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An Improved Soil Moisture Downscaling Method Based on Soil Properties and Geographical Divisions over the Loess Plateau

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Listed:
  • Lei Han

    (School of Land Engineering, Chang’an University, Xi’an 710054, China
    State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China)

  • Zheyuan Miao

    (School of Land Engineering, Chang’an University, Xi’an 710054, China)

  • Zhao Liu

    (School of Land Engineering, Chang’an University, Xi’an 710054, China)

  • Hongliang Kang

    (School of Land Engineering, Chang’an University, Xi’an 710054, China)

  • Han Zhang

    (School of Land Engineering, Chang’an University, Xi’an 710054, China)

  • Shaoan Gan

    (School of Land Engineering, Chang’an University, Xi’an 710054, China)

  • Yuxuan Ren

    (School of Land Engineering, Chang’an University, Xi’an 710054, China)

  • Guiming Hu

    (School of Land Engineering, Chang’an University, Xi’an 710054, China)

Abstract

As the contradiction between vegetation growth and soil moisture (SM) demand in arid zones gradually expands, accurately obtaining SM data is crucial for ecological construction. Remote sensing products limit small-scale studies due to the low resolution, and the emergence of downscaling solves this problem. This study proposes an improved semi-physical SM downscaling method. The effects of environmental factors on SM in different geographical zones (Windy Sand Hills, Flood Plains, Loess Yuan, Hilly Loess, Earth-rock Hills and Rocky Mountain) were analyzed using Random Forests. Vegetation and topographic factors were incorporated into the traditional downscaling algorithm based on the Mualem–van Genuchten model by setting weights, yielding 250 m resolution SM data for the Loess Plateau. This study found the following: (1) The Normalized Difference Vegetation Index (NDVI) was the most important environmental factor in all divisions except the Flood Plain, and the Digital Elevation Model (DEM) was second only to the NDVI in the overall importance evaluation, both of which positively influenced SM. (2) SM variability increased and then decreased when SM was below 0.4 cm 3 /cm 3 , but showed a quadratic growth trend when exceeding this threshold. The Rocky Mountain division exhibited the highest variability under the same SM. (3) Validation showed that the improved algorithm, based on geographic divisions to analyze factors importance and interpolation of coarse-scale SM and variability, had the highest accuracy, with an average R of 0.753 and an average ubRMSE of 0.042 cm 3 /cm 3 . The improved algorithm produced higher resolution, more accurate SM data, and offered insights for downscaling studies in arid regions, meeting the region’s high-resolution SM needs.

Suggested Citation

  • Lei Han & Zheyuan Miao & Zhao Liu & Hongliang Kang & Han Zhang & Shaoan Gan & Yuxuan Ren & Guiming Hu, 2025. "An Improved Soil Moisture Downscaling Method Based on Soil Properties and Geographical Divisions over the Loess Plateau," Land, MDPI, vol. 14(2), pages 1-22, February.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:2:p:410-:d:1592390
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    References listed on IDEAS

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    1. Laibao Liu & Lukas Gudmundsson & Mathias Hauser & Dahe Qin & Shuangcheng Li & Sonia I. Seneviratne, 2020. "Soil moisture dominates dryness stress on ecosystem production globally," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
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