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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 #370518

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: Assessing the impact of soil layer specification on the observability of modeled soil moisture and brightness temperature

item SHELLITO, P. - Goddard Space Flight Center
item KUMAR, S. - Goddard Space Flight Center
item SANTANELLO, J. - Goddard Space Flight Center
item LAWSTON, P. - Goddard Space Flight Center
item BOLTON, J. - Goddard Space Flight Center
item Cosh, Michael
item Bosch, David - Dave
item Holifield Collins, Chandra
item Livingston, Stanley
item Prueger, John
item Seyfried, Mark
item Starks, Patrick

Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/14/2020
Publication Date: 9/1/2020
Citation: Shelito, P., Kumar, S., Santanello, J., Lawston, P., Bolton, J., Cosh, M.H., Bosch, D.D., Holifield Collins, C.D., Livingston, S.J., Prueger, J.H., Seyfried, M.S., Starks, P.J. 2020. Assessing the impact of soil layer specification on the observability of modeled soil moisture and brightness temperature. Journal of Hydrometeorology. 21(9):2041-2060.

Interpretive Summary: Soil moisture modeling is a valuable tool for bridging the gap between in situ station monitoring and remote sensing scales. However, models are heavily dependent upon the parameters which construct the foundation of the model and the depths of soil computed in the models versus the satellite and in situ estimates. This study was developed to assess the magnitude of influence these two issues have on model estimates. It was found that soil layer modeling is less important than the model parameters which have a large influence on the ability to measure soil moisture at a particular location. This study will help to prioritize research focus on future model development toward model parameters such as soil texture or topography which has a primary impact on model accuracy.

Technical Abstract: The utility of hydrologic land surface models (LSMs) can be enhanced by using information from observational platforms. Mismatches between the two are common and can be attributed to 1) physical incongruities between the volumes being characterized and 2) inadequate model parameters and/or parameterizations. This study assesses the degree to which model agreement with observations (observability) is affected by these two mechanisms. The Noah and Noah-MP LSMs by default characterize surface soil moisture (SSM) in the top 10 cm of the soil column. This depth may be reasonable when comparing modeled SSM to soil moisture from in situ probes centered at 5 cm, but it is notably different from the 5 cm (or less) sensing depth of NASA’s Soil Moisture Active Passive (SMAP) satellite mission. These depth inconsistencies are examined by using thinner model layers in the Noah and Noah-MP land surface models and comparing resultant simulations to in situ and SMAP soil moisture. In addition, a forward radiative transfer model to simulate microwave brightness temperatures (Tbs) is used to facilitate direct comparisons of LSM-based and SMAP based L-band Tb observations. Observability is quantified using Kolmogorov-Smirnov distance values, calculated from empirical cumulative distribution functions of SSM and Tb time series. This study finds that the role of increased soil layer discretization on model SSM and Tb observability is secondary to the influence of model parameterizations, which can dominate the systematic differences and observability of simulated soil moisture.