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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 #311908

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

Location: Hydrology and Remote Sensing Laboratory

Title: Estimating effective roughness parameters of the L-MEB model for soil moisture retrieval using passive microwave observations from SMAPVEX12

item MARTENS, B. - Collaborator
item LIEVENS, H. - Collaborator
item COLLIANDER, ANDREAS - Jet Propulsion Laboratory
item Jackson, Thomas
item VERHOEST, N. - Collaborator

Submitted to: IEEE Transactions on Geoscience and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/18/2015
Publication Date: 7/1/2015
Publication URL:
Citation: Martens, B., Lievens, H., Colliander, A., Jackson, T.J., Verhoest, N. 2015. Estimating effective roughness parameters of the L-MEB model for soil moisture retrieval using passive microwave observations from SMAPVEX12. IEEE Transactions on Geoscience and Remote Sensing. 63:4091-4203.

Interpretive Summary: An effective roughness model was developed and validated using a data set collected during a a campaign field conducted in Canada. Although numerous attempts have been made to improve the quality of soil moisture estimates, the retrievals are still hampered by the problem of modeling soil roughness at large scale. Results indicated that the retrieved roughness parameters represent the effects of surface roughness and also compensate for the shortcomings in the simple vegetation model employed in the approach. As a consequence, the resulting effective roughness formulation models both effects. These results should be useful for the improvement of soil moisture retrieval algorithms for future satellite missions that will provide soil moisture information for agricultural hydrology.

Technical Abstract: Although there have been efforts to improve existing soil moisture retrieval algorithms, the ability to estimate soil moisture from passive microwave observations is still hampered by problems in accurately modeling the observed microwave signal. This paper focuses on the estimation of effective surface roughness parameters of the L-band Microwave Emission from the Biosphere (L-MEB) model in order to improve soil moisture retrievals from passive microwave observations. Data from the SMAP Validation Experiment 2012 (SMAPVEX12) conducted in Canada are used to develop and validate a simple model for the estimation of effective roughness parameters. Results presented in this paper show that the L-MEB roughness parameters can be empirically related to the observed brightness temperatures and the Leaf Area Index (LAI) of the vegetation. It is also shown using a leave-one-out cross validation that the model is able to accurately estimate the roughness parameters necessary for the inversion of the L-MEB model. In order to demonstrate the benefits of the roughness parameterization, the performance of the model is compared to a widely-used roughness formulation where effective roughness parameters are linearly related to the observed surface soil moisture. Results indicate that the soil moisture retrieval error can be reduced.