Improved soil moisture retrieval during crop growing season using passive microwave data at L-band

• A novel ratio-based method enhances τ-ω model’s vegetation transmissivity simulation. • Ratio-based model integration boosts soil moisture retrieval accuracy. • Combining RVI enhances soil moisture retrieval under adverse weather. • The method enhances soil moisture retrieval accuracy in diverse agricultural settings. Soil moisture (SM) is a critical driver of crop growth and a key indicator of agriculture monitoring. Efficient and accurate SM monitoring is essential for advancing sustainable agricultural practices. The zero-order radiative transfer model (τ-ω), widely used for SM estimation from passive microwave satellites, simplifies the characterization of vegetation-covered surfaces by neglecting multiple scattering effects. However, its canopy transmissivity calculations within this framework remain limiting. To address this gap, this study proposes a ratio-based approach to quantify vegetation transmissivity, capitalizing on unique features of passive microwave radiative transfer over vegetated surfaces. The method is then integrated into the Single Channel Algorithm (SCA) and the L-band Microwave Emission of the Biosphere (L-MEB) model, respectively, to improve the accuracy of SM retrieval. To mitigate the weather-related constraints of optical data, the study is further to investigate the integration of the Radar Vegetation Index (RVI) into the enhanced models. Experimental results demonstrate that the ratio-based method effectively refines vegetation transmissivity estimations, thereby enhancing SM retrieval accuracy: For L-MEB model, R increased from 0.63 to 0.72 and RMSE decreased from 0.119 m 3 /m 3 to 0.061 m 3 /m 3 ; for SCA-H and SCA-V, R increased from 0.48 and 0.54 to 0.71 and 0.63, and RMSE decreased from 0.150 m 3 /m 3 and 0.102 m 3 /m 3 to 0.053 m 3 /m 3 and 0.067 m 3 /m 3 , respectively. This study presents a novel approach for SM retrieval. This work underscores the potential of the ratio-based method to improve SM assessments across diverse environmental and climatic conditions.