Coupling RUSLE with Spatial Econometrics: A 35-Year Assessment of Soil Erosion Dynamics and Driving Factors on the Loess Plateau, China (1990–2024)

Soil erosion poses a severe threat to agricultural productivity and ecological security on the Loess Plateau. However, previous studies have rarely integrated physical modeling, elasticity coefficients, and spillover effects into a unified framework at the county level. To address this gap, this study coupled the Revised Universal Soil Loss Equation (RUSLE) with the Spatial Durbin Model (SDM) to systematically investigate the spatiotemporal dynamics, factor elasticity characteristics, and spatial dependence mechanisms of soil erosion on the Loess Plateau from 1990 to 2024. Results show that the annual average erosion rate decreased by 15.5%, with a highly volatile phase before 2001 and a stabilized, low-erosion phase thereafter. The driving factors exhibited marked heterogeneity in direction and strength. The land cover and management factor (C) was the strongest erosion-reducing factor, whereas annual precipitation (PRE) was the primary natural erosion-enhancing factor. County-level erosion also displayed significant positive spatial dependence. PRE had a stable positive indirect effect, whereas C and the support practice factor (P) mainly contained erosion within local jurisdictions. These findings of a unified RUSLE–SDM framework reveal a joint driving mechanism of localized human interventions and climate-driven cross-regional spillovers, providing quantitative support for differentiated soil and water conservation strategies on the Loess Plateau.

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Journal
Remote Sensing
Published
2026-06-18
DOI
https://doi.org/10.3390/rs18122034
Primary Topic
Soil erosion and sediment transport
Type
article
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article

Coupling RUSLE with Spatial Econometrics: A 35-Year Assessment of Soil Erosion Dynamics and Driving Factors on the Loess Plateau, China (1990–2024)

Qigen Lin, Yujin Xia, Jiangbing Sun, Wen Dai et al.
Remote Sensing
Soil erosion and sediment transport
article

Coupling RUSLE with Spatial Econometrics: A 35-Year Assessment of Soil Erosion Dynamics and Driving Factors on the Loess Plateau, China (1990–2024)

Qigen Lin, Yujin Xia, Jiangbing Sun, Wen Dai, Yuhanbing Liang
article en

Abstract

Soil erosion poses a severe threat to agricultural productivity and ecological security on the Loess Plateau. However, previous studies have rarely integrated physical modeling, elasticity coefficients, and spillover effects into a unified framework at the county level. To address this gap, this study coupled the Revised Universal Soil Loss Equation (RUSLE) with the Spatial Durbin Model (SDM) to systematically investigate the spatiotemporal dynamics, factor elasticity characteristics, and spatial dependence mechanisms of soil erosion on the Loess Plateau from 1990 to 2024. Results show that the annual average erosion rate decreased by 15.5%, with a highly volatile phase before 2001 and a stabilized, low-erosion phase thereafter. The driving factors exhibited marked heterogeneity in direction and strength. The land cover and management factor (C) was the strongest erosion-reducing factor, whereas annual precipitation (PRE) was the primary natural erosion-enhancing factor. County-level erosion also displayed significant positive spatial dependence. PRE had a stable positive indirect effect, whereas C and the support practice factor (P) mainly contained erosion within local jurisdictions. These findings of a unified RUSLE–SDM framework reveal a joint driving mechanism of localized human interventions and climate-driven cross-regional spillovers, providing quantitative support for differentiated soil and water conservation strategies on the Loess Plateau.

Remote SensingVol. 18(12)
Hohai University (CN), Nanjing University of Information Science and Technology (CN)
Openalex Percentile: Top 9%
Soil erosion and sediment transport
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Coupling RUSLE with Spatial Econometrics: A 35-Year Assessment of Soil Erosion Dynamics and Driving Factors on the Loess Plateau, China (1990–2024) — Qigen Lin, Yujin Xia, et al. · Remote Sensing (2026) | TGRS Research Map | TGRS