Optimal soil dielectric model map for global soil moisture retrieval from SMAP

Accurate characterization of soil complex dielectric permittivity is essential for passive microwave soil moisture (SM) estimation, but the uncertainty introduced by dielectric model choice remains insufficiently characterized. Moreover, the impact of soil organic matter (SOM) on SM retrieval has not been systematically assessed at the global scale. Therefore, this study retrieved SM using five mineral-soil and two organic-soil dielectric models, evaluated their performances and quantified how their structural differences affect SM retrieval uncertainties based on L-band Soil Moisture Active Passive (SMAP) data. We revealed a substantial inter-model divergence of retrieved SM among dielectric models with a global mean RMSE of 0.035 m 3 /m 3 and this discrepancy was most pronounced in forests and northern high-latitude regions. We further demonstrated that soil dielectric model choice accounted for 24% of the overall SM retrieval error, and this contribution greatly increased under high SOM conditions. Validation using in-site measurements showed that organic soil-based models generally outperformed mineral soil-based models with significantly higher R values. The SOM-aware Mironov 2019 model had the highest correlation (mean R = 0.67), but also showed the largest absolute error primarily due to its overestimation of SOM effect. Evaluation using spatial representative sites yielded similar results regarding the relative performances of dielectric models. Using the triple collocation analysis, we demonstrated that organic soil-based models were the optimal choice over 36.51% of global regions, showing both higher correlation and lower random error. We then derived a global map of optimal soil dielectric models, which could further improve SM retrieval performance relative to SMAP SCA SM product across 76.73% of global areas. Our results underscore the critical role of soil dielectric model selection in L-band SM retrieval and provide new insights for developing SM retrieval algorithms and improving global SM products.