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IEEE Trans Geosci Remote Sens. 2017 Jan 19;Volume 55(Iss 4):1897-1914. doi: 10.1109/TGRS.2016.2631126.

Surface Soil Moisture Retrieval Using the L-Band Synthetic Aperture Radar Onboard the Soil Moisture Active-Passive Satellite and Evaluation at Core Validation Sites.

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Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109 USA.
The Ohio State University, Columbus, OH 43212 USA.
University of Southern California, Los Angeles, CA 90089 USA.
University of Michigan, Ann Arbor, MI 48109 USA.
Hydrology and Remote Sensing Laboratory, USDA ARS, Beltsville, MD 20705 USA.
University of Guelph, Guelph, ON N1G 2W1, Canada.
University of Texas-Austin, Austin, TX 78713 USA.
Southwest Watershed Research Center, USDA ARS, USA.
National Soil Erosion Research Laboratory, USDA ARS, West Lafayette, IN 47907 USA.
University of Valencia, 46100 Valencia, Spain.
Comisión Nacional de Actividades Espaciales, Buenos Aires, Argentina.
Monash University, Melbourne, VIC 3800, Australia.
Massachusetts Institute of Technology, Cambridge, MA 02139 USA.
NASA Goddard Space Flight Center, Greenbelt, MD 20771 USA.


This paper evaluates the retrieval of soil moisture in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active-Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid-April to early July, 2015. Surface soil moisture retrievals using radar observations have been challenging in the past due to complicating factors of surface roughness and vegetation scattering. Here, physically based forward models of radar scattering for individual vegetation types are inverted using a time-series approach to retrieve soil moisture while correcting for the effects of static roughness and dynamic vegetation. Compared with the past studies in homogeneous field scales, this paper performs a stringent test with the satellite data in the presence of terrain slope, subpixel heterogeneity, and vegetation growth. The retrieval process also addresses any deficiencies in the forward model by removing any time-averaged bias between model and observations and by adjusting the strength of vegetation contributions. The retrievals are assessed at 14 core validation sites representing a wide range of global soil and vegetation conditions over grass, pasture, shrub, woody savanna, corn, wheat, and soybean fields. The predictions of the forward models used agree with SMAP measurements to within 0.5 dB unbiased-root-mean-square error (ubRMSE) and -0.05 dB (bias) for both copolarizations. Soil moisture retrievals have an accuracy of 0.052 m3/m3 ubRMSE, -0.015 m3/m3 bias, and a correlation of 0.50, compared to in situ measurements, thus meeting the accuracy target of 0.06 m3/m3 ubRMSE. The successful retrieval demonstrates the feasibility of a physically based time series retrieval with L-band SAR data for characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation.


Soil moisture; synthetic aperture radar (SAR); vegetation

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