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Enhancing the European Space Agency’s Climate Change Initiative Soil Moisture Product over China

  • Authors
    Wang Y.Corresponding
    Zuo T.
    Xing F.
    Hu Y.
    Cui T.
    Wu J.
    Shen X.
    Wang S.
    Xiao Q.
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  • gnest_05844_final.pdf
  • Paper ID
    gnest_05844
  • Paper status
    Published
  • Date paper submitted
  • Date paper accepted
  • Date paper online
DOI
https://doi.org/10.30955/gnj.05844
Graphical abstract
Abstract

Soil moisture heavily influences the energy exchange between land and the atmosphere, and it plays an important role in ecological systems. Quantitatively acquiring soil moisture information is important for agricultural production, ecological protection and other processes. The current range of soil moisture data products is diverse, but how to enhance the applicability and accuracy of these products in China through data fusion is a question worth exploring. As a new, merged soil moisture product, ECV_SM was initially developed under the European Space Agency’s (ESA’s) Water Cycle Multi-Mission Observation Strategy (WACMOS) project and is currently being extended and improved within the ESA’s Climate Change Initiative (CCI). In this study, an empirical model is suggested to improve the performance of ECV_SM over China. First, the study area was divided into seven sub-areas using digital elevation model (DEM), land surface roughness (ROUGHNESS) and vegetation optical depth (VOD). Then, nine impact factors (DEM, ROUGHNESS, VOD, antecedent precipitation index, slope, aspect, sand content, clay content and ECV_SM) and in-situ soil moisture data were used to build an empirical soil moisture estimation model for each sub-area. In total, 70% of the in-situ soil moisture data was used for modeling and 30% was used for validation. The validation results indicate that the BIAS, root-mean-square difference (RMSD) and mean relative error (MRE) improved from 0.078 cm3/cm3 to 0.062 cm3/cm3, from 0.099 cm3/cm3 to 0.078 cm3/cm3, and from 30.0% to 22.6%, respectively. The spatial distribution of the improved dataset is also consistent with the actual conditions. The approach optimizes the ECV_SM product; therefore, the approach is efficient. The results of this study have successfully improved the accuracy of existing data products in China and enhanced the efficiency of data fusion. This has significant implications for the impact of soil moisture products on the regional ecological environment and agricultural production in China.

Keywords
Soil moisture Performance improvement Partition Impact factors Multiple linear stepwise-regression estimation model China Essential Climate Variable
Cite this article
Wang, Y. et al. (2024) “Enhancing the European Space Agency’s Climate Change Initiative Soil Moisture Product over China”, Global NEST Journal, 26(6). Available at: https://doi.org/10.30955/gnj.05844.
PDF file
  • gnest_05844_final.pdf