High resolution soil moisture estimation in the Mississippi Delta via data assimilation using the NASA Land Information System
Valentine
Anantharaj, Mississippi State University / GeoResources Institute, val@gri.msstate.edu
(Presenting)
Paul
Houser, Center for Research on Environment and Water / George Mason University, houser@gmu.edu
Luo
Yan, Center for Research on Environment and Water / George Mason University, yluo@iges.org
Mostovoy
Georgy, Mississippi State University / GeoResources Institute, mostovoi@gri.msstate.edu
Peters-Lidard
Christa, NASA GSFC / Hydrological Sciences Branch, christa.d.peters-lidard@nasa.gov
Li
Bailing, NASA GSFC / Hydrological Sciences Branch, bli@hsb.gsfc.nasa.gov
The goal of this Rapid Prototyping Capability (RPC) experiment was to evaluate the feasibility of using NASA resources to add value to the capabilities of the USDA Soil Climate Analysis Network (SCAN) by deriving soil moisture products at higher spatial resolutions. We used an Ensemble Kalman Filtering (EnKF) algorithm within NASA’s Land Information System (LIS) for the purpose of effectively combining the NASA satellite land surface products (AMSR-E soil moisture) and in-situ observations with land surface simulations to extend SCAN capabilities in a regional domain. We performed a set of data assimilation experiments using the Noah Land Surface Model (LSM), available in the LIS framework, and configured it over our study region in the lower Mississippi river valley at spatial resolutions of 1x1 km2 and 15x15 km2. Preliminary results show that soil moisture assimilation of SCAN observations is reasonably better, compared to the model simulation alone or the AMSR-E assimilation. In addition, we found that, given the large systematic discrepancies in soil moisture estimation, it is difficult to reach a conclusion whether or not soil moisture estimation from AMSR-E assimilation is superior to both the model simulation and satellite estimates individually. Nevertheless, these results suggest that there is hope that, with additional innovations such as three dimensional EnKF algorithms, AMSR-E remotely sensed soil moisture data will be useful in land data assimilation applications.
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