragg model based on a zero-mean normal distribution combined with the SNVSM outperforms all other options with an overall root-mean-square error of 6.17 Vol.% and a correlation coefficient of 0.53.