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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 #342298

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: Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates

item LIEVENS, H. - Ghent University
item REICHLE, R - Goddard Space Flight Center
item LIU, Q - Goddard Space Flight Center
item DE LANNOY - Goddard Space Flight Center
item DUNBAR, R.S. - Jet Propulsion Laboratory
item KIM, S. - Jet Propulsion Laboratory
item DAS, N. - Jet Propulsion Laboratory
item Cosh, Michael
item WALKER, J. - Monash University
item WAGNER, W. - Vienna University Of Technology

Submitted to: Geophysical Research Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/30/2017
Publication Date: 6/28/2018
Citation: Lievens, H., Reichle, R., Liu, Q., De Lannoy, Dunbar, R., Kim, S., Das, N., Cosh, M.H., Walker, J., Wagner, W. 2018. Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates. Geophysical Research Letters. 44(12):6145-6153.

Interpretive Summary: The Soil Moisture Active Passive (SMAP) mission is currently producing a 9-km resolution root zone soil moisture product. This product was improved by the incorporation of an additional stream of data from the Sentinel-1 satellite which also monitors the microwave spectrum. This demonstrates the potential for combining both active and passive microwave products into a unified soil moisture product. This research will provide direction and concurrence with other combined products. Ultimately, this will improve the quality and resolution of data provided to agricultural and watershed managers.

Technical Abstract: SMAP (Soil Moisture Active and Passive) radiometer observations at 40 km resolution are routinely assimilated into the NASA Catchment Land Surface Model to generate the 9-km SMAP Level-4 Soil Moisture product. This study demonstrates that adding high-resolution radar observations from Sentinel-1 to the SMAP assimilation can increase the spatio-temporal accuracy of soil moisture estimates. Radar observations were assimilated either separately from or simultaneously with radiometer observations. Assimilation impact was assessed by comparing 3-hourly, 9-km surface and root-zone soil moisture simulations with in situ measurements from 9-km SMAP core validation sites and sparse networks, from May 2015 to December 2016. The Sentinel-1 assimilation consistently improved surface soil moisture, whereas root-zone impacts were mostly neutral. Relatively larger improvements were obtained from SMAP assimilation. The joint assimilation of SMAP and Sentinel-1 observations performed best, demonstrating the complementary value of radar and radiometer observations.