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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 #323267

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: Assimilation of active and passive microwave observations for improved estimates of soil moisture and crop growth

Author
item LIU, P. - University Of Florida
item BONGIOVQNNI, T. - University Of Florida
item MONSIVAIS-HUERTERO, A. - University Of Florida
item JUDGE, J. - University Of Florida
item STEELE-DUNNE, SUSAN - Delft University
item BINDLISH, R. - Science Systems, Inc
item Jackson, Thomas

Submitted to: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/10/2016
Publication Date: 4/1/2016
Publication URL: http://handle.nal.usda.gov/10113/5729144
Citation: Liu, P., Bongiovqnni, T., Monsivais-Huertero, A., Judge, J., Steele-Dunne, S., Bindlish, R., Jackson, T.J. 2016. Assimilation of active and passive microwave observations for improved estimates of soil moisture and crop growth. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 9(4):1357-1369.

Interpretive Summary: A data assimilation framework that is capable of incorporating complementarities of active and passive microwave observations was developed and implemented for updating soil moisture at two layers and vegetation biomass through a growing season of soybean. Overall, estimates of soil moisture, dry biomass, and leaf area index using the data assimilation framework were significantly improved in the synthetic experiments. Accurate estimates of crop development over a growing season are important for managing agricultural production and assessing food security. Assimilating soil moisture or remotely sensed observations that are sensitive to soil moisture in a dynamic crop growth model can improve the estimation of crop growth parameters over the growing season, and thus, the prediction of crop yield.

Technical Abstract: An Ensemble Kalman Filter-based data assimilation framework that links a crop growth model with active and passive (AP) microwave models was developed to improve estimates of soil moisture (SM) and vegetation biomass over a growing season of soybean. Complementarities in AP observations were incorporated in the framework, where the active observations were used to optimize surface roughness and update vegetation biomass, while passive observations were used to update SM. The framework was implemented in a rain-fed agricultural region of the southern La-Plata Basin during the 2011-2012 growing season, through a synthetic experiment and AP observations from the Aquarius mission. The synthetic experiment was conducted at a temporal resolution of 3- and 7-days to match the current AP missions. The assimilated estimates of SM in the root zone and dry biomass were improved compared to those from the cases without assimilation, during both 3- and 7-day assimilation scenarios. Particularly, the 3-day assimilation provided the best estimates of SM in the near surface and dry biomass with reductions in RMSEs of 41 % and 42 %, respectively. The absolute differences of assimilated LAI from Aquarius were 0.29 compared to the MODIS LAI indicating that the performance of assimilation was similar to the MODIS product at a regional scale. This study demonstrates the potential of assimilation using AP observations at high temporal resolution such as those from SMAP for improved estimates of SM and vegetation parameters.