Seven global, gap-free, long-term (1970–2016), multilayer (0–10, 10–30, 30–50, and 50–100 cm) soil moisture products at monthly 0.5∘ resolution were produced by synthesizing a wide range of soil moisture datasets using different statistical methods.
These hybrid products create added value over existing soil moisture datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management.
Soil moisture datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of soil moisture data have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling and retrieval assumptions. We produced seven global, gap-free, long-term, multilayer soil moisture products by synthesizing a wide range of soil moisture datasets using three statistical methods. The merged products outperformed their source datasets when evaluated with in situ observations and multiple gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. The merged products generally showed reasonable temporal homogeneity and physically plausible global sensitivities to observed meteorological factors. Based on these evaluation results, the three products, produced by applying any of the three merging methods to the source datasets excluding the Earth System Models, were finally recommended for future applications.