Abstract
A comprehensive evaluation of the performance of satellite-based soil moisture (SM) retrievals is undoubtedly very important to improve its quality and evaluate its potential application in hydrology, climate, and natural disasters (drought, flood, etc.). Since the release of the SMAP (Soil Moisture Active Passive) mission data in April 2015, the associated SM retrieval algorithms have developed rapidly, and their improvement work is still in progress. However, some newly developed SM retrievals have not been fully assessed and inter-compared. One such product is the new multi-temporal dual-channel retrieval algorithm (MT-DCA) SM retrievals, which was recently retrieved using the so-called MT-DCA algorithm. To solve this, we aim to assess the MT-DCA SM retrievals along with the SMAP-enhanced level three SM products (SPL3SMP_E, version 2). More specifically, in this paper we evaluated and inter-compared the two SMAP SM retrievals with the ECMWF (European Centre for Medium-Range Weather Forecasts) modeled SM and ISMN (International Soil Moisture Network) in situ observations by applying four statistical scores: Pearson correlation coefficient (R), root mean square difference (RMSD), bias, and unbiased RMSD (ubRMSD). It was found that both SMAP SM retrievals can better capture the seasonal variations of ECMWF-modeled SM and ground-based measurements according to correlations, and MT-DCA SM was drier than SPL3SMP_E SM by ~0.018 m3/m3 on average on a global scale. With respect to the ISMN ground-based measurements, the performance of SPL3SMP_E SM compared better than the MT-DCA SM. The median ubRMSD of SPL3SMP_E SM and MT-DCA SM with ground measurements computed over all selected ISMN sites were 0.058 m3/m3 and 0.070 m3/m3, respectively.
Highlights
Surface soil moisture (SM) is a key state variable of the hydrological cycle and land-surface/atmosphere interactions [1,2,3,4,5]
Four metrics, which are widely used in the soil moisture community [45,47], were used to compare the SMAP SM retrievals with the reference data: (Pearson) correlation coefficient (R; Equation 1) to assess the performance of SMAP retrievals to capture the seasonal variations of the reference SMs, bias (m3/m3; Equation 2) to measure the wetness or dryness of the SMAP SM compared to the reference SMs, root mean square difference (RMSD; m3/m3; Equation 3), and the unbiased RMSD (ubRMSD) (m3/m3; Equation 4) [18]
The results presented above can lead to several main conclusions: (i) Both SPL3SMP_E and multi-temporal dual-channel retrieval algorithm (MT-DCA) SM retrievals are generally found to be drier than European Centre for Medium-Range Weather Forecasts” (ECMWF) modeled SM, while MT-DCA SM was found to be drier than SPL3SMP_E SM by ~0.018 m3/m3 on average on a global scale
Summary
Surface soil moisture (SM) is a key state variable of the hydrological cycle and land-surface/atmosphere interactions [1,2,3,4,5]. SM controls evaporation, water balance, and profoundly affects the partitioning of land surface energy [6,7,8,9,10] All of this relevance makes SM known as one of the “Essential Climate Variables” [11]. The most recent space-borne mission using this technology to monitor SM was NASA’s SMAP (NASA: National Aeronautics and Space Administration; SMAP: Soil Moisture Active Passive) launched in 2015 [18]. This mission concept was to obtain a set of SM products with a spatial resolution of 9 km, which is achieved by combining the higher spatial resolution of radar measurements with the higher sensitivity to SM of radiometer measurements. Since the end of 2016, the SMAP team has introduced a series of new SM products with the aim of compensating for the loss of high-resolution measurement capabilities due to radar failures, including both level two and level three SMAP-Enhanced Passive Soil Moisture product and SMAP/Sentinel-1 Active-Passive Soil Moisture Product, etc
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