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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 #377981

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Parameterization of vegetation scattering albedo in the tau-omega model for soil moisture retrieval on croplands

item PARK, C.H. - Korea Meteorological Administration
item JAGDHUBER, T. - German Aerospace Center
item COLLIANDER, A. - Jet Propulsion Laboratory
item LEE, J. - Korea Meteorological Administration
item BERG, A. - University Of Guelph
item Cosh, Michael
item KIM, S. - Jet Propulsion Laboratory
item KIM, Y. - Korea Meteorological Administration
item WULFMEYER, V. - University Of Hohenheim

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/30/2020
Publication Date: 9/30/2020
Citation: Park, C., Jagdhuber, T., Colliander, A., Lee, J., Berg, A., Cosh, M.H., Kim, S., Kim, Y., Wulfmeyer, V. 2020. Parameterization of vegetation scattering albedo in the tau-omega model for soil moisture retrieval on croplands. Remote Sensing. 12(18):2939.

Interpretive Summary: Soil moisture estimation from space is often accomplished via a radiative transfer model, which uses a crop modeling model called the tauomega model. This model is deficient for croplands in particular, because of the high temporal variability of the vegetation. We propose a new set of assumptions which provide for a time varying tau-omega model. This new model is compared to in situ datasets resulting in improvements of 15-16% for croplands for the Soil Moisture Active Passive Mission products. This result is of value to the agricultural and water management communities who rely upon accurate soil moisture estimates at large scales.

Technical Abstract: An accurate radiative transfer model (RTM) is essential for the retrieval of soil moisture (SM) from microwave remote sensing data, such as the passive microwave measurements from the Soil Moisture Active Passive (SMAP) mission. This mission delivers soil moisture products based upon L-band brightness temperature data, via retrieval algorithms for surface and root-zone soil moisture, the latter is retrieved using data assimilation and model support. We found that the RTM based on the tau-omega ( -') model, can suffer from significant errors over croplands in the simulation of brightness temperature (Tb) (in average between -9.4K and + 12.0K for Single Channel Algorithm SCA; -8K and + 9.7K for Dual-Channel Algorithm DCA) if the vegetation scattering albedo (omega) is set constant and temporal variations are not considered. In order to reduce this uncertainty, we propose a time-varying parameterization of omega for the widely established zeroth order radiative transfer -' model. The main assumption is that omega can be expressed by a functional relationship between vegetation optical depth (tau) and the Green Vegetation Fraction (GVF). Assuming allometry in the tau-omega relationship, a power-law function was established and it is supported by correlating measurements of tau and GVF. With this relationship, both tau and omega increase during the development of vegetation. The application of the proposed time-varying vegetation scattering albedo results in a consistent improvement for the unbiased root mean square error of 16% for SCA and 15% for DCA. The reduction for positive and negative biases was 45% and 5% for SCA and 26% and 12% for DCA, respectively. This indicates that vegetation dynamics within croplands are better represented by a time-varying single scattering albedo. Based on these results, we anticipate that the time-varying omega within the tau-omega model will help to mitigate potential estimation errors in the current SMAP soil moisture products (SCA DCA). Furthermore, the improved tau-omega model might serve as a more accurate observation operator for SMAP data assimilation in weather and climate prediction model.