Preprints
https://doi.org/10.5194/essd-2021-84
https://doi.org/10.5194/essd-2021-84

  16 Mar 2021

16 Mar 2021

Review status: this preprint is currently under review for the journal ESSD.

Development of Observation-based Global Multi-layer Soil Moisture Products for 1970 to 2016

Yaoping Wang1,2, Jiafu Mao2, Mingzhou Jin1,3, Forrest M. Hoffman4, Xiaoying Shi2, Stan D. Wullschleger2, and Yongjiu Dai5 Yaoping Wang et al.
  • 1Institute for a Secure and Sustainable Environment, University of Tennessee, Knoxville, TN, USA 37902
  • 2Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA 37830
  • 3Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, USA 37996
  • 4Computational Sciences and Engineering Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA 37830
  • 5School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China 519082

Abstract. Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling/retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970–2016), multi-layer (0–10, 10–30, 30–50, and 50–100 cm) SM products at monthly 0.5° resolution (available at https://doi.org/10.6084/m9.figshare.13661312.v1) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations and the latest gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Assessed against in situ observations, the global mean bias of the synthesized SM data ranged from −0.044 to 0.033 m3/m3, root mean squared error from 0.076 to 0.104 m3/m3, and Pearson correlation from 0.35 to 0.67. The merged SM datasets also showed the ability to capture historical large-scale drought events and physically plausible global sensitivities to observed meteorological factors. Three of the new SM products, produced by applying any of the three merging methods onto the source datasets excluding the Earth system models, were finally recommended for future applications because of their better performances than the Earth system model–dependent merged estimates. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM 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.

Yaoping Wang et al.

Status: open (until 11 May 2021)

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Yaoping Wang et al.

Data sets

Global Multi-layer Soil Moisture Products Yaoping Wang and Jiafu Mao https://doi.org/10.6084/m9.figshare.13661312.v1

Yaoping Wang et al.

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Short summary
We developed seven global soil moisture datasets (1970–2016, monthly, half degree, and multi-layer) by merging a wide range of data sources, including in situ and satellite observations, reanalysis, offline land surface model simulations, and Earth system model simulations. Given the great value of long-term, multi-layer, gap-free soil moisture products to climate research and applications, we believe this manuscript and presented datasets would be of interest to many different communities.