An intercomparison study of algorithms for downscaling SMAP radiometer soil moisture retrievals

Authors: FANG, L. - Collaborator; ZHAN, X. - Collaborator; YIN, J. - Collaborator; SCHULL, M.A. - University Of Maryland; WALKER, J. - Monash University; WEN, J. - Chengdu University; Cosh, Michael; LAKANKAR, T. - City University Of New York; Holifield Collins, Chandra; Bosch, David - Dave; Starks, Patrick; CALDWELL, T. - University Of Texas

Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/4/2020
Publication Date: 8/1/2020
Citation: Fang, L., Zhan, X., Yin, J., Schull, M., Walker, J., Wen, J., Cosh, M.H., Lakankar, T., Holifield Collins, C.D., Bosch, D.D., Starks, P.J., Caldwell, T. 2020. An intercomparison study of algorithms for downscaling SMAP radiometer soil moisture retrievals. Journal of Hydrometeorology. 21(8):1761-1775. https://doi.org/10.1175/JHM-D-19-0034.1.
DOI: https://doi.org/10.1175/JHM-D-19-0034.1

Interpretive Summary: Downscaling is the technique of improving the resolution of a satellite product by using additional information at the earth’s surface. There are many different methods, depending on the land surface parameter in question. For surface soil moisture, variables such as vegetation density or land surface temperature are the most logical downscaling parameters. Results from three different downscaling methods are compared using data from the Soil Moisture Active Passive Mission (SMAP) and data from two other U.S. satellites: land surface temperature from the Geostationary Operational Environmental Satellite (GOES) and a vegetation index from the Moderate Resolution Imaging Spectroradiometer (MODIS). Calibration and validation is conducted against dense soil moisture networks deployed around the world. Both 9km and 1 km resolutions are analyzed and the regression tree algorithm outperformed the others and demonstrated the most promise for operational use in weather and climate modeling.

Technical Abstract: In the past decade, a variety of algorithms have been introduced to downscale passive microwave soil moisture observations. Some exploit the soil moisture information from optical/thermal sensing of land surface temperature (LST) and vegetation dynamics while others use active microwave (radar) observations. In this study, downscaled soil moisture data at 9km or 1km resolution from several algorithms are inter-compared against in situ soil moisture measurements to determine their reliability in an operational system. The fine scale satellite data used here for downscaling the coarse scale SMAP data are observations of LST from the Geostationary Operational Environmental Satellite (GOES) and vegetation index (VI) from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) for the warm seasons in 2015 and 2016. Results from three downscaling algorithms using these data were analyzed, being NASA SMAP enhanced 9km SM product algorithm, a simple regression algorithm based on 9km thermal inertial data, and a data mining approach called regression tree based on 9km and 1km LST and/or VI. Totally eight sets of in situ soil moisture data from intensive networks were used for validation, including 1) the CREST-SMART network in Millbrook, NY, 2) Walnut Gulch Watershed in Arizona, 3) Little Washita Watershed in Oklahoma, 4) Fort Cobb Reservoir experimental watersheds in Oklahoma, 5) Little River Watershed in Georgia, 6) Texas Soil Observation Network (TxSON), 7) the Tibetan Plateau network in China, and 8) the OzNet in Australia. Soil moisture measurements of the in situ networks were up-scaled to the corresponding SMAP reference pixels at 9km and used to assess the performance of the downscaling algorithms. Results revealed that the downscaled 9km soil moisture products generally outperform the 36km product for most in situ data sets. The linear regression algorithm using the thermal sensing based evaporative stress index (ESI) had the best agreement with the in situ measurements from networks in the Contiguous U.S. (CONUS). The linear regression method reduced unbiased root mean square error (ubRMSE) by 0.037 m3m-3 and strengthened the correlation by 0.345 for the validation against the CREST-SMART network. The disaggregated 1km soil moisture product from the regression tree algorithm demonstrated the lowest ubRMSE when comparing to the matched-up in situ data. In general, the regression tree algorithm performed the best for most validation sites and demonstrated promise for operational generation of fine resolution soil moisture data product.