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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 #350318

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: Downscaling of SMAP soil moisture using land surface temperature and vegetation data

item FANG, BIN - University Of South Carolina
item LASKSHMI, V. - University Of South Carolina
item BINDLISH, R. - Goddard Space Flight Center
item Jackson, Thomas

Submitted to: Vadose Zone Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/12/2018
Publication Date: 9/9/2018
Citation: Fang, B., Laskshmi, V., Bindlish, R., Jackson, T.J. 2018. Downscaling of SMAP soil moisture using land surface temperature and vegetation data. Vadose Zone Journal. 17:170198.

Interpretive Summary: A soil moisture downscaling algorithm was applied to the Soil Moisture Active Passive (SMAP) Enhanced 9 km daily soil moisture product and validated using in situ soil moisture measurements and aircraft observations from the SMAP Validation Experiment 2015 (SMAPVEX15) field campaign in Arizona. Downscaling methods attempt to extract high resolution soil moisture estimates from existing passive microwave soil moisture products using additional information obtained at higher resolution. The algorithm used here was developed based on a vegetation-modulated daytime soil moisture – daily surface temperature change relationship. The assessment metrics for the 1 km downscaled SMAP soil moisture showed better overall consistency with the aircraft-based soil moisture than with the 9 km SMAP soil moisture. Based on these results, it is expected that the downscaling approach can be applied to the entire contiguous United States.

Technical Abstract: Remotely sensed soil moisture retrieved by the Soil Moisture Active and Passive (SMAP) sensor is currently provided at a 9km grid resolution. However, many research and applications may require higher spatial resolution data. In this investigation, a passive microwave soil moisture downscaling algorithm based on thermal inertia theory was refined and implemented for use with SMAP. This algorithm utilizes a NDVI (Normalized Difference Vegetation Index) modulated relationship between daytime soil moisture and daily temperature change, which is modeled using land surface model output variables from NLDAS (North America Land Data Assimilation System), and remote sensing data from MODIS (Moderate-Resolution Imaging Spectroradiometer) and AVHRR (Advanced Very High Resolution Radiometer). The reference component of the algorithm was developed at the NLDAS grid size (12.5 km) and used to downscale the SMAP Level 3 radiometer-based 9 km soil moisture. The downscaled results were validated using a unique data set acquired in SMAPVEX15 (Soil Moisture Active Passive Validation Experiment 2015) that included in situ soil moisture measurements as well as PALS (Passive Active L-band Sensor) airborne soil moisture retrievals. The overall metrics of the downscaled SMAP soil moisture validation (root mean square error, bias, and correlation) indicated good accuracy. The downscaled SMAP estimates better characterized the spatial and temporal variabilities of soil moisture at watershed scales (Walnut Gulch Watershed) than the SMAP soil moisture product. Additionally, the overall accuracy of downscaled SMAP soil moisture is comparable to the high spatial resolution soil moisture from PALS which was acquired from SMAPVEX15 campaign.