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The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m3 m−3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m3 m−3 (0.030 m3 m−3) at the 9-km scale and 0.035 m3 m−3 (0.026 m3 m−3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m3 m−3 (0.032 m3 m−3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.

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Abstract:The objective of this study was to validate the soil moisture data derived from coarse-resolution active microwave data (50 km) from the ERS scatterometer. The retrieval technique is based on a change detection method coupled with a data-based modelling approach to account for seasonal vegetation dynamics. The technique is able to derive information about the soil moisture content corresponding to the degree of saturation of the topmost soil layer (¾5 cm). To estimate profile soil moisture contents down to 100 cm depth from the scatterometer data, a simple two-layer water balance model is used, which generates a red noise-like soil moisture spectrum. The retrieval technique had been successfully applied in the Ukraine in a previous study. In this paper, the performance of the model in a semi-arid Mediterranean environment characterized by low annual precipitation (400 mm), hot dry summers and sandy soils is investigated. To this end, field measurements from the REMEDHUS soil moisture station network in the semi-arid parts of the Duero Basin (Spain) were used. The results reveal a significant coefficient of determination R 2 D 0Ð75 for the averaged 0-100 cm soil moisture profile and a root mean square error (RMSE) of 2Ð2 vol%. The spatial arrangement of the REMEDHUS soil moisture stations also allowed us to study the influence of the small-scale variability of soil moisture within the ERS scatterometer footprint. The results show that the small-scale variability in the study area is modest and can be explained in terms of texture fraction distribution in the soil profiles.

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