Active-Passive Soil Moisture Retrievals During the SMAP Validation Experiment 2012

• Published in: IEEE geoscience and remote sensing letters Vol. 13; no. 4; pp. 475 - 479
• Main Authors: Leroux, Delphine J., Das, Narendra N., Entekhabi, Dara, Colliander, Andreas, Njoku, Eni, Jackson, Thomas J., Yueh, Simon
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-04-2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Active-passive; Algorithms; Brightness temperature; brightness temperature disaggregation; Ground-based observation; Radar; Radar data; Radiometry; SMAP; Soil measurements; Soil moisture; Temperature measurement; Vegetation; Vegetation growth; brightness temperature disaggregation; Active–passive; soil moisture; SMAP
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2015.2491643

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 (TB) using high-resolution radar backscatter (σ o ) observations. The colocated TB and σ o acquired by the aircraft-based Passive Active Land 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 asses 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.