Assessing Disaggregated SMAP Soil Moisture Products in the United States

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<inline-formula><tex-math notation="LaTeX">$\_$</tex-math></inline-formula>E) from 9 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 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 <italic>in situ</italic> measurements from SMAP core validation sites (CVS), the U.S. Department of Agriculture Soil Climate Analysis Network (SCAN), and the National Oceanic and Atmospheric Administration Climate Reference Network (CRN). Results were also compared with the baseline SPL2SMP<inline-formula><tex-math notation="LaTeX">$\_$</tex-math></inline-formula>E and the SMAP/Sentinel-1 (SPL2SMAP<inline-formula><tex-math notation="LaTeX">$\_$</tex-math></inline-formula>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 <inline-formula><tex-math notation="LaTeX">$\text{m}^3/\text{m}^3$</tex-math></inline-formula>, which is the SMAP mission requirement for the baseline products. The TI approach outperforms the SMAP/Sentinel SL2SMAP<inline-formula><tex-math notation="LaTeX">$\_$</tex-math></inline-formula>S 1 km product by approximately 0.02 <inline-formula><tex-math notation="LaTeX">$\text{m}^3/\text{m}^3$</tex-math></inline-formula>. Over the agriculture/crop areas from SCAN and CRN sparse network stations, the TI approach exhibits better ubRMSE compared to SPL2SMP<inline-formula><tex-math notation="LaTeX">$\_$</tex-math></inline-formula>E and SPL2SMAP<inline-formula><tex-math notation="LaTeX">$\_$</tex-math></inline-formula>S by about 0.01 and 0.02 <inline-formula><tex-math notation="LaTeX">$\text{m}^3/\text{m}^3$</tex-math></inline-formula>, indicating its advantage in these areas. However, a 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.