Optimal model-based temperature inputs for global soil moisture and vegetation optical depth retrievals from SMAP

Abstract

The accuracy of global L-band soil moisture (SM) and vegetation optical depth (L-VOD) products retrieved through the τ-ω model is highly dependent on temperature inputs obtained from model-based temperature products. However, the performance of these temperature products in the retrieval of global-scale SM and L-VOD has not yet been evaluated. Therefore, this study aimed to evaluate four commonly used model-based temperature products as input to the SMAP-INRAE-BORDEAUX (SMAP-IB) algorithm for retrieving SM and L-VOD. Specifically, we investigated differences in SMAP-IB retrievals of SM and L-VOD using four model-based temperature sources as input, along with four configurations concerning the parameterization of effective soil (TG) and vegetation (TC) temperatures. Triple collocation analysis (TCA) results showed that SM retrievals based on GLDAS temperatures (SMGLDAS), with TC set to skin temperature and TG calculated from shallow soil temperatures at layers 1 (0–10 cm) and 2 (10–40 cm), led to the highest global median TCA correlation (TCA-R) value of 0.780. In particular, SMGLDAS achieved the highest TCA-R values over 34.94% of global pixels, predominantly in forested areas. Comparison with in situ measurements also showed improved regional performance of SMGLDAS. In contrast, SM retrievals using MERRA2 temperature inputs, employing the same configurations for TC but different soil temperature layers (1 (0–10 cm) and 4 (40–80 cm)) for TG, yielded the lowest TCA-R value of 0.755. Overall, using the GLDAS temperature products as inputs to the retrieval algorithm resulted in the best performance for both SM and L-VOD retrievals. These new findings are valuable for selecting optimal model-based temperature datasets as inputs to the development of future satellite mission algorithms.