Meter-level resolution surface soil moisture estimation over agricultural fields from time-series quad-pol SAR with constraints of coarse resolution CCI data products

Monitoring the spatial and temporal variations in surface soil moisture (SSM) within agricultural areas is essential for effective water resource management. Active and passive microwave remote sensing techniques have been widely utilized for SSM retrieval and mapping at both global and regional scales. Despite these advancements, accurately monitoring SSM at high spatial resolutions (up to tens of meters) remains a substantial challenge. This study proposes a novel time-series SSM retrieval algorithm for field-scale mapping of surface volumetric soil moisture using L-band quad-polarimetric (HH, HV or VH, and VV) synthetic aperture radar (PolSAR) data. The method employs a two-component polarimetric target decomposition model to isolate the soil surface scattering component by removing the contribution of vegetation scattering. The real part of the complex soil dielectric constant is subsequently estimated using the alpha approximation model, from the extracted time-series soil surface scattering coefficients. To address the under-determined nature of the soil dielectric constant estimation, which limits retrieval accuracy, constraints on soil permittivity derived from coarse-resolution microwave SSM products are introduced. Time-series volumetric SSM values are subsequently estimated using an empirical dielectric mixing model, which establishes a relationship between the dielectric constant and volumetric soil moisture. The proposed approach demonstrates significant advantages, including the generation of accurate SSM maps over vegetated areas at the SAR pixel scale, without relying on ancillary optical remote sensing data and empirical fitting models between SAR data and ground measurements. Validation of the method was conducted using data from NASA’s L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), collected during the SMAP Validation Experiment 2012 (SMAPVEX12) in Manitoba, Canada (June 17 to July 17, 2012). Comparisons between PolSAR-derived SSM estimates and in-situ measurements showed retrieval accuracies with root mean square errors (RMSE) ranging from 0.03 to 0.08 cm 3 / cm 3 and correlation coefficients (R) between 0.5 and 0.86 across canola, corn, soybean, wheat, and winter wheat fields. However, this study only covers a one-month experiment period, and does not provide the estimation results of the whole crop cycle. The retrieval accuracy varies at different dates, highlighting the dependency of the proposed method’s performance on crop growth stages and phenological parameters. In future studies, the performance of this algorithm for soil moisture retrieval across the entire crop growth cycle needs to be further validated. Although, this study demonstrates the potential of the proposed technique to produce high-resolution global soil moisture products, particularly with future L-band NISAR and P-band BIOMASS satellite missions. • Soil moisture retrieval with L-band quad PolSAR and CCI data product over crop areas. • Alpha approximation SSM retrieval model with constraints of coarse-resolution microwave products. • Isolating surface soil back scattering with two-component polarimetric SAR decomposition. • Time-series and high-resolution (6 m) soil moisture mapping with accuracy of 0.03 ≤ RMSE ≤ 0 . 08 cm 3 / cm 3 .