- Estimating soil moisture from microwave brightness temperature is extremely challenging in densely vegetated areas. The soil moisture retrieved from the Soil Moisture Active Passive (SMAP) measurements tends to be consistently overestimated, sometimes exceeding the saturation level of mineral soils. Therefore, the retrieved soil moisture cannot detect or monitor climate extremes, such as floods and droughts for forests, natural resource management, and climate change research. We hypothesize that the main issue is that the scattering albedo (ω) and the optical depth (τ) are parameterized solely with NDVI (Normalized Difference Vegetation Index), neglecting the polarization characteristics from vegetation structure. This study proposes a weighting factor between scattering and optical thickness, a function of MPDI (Microwave Polarization Difference Index), and applies it to both parameters simultaneously to increase the scattering effect and decrease the attenuation effect in high MPDI.Estimating soil moisture from microwave brightness temperature is extremely challenging in densely vegetated areas. The soil moisture retrieved from the Soil Moisture Active Passive (SMAP) measurements tends to be consistently overestimated, sometimes exceeding the saturation level of mineral soils. Therefore, the retrieved soil moisture cannot detect or monitor climate extremes, such as floods and droughts for forests, natural resource management, and climate change research. We hypothesize that the main issue is that the scattering albedo (ω) and the optical depth (τ) are parameterized solely with NDVI (Normalized Difference Vegetation Index), neglecting the polarization characteristics from vegetation structure. This study proposes a weighting factor between scattering and optical thickness, a function of MPDI (Microwave Polarization Difference Index), and applies it to both parameters simultaneously to increase the scattering effect and decrease the attenuation effect in high MPDI. The validation results based on the Climate Reference Network revealed that considering MPDI is critical in reducing soil moisture overestimation errors and obtaining more accurate soil moisture over forested regions. This results in correlation improving from 0.36 to 0.44, a decrease in ubRMSE from 0.179 to 0.125 cm³cm-³, and bias lowering from 0.127 to 0.060 cm³cm-³ in comparison with the SMAP measurements over forested regions.…
