Abstract
With recent advances in satellite microwave soil moisture estimation, there is a demand for up-to-date validation of satellite soil moisture products. This article presents a sparse network validation over a humid region within the Laurentian Great Lakes basin for five state-of-the-art satellite soil moisture datasets, including the Soil Moisture and Ocean Salinity (SMOS) Level 2 Soil Moisture User Data Product (MIR_SMUDP2) V650, the Soil Moisture Active Passive (SMAP) Enhanced Level 3 Radiometer Soil Moisture (SPL3SMP_E) Version 4, and the European Space Agency Climate Change Initiative (CCI) Soil Moisture v05.2 (containing the Active, Passive, and Combined sets). Unsurprisingly, the five sets of soil moisture products performed differently. With respect to the unbiased root-mean-squared error (ubRMSE), the CCI Combined product performed best (an average ubRMSE of about 0.04 m3 m−3), whereas the CCI Passive had the lowest performance with an average ubRMSE exceeding 0.10 m3 m−3. Overall, in terms of correlation measure, the SMAP and CCI Combined performed better than other products, with the lowest skill from the SMOS product. The SMAP product performed best in the context of the soil moisture anomaly detection, whereas the SMOS and CCI Passive showed the lowest anomaly correlation with the in situ observations. The validation results provide an important guidance for hydrological and meteorological applications involving satellite soil moisture datasets in the study region or other similar areas.
Highlights
A CCURATE estimation of soil water content, which affects the partitioning of energy and water at the land surface, is critically important to understanding variability in the hydrological cycle and in water resource availability
Significant progress has been made in satellite microwave soil moisture detection and estimation
There is a clear seasonal variability of surface soil moisture at the site, which is characterized by dry soils in summer/early autumn and saturated soils in late winter and spring
Summary
A CCURATE estimation of soil water content, which affects the partitioning of energy and water at the land surface, is critically important to understanding variability in the hydrological cycle and in water resource availability. Microwave remote sensing holds the ability to provide spatially distributed surface soil moisture information at multiple scales [1]–[7]. Significant progress has been made in satellite microwave soil moisture detection and estimation. Manuscript received April 27, 2021; revised July 11, 2021; accepted October 18, 2021. Date of publication October 21, 2021; date of current version November 3, 2021.
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