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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 #329537
Title: Active–passive soil moisture retrievals during the SMAP validation experiment 2012

Author
item LEROUX, D. - Collaborator
item DAS, N. - Jet Propulsion Laboratory
item ENTEKHABI, DARA - Collaborator
item COLLIANDER, ANDREAS - Jet Propulsion Laboratory
item NJOKU, ENI - Jet Propulsion Laboratory
item Jackson, Thomas
item YUEH, S. - Jet Propulsion Laboratory

Submitted to: Geoscience and Remote Sensing Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/2/2016
Publication Date: 9/1/2017
Publication URL: http://handle.nal.usda.gov/10113/5729143
Citation: Leroux, D., Das, N., Entekhabi, D., Colliander, A., Njoku, E., Jackson, T.J., Yueh, S. 2017. Active–passive soil moisture retrievals during the SMAP validation experiment 2012. Geoscience and Remote Sensing Letters. 13:475-479.

Interpretive Summary: Field experiment observations were used to test the key disaggregation algorithm that would be used with the Soil Moisture Active Passive (SMAP) satellite. The algorithm uses high-resolution radar information to disaggregate the coarser resolution radiometer observations. This particular campaign covered a larger range of vegetation and soil moisture conditions than previous aircraft campaigns.. The experiment domain was mapped several times with airborne radiometer and radar instruments, while in situ samples were taken on the ground. Soil moisture retrievals over areas with different crop land covers show improved soil moisture estimates, when radar information is used in conjunction with the radiometer measurements. Based on the campaign data, this study provides valuable insights into the SMAP active–passive soil moisture algorithm development and guides topics that are in need of more focused attention.

Technical Abstract: The goal of this study is to assess the performance of the active–passive algorithm for the NASA Soil Moisture Active Passive mission (SMAP) using airborne and ground observations from a field campaign. The SMAP active–passive algorithm disaggregates the coarse-resolution radiometer brightness temperature (T B) using high-resolution radar backscatter (so ) observations. The colocated TB and so acquired by the aircraft-based Passive Active L- and S-band sensor during the SMAP Validation Experiment 2012 (SMAPVEX12) are used to evaluate this algorithm. The estimation of its parameters is affected by changes in vegetation during the campaign. Key features of the campaign were the wide range of vegetation growth and soil moisture conditions during the experiment period. The algorithm performance is evaluated by comparing retrieved soil moisture from the disaggregated brightness temperatures to in situ soil moisture measurements. A minimum performance algorithm is also applied, where the radar data are withheld. The minimum performance algorithm serves as a benchmark to assess the value of the radar to the SMAP active–passive algorithm. The temporal correlation between ground samples and the SMAP active–passive algorithm is improved by 21% relative to minimum performance. The unbiased root-mean-square error is decreased by 15% overall.