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 #376062

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: Assessing disaggregated SMAP soil moisture products in the United States

Author
item LIU, P.-W. - National Aeronautics And Space Administration (NASA)
item BINDLISH, R. - Goddard Space Flight Center
item FANG, B. - University Of Virginia
item LAKSHMI, V. - University Of Virginia
item O'NEILL, P.E. - Goddard Space Flight Center
item YANG, Z. - US Department Of Agriculture (USDA)
item Cosh, Michael
item BONGIOVQNNI, T. - University Of Florida
item Bosch, David - Dave
item Holifield Collins, Chandra
item Starks, Patrick - Pat
item Prueger, John
item Seyfried, Mark
item Livingston, Stanley

Submitted to: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/25/2020
Publication Date: 2/1/2021
Citation: Liu, P., Bindlish, R., Fang, B., Lakshmi, V., O'Neill, P., Yang, Z., Cosh, M.H., Bongiovqnni, T., Bosch, D.D., Holifield Collins, C.D., Starks, P.J., Prueger, J.H., Seyfried, M.S., Livingston, S.J. 2021. Assessing disaggregated SMAP soil moisture products in the United States. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 14:2577-2592. https://doi.org/10.1109/JSTARS.2021.3056001.
DOI: https://doi.org/10.1109/JSTARS.2021.3056001

Interpretive Summary: Soil moisture products from the Soil Moisture Active Passive (SMAP) satellite mission are regularly produced on a 9km grid, though the actual resolution of the sensor is 33km. By integrating knowledge of land surface temperature into a new algorithm, it is possible to produce a 1km gridded product. This new higher resolution product was compared to in situ networks which were also used to validate the 9km product and similar errors were observed. A comparison of the new algorithm output with a similar 1km product which uses the Sentinel-1 satellite is made. Unlike the Sentinel product, the new product has better temporal coverage, but is subject to data gaps caused by cloud cover. This new algorithm product is one step forward towards development of a management scale soil moisture product from satellites.

Technical Abstract: A soil moisture (SM) disaggregation algorithm based on thermal inertia (TI) theory was implemented to downscale the Soil Moisture Active Passive (SMAP) Enhanced product (SPL2SMP E) from 9 km to 1 km over the continental United States. The algorithm applies land surface temperature and normalized difference vegetation index from Moderate Resolution Imaging Spectroradiometer (MODIS) at higher spatial resolution to estimate relative soil wetness within a coarse SMAP grid - this MODIS-derived relative wetness index is then used to produce the downscaled SMAP SM. Results from the algorithm were evaluated in terms of their spatio-temporal coverage and accuracy using in situ measurements from SMAP Core Validation Sites (CVS), the US Department of Agriculture Soil Climate Analysis Network (USDA-SCAN), and the National Oceanic and Atmospheric Administration Climate Reference Network (NOAA-CRN). Results were also compared with the baseline SPL2SMP E and the SMAP/Sentinel-1 (SPL2SMAP S) 1 km product. Overall, the unbiased root mean square error (ubRMSE) of the disaggregated SM at the CVS using the TI approach is approximately 0.04 m3/m3, which is the SMAP mission requirement for the baseline products. The TI approach outperforms the SMAP/Sentinel SL2SMAP S 1 km product by approximately 0.02 m3/m3. Over the agriculture/crop dominant areas from SCAN and CRN sparse network stations, the TI approach exhibits better ubRMSE compared to SPL2SMP E and SPL2SMAP S by about 0.01 and 0.02 m3/m3, indicating its advantage in these areas. However, a major drawback of this approach is that there are data gaps due to cloud cover as optical sensors cannot have a clear view of the land surface in the presence of clouds.