Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #381100
Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: High-resolution soil moisture maps over landslide regions in northern California grassland derived from SAR backscattering coefficients

Author
item LIAO, T.H. - National Aeronautics And Space Administration (NASA)
item KIM, S. - Jet Propulsion Laboratory
item HANDWERGER, A. - University Of California (UCLA)
item FIELDING, E. - National Aeronautics And Space Administration (NASA)
item Cosh, Michael
item SCHULZ, W. - Us Geological Survey (USGS)

Submitted to: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/9/2021
Publication Date: 3/25/2021
Citation: Liao, T., Kim, S., Handwerger, A., Fielding, E., Cosh, M.H., Schulz, W. 2021. High-resolution soil moisture maps over landslide regions in northern California grassland derived from SAR backscattering coefficients. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 14:4547-4560. https://doi.org/10.1109/JSTARS.2021.3069010.
DOI: https://doi.org/10.1109/JSTARS.2021.3069010

Interpretive Summary: High resolution remote sensing allows for increased monitoring and modeling of the land surface for important land surface parameters, such as soil moisture. In anticipation of satellite based remote sensing, a physically based scattering algorithm is tested to estimate soil moisture using L-band radar data from an aircraft-based instrument over a grassland region in northern California. A reasonable error was attained for this model and will be useful for future algorithm development in grasslands and rangelands similar to this ecosystem.

Technical Abstract: Slow-moving landslides are destabilized by accumulated precipitation and consequent soil moisture. Yet the continuous high-resolution soil moisture measurements needed to aid understanding of landslide processes are generally absent in steep terrain. Here we produce soil moisture time-series maps for a seasonally active grassland landslide in the northern California Coast Ranges, USA using backscattering coefficients from NASA’s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) at 6-m resolution. A physically based radar scattering model is used to retrieve the near-surface (5-cm depth) soil moisture for the landslide. Both forward modelling (backscattering estimation) and the retrieval (soil moisture validation) show good agreement. The root mean square errors (RMSE) for VV and HH polarizations in forward model comparison are 1.93 dB and 1.88 dB, respectively. The soil moisture retrieval shows unbiased RMSE (ubRMSE) of 0.054 m^3/m^3. Our successful retrieval benefits from surface and double-bounce scattering, which is common in grasslands. The retrieved maps show saturated wetness conditions within the active landslide boundaries. We also performed sensitivity tests for incidence angle and found that the retrieval is weakly dependent on the angle especially while using HH and VV together. Using the two copol inputs, the retrieval is also not sensitive to the change of orientation angles of grass cylinders. The physical model inversion presented here can be generally applied for soil moisture retrieval in areas with the same vegetation cover types in California.