How much does the soil dielectric model matter for passive microwave soil moisture retrieval?

Passive microwave soil moisture (SM) retrieval relies on an accurate representation of soil complex dielectric permittivity, yet the uncertainty introduced by dielectric model selection remains insufficiently quantified. In this study, we evaluated five widely used mineral soil dielectric models and two organic soil dielectric models, and assessed how errors and uncertainties from soil dielectric models propagated into SM retrievals using the single channel algorithm (SCA) based on L-band Soil Moisture Active Passive (SMAP) data. We revealed a substantial inter-model disagreement of retrieved SM with a global mean spread of 0.044 m³/m³. The largest divergence occurred in the tropics and northern high latitudes, where mean RMSE value exceeded 0.10 m³/m³. In generally, organic soil models outperformed mineral soil models, yielding significantly higher R (0.66 vs 0.64) and lower ubRMSE (0.068 m³/m³ vs 0.069 m³/m³) values. Among all models, the Mironov 2019 model that accounts for soil organic carbon (SOC) effect exhibited the best performance with a mean R value of 0.66 and ubRMSE value of 0.07 m³/m³. We further demonstrated that soil dielectric model choices overall contributed 27.6% of SM retrieval error, especially under high SOC conditions. Finally, we derived a global map of optimal dielectric model using triple collocation analysis, and showed that the R and ubRMSE metrics could be further improved by 0.04 and 0.006 m³/m³. compared with the SMAP SM product. Our results highlight the importance of dielectric model specific uncertainty characterization and support regionally adaptive dielectric parameterizations for more accurate L-band SM products.