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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #403645
Research Project: From Field to Watershed: Enhancing Water Quality and Management in Agroecosystems through Remote Sensing, Ground Measurements, and Integrative Modeling

Location: Hydrology and Remote Sensing Laboratory

Title: Retrieving forest soil moisture from SMAP observations considering a microwave polarization difference index (MPDI) to tau-omega model

Author
item PARK, C.H. - Korea Meteorological Administration
item COLLIANDER, A. - Jet Propulsion Laboratory
item JAGDHUBER, T. - German Aerospace Center
item BERG, A. - University Of Guelph
item LEE, J.H. - Korea University
item BOO, K.O. - Collaborator
item Cosh, Michael

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/1/2024
Publication Date: 6/1/2024
Citation: Park, C., Colliander, A., Jagdhuber, T., Berg, A., Lee, J., Boo, K., Cosh, M.H. 2024. Retrieving forest soil moisture from SMAP observations considering a microwave polarization difference index (MPDI) to tau-omega model. Remote Sensing of Environment. 9. Article e100131. https://doi.org/10.1016/j.srs.2024.100131.
DOI: https://doi.org/10.1016/j.srs.2024.100131

Interpretive Summary: Remotely sensing surface soil moisture is complicated by dense vegetation. Current methodologies do not accurately account for this dense vegetation and its characterization, referred to at the vegetation optical depth. A new methodology is applied called the unified radiative transfer model which improves the estimation of soil moisture and mirrors the results of real time operational estimations of remote sensing signals. This will be valuable for the advancement of the field of soil moisture remote sensing.

Technical Abstract: Estimation of soil moisture from microwave brightness temperature is extremely challenging in densely vegetated areas. Here, the retrieved soil moisture from SMAP tends to be consistently overestimated, sometimes exceeding the saturation of mineral soils. This means that based on current remote sensed soil moisture, the phenomenon of climate extremes such as flood and drought cannot be properly detected or monitored over these regions which are important for forest and natural resources management and climate change study. In this study, we assumed that the main problem is due to the scattering albedo, considered as a set of constants in the conventional microwave radiative transfer models (RTMs). To tackle the overestimation issue over dense vegetation, we introduce the unified radiative transfer model, which includes adjusting omega based on the measured vegetation optical depth VOD. In the operational RTMs increasing VOD with constant omega, produces a higher brightness temperature (Tb) during modeling. However, when the unified R is applied, the Tb is similar to the operational RTMs up to approximately 0.6 VOD, but the increase slows down with VOD >0.6 VOD. This slowdown pattern due to omega change in Tb simulated by the unified is a new finding in this study; it turns out to be a critical factor to achieve a significant bias reduction in soil moisture estimation from microwave brightness temperature over forested regions.