Spatiotemporal dynamics and drivers of ecological quality in Shaanxi (Yellow River Basin, China) based on optimized RSEI and SHAP

The Yellow River Basin serves as a crucial ecological barrier in China, necessitating an understanding of its ecological quality dynamics and drivers. Utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) data (2000–2023), we developed an enhanced Remote Sensing Ecological Index (RSEI) for the Shaanxi section of the Yellow River Basin by applying the entropy weight method (EWM). Spatiotemporal variations were assessed using Sen’s slope and the Mann–Kendall (MK) test (Sen–MK) and the Hurst exponent (H), while driving forces were interpreted using SHapley Additive exPlanations (SHAP). The mean RSEI was 0.51, indicating ecological quality above medium with a slight upward trend. Improvement was predominant, following a “south-high, north-low” pattern; slightly and significantly improved areas comprised 45.05% and 11.17%, while significant degradation covered 6.20%. Hurst analysis showed 56.96% of areas had significant increases, with 56.64% likely to improve further. Land use changes and natural factors jointly influenced RSEI, with cropland/grassland conversion to forest enhancing ecological quality. Soil moisture emerged as the dominant climatic factor, while interactions among wind speed, precipitation, and SOIL moisture bolstered ecosystem stability. These findings offer a scientific basis for ecological management and sustainable development in the Shaanxi section of the Yellow River Basin.

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Publication Details

Journal
Frontiers in Environmental Science
Published
2026-02-09
DOI
https://doi.org/10.3389/fenvs.2026.1721306
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00

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article

Spatiotemporal dynamics and drivers of ecological quality in Shaanxi (Yellow River Basin, China) based on optimized RSEI and SHAP

Chan Ma, Ming Ma, Siyu Wang, Hongqiang Chen et al.
Frontiers in Environmental Science
Remote Sensing in Agriculture
article

Spatiotemporal dynamics and drivers of ecological quality in Shaanxi (Yellow River Basin, China) based on optimized RSEI and SHAP

Chan Ma, Ming Ma, Siyu Wang, Hongqiang Chen, Chi Zhang
article en

Abstract

The Yellow River Basin serves as a crucial ecological barrier in China, necessitating an understanding of its ecological quality dynamics and drivers. Utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) data (2000–2023), we developed an enhanced Remote Sensing Ecological Index (RSEI) for the Shaanxi section of the Yellow River Basin by applying the entropy weight method (EWM). Spatiotemporal variations were assessed using Sen’s slope and the Mann–Kendall (MK) test (Sen–MK) and the Hurst exponent (H), while driving forces were interpreted using SHapley Additive exPlanations (SHAP). The mean RSEI was 0.51, indicating ecological quality above medium with a slight upward trend. Improvement was predominant, following a “south-high, north-low” pattern; slightly and significantly improved areas comprised 45.05% and 11.17%, while significant degradation covered 6.20%. Hurst analysis showed 56.96% of areas had significant increases, with 56.64% likely to improve further. Land use changes and natural factors jointly influenced RSEI, with cropland/grassland conversion to forest enhancing ecological quality. Soil moisture emerged as the dominant climatic factor, while interactions among wind speed, precipitation, and SOIL moisture bolstered ecosystem stability. These findings offer a scientific basis for ecological management and sustainable development in the Shaanxi section of the Yellow River Basin.

Frontiers in Environmental ScienceVol. 14
Ethnographic Museum (HU), First Research Institute of the Ministry of Public Security (CN), Ministry of Education (BD)
Education Department of Shaanxi Province
Life in Land
Openalex Percentile: Top 66%
Remote Sensing in Agriculture
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Spatiotemporal dynamics and drivers of ecological quality in Shaanxi (Yellow River Basin, China) based on optimized RSEI and SHAP — Chan Ma, Ming Ma, et al. · Frontiers in Environmental Science (2026) | TGRS Research Map | TGRS