Assessing Disaggregated SMAP Soil Moisture Products in the United States

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 gridthis 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 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 m 3 /m 3 , 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 m 3 /m 3 . Over the agriculture/crop 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 m 3 /m 3 , 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.

A High‐Resolution Land Data Assimilation System Optimized for the Western United States

“…2020; Liu et al. 2021, in revision), and Gravity Recovery and Climate Experiment (GRACE; Tapley et al. 2004) and GRACE Follow On terrestrial water storage (TWS) will be included.…”

A Novel Fusion Method for Generating Surface Soil Moisture Data With High Accuracy, High Spatial Resolution, and High Spatio‐Temporal Continuity

“…However, cloud contamination and atmospheric pollution usually prevent producing spatio‐temporally continuous remotely sensed SSM (Cheng et al., 2014; P.‐W. Liu et al., 2021; Jamshidi et al., 2019).…”

Thermal Hydraulic Disaggregation of SMAP Soil Moisture Over the Continental United States

“…Then, SM is retrieved from fine scale TB to provide the SMAP/Sentinel-1 Level 2 SM product (SPL2SMAP_S) at 3 and 1 km [34], [35]. However, the temporal availability of this data product is reduced by ~70% due to Sentinel-1's narrower swath and less frequent (6-12 days) satellite revisits compared to SMAP [47]. In contrast, approaches using optical/thermal sensors were developed based upon the triangle method, which correlates a tripartite relationship between land surface temperature (LST), vegetation content, and SM [48], [49].…”