Impact of SAR-based vegetation attributes on the SMAP high-resolution soil moisture product

Abstract

NASA's Soil Moisture Active Passive (SMAP) mission employs L-band radiometer observations with a spatial resolution of ~33 km to produce global soil moisture products at various spatial resolutions and regular intervals. Among these products is the SMAP-Sentinel active-passive high-resolution soil moisture, available at spatial resolutions of 1 km and 3 km. The SMAP-Sentinel product combines data from the SMAP L-band radiometer and the Copernicus Sentinel-1A and Sentinel-1B C-band Synthetic Aperture Radar (SAR) observations. The SMAP-Sentinel high-resolution soil moisture product enables various hydrological and agricultural applications that were previously beyond the capability of coarser resolution radiometer-only products. However, the assessment of SMAP-Sentinel product over SMAP core validation sites (CVS) has revealed high errors and biases, particularly in agricultural regions. We suspect that the discrepancy may be related to the vegetation attribute parameters (e.g., vegetation optical depth, τ) used in the SMAP-Sentinel baseline Single-Channel Algorithm (SCA) microwave emission model (i.e., τ-ω model) for soil moisture retrievals, which might be out of sync due to reliance on NDVI climatology in the τ estimation. In this study, we leveraged and evaluated the advantage of using vegetation attribute information inherently available through the SMAP ratiometer and Sentinel- 1A/1B SAR backscatter as an alternative to NDVI climatology-derived τ in the SCA to enhance soil moisture retrievals. The SAR cross-polarization (vh) backscatter potentially contains vegetation attribute information that can be correlated with the coarse resolution τ derived from SMAP observations. Therefore, we hypothesized that including Sentinel- 1A/1B vh observations to derive high-resolution (1 km and 3 km) τ, and subsequently incorporating it into the SCA, can significantly improve the accuracy of the SMAP-Sentinel soil moisture product. We established a statistical relationship between Sentinel- 1A/1B SAR backscatter and the SMAP Dual-Channel Algorithm (DCA)-based τ retrieval at ~33 km, for a specific region and different landcover classes at a global extent, using seven years of observations (2015–2022). Further, we developed an algorithm to downscale the SMAP DCA τ of ~33 km to 1 km and 3 km resolutions based on the developed statistical relationships, enabling us to capture the actual temporal variability in vegetation attributes (i.e., τ) at higher resolutions. The downscaled τ values were then incorporated into the SCA to retrieve SMAP-Sentinel high-resolution (1 km and 3 km) soil moisture. The validation analysis of high-resolution soil moisture retrievals demonstrates that the inclusion of the downscaled τ approach significantly improved the performance of the SMAP-Sentinel product by reducing both bias and error standard deviation compared to the retrievals based on τ derived from the NDVI climatology.