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

Lei Han (), Zheyuan Miao, Zhao Liu, Hongliang Kang, Han Zhang, Shaoan Gan, Yuxuan Ren and Guiming Hu
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Lei Han: School of Land Engineering, Chang’an University, Xi’an 710054, 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

Land, 2025, vol. 14, issue 2, 1-22

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.

Keywords: soil moisture downscaling; soil moisture variability; soil properties; geographical divisions; Random Forest (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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