Abstract
Large-scale eco-environmental assessment requires not only long-term monitoring but also an understanding of short-term predictability and spatially heterogeneous environmental associations. Here, we evaluated the Remote Sensing Ecological Index (RSEI) across China from 2002 to 2024 and integrated spatiotemporal trend analysis, one-year-ahead prediction, and interpretable machine learning. The national area-weighted mean RSEI increased from 0.6385 in 2002 to 0.6614 in 2024, although the magnitude and timing of change varied markedly among climatic zones. Under a spatially and temporally held-out test design, XGBoost achieved the best predictive performance, with a weighted of 0.9712 and an RMSE of 0.0262, reducing RMSE by 13.67% relative to a persistence baseline. Variance decomposition showed that 97.39% of total RSEI variability was attributable to between-pixel spatial differences, whereas only 2.61% reflected within-pixel interannual variation. Interpretable modelling further revealed contrasting association structures: elevation, warm-season temperature and land cover were most important for spatial-level differences, whereas warm-season precipitation, land cover and potential evapotranspiration dominated interannual variability. These relationships also differed substantially among climatic zones. Our results show that RSEI dynamics are highly predictable at a one-year horizon, but the factors associated with persistent spatial patterns differ from those linked to year-to-year ecological fluctuations, highlighting the need to distinguish spatial baseline conditions from interannual environmental variability in large-scale ecological assessment.
Funding
This work was supported by the Research and Demonstration of Key Technologies for Ecological Restoration and Reconstruction in Grassland Mining Areas (Inner Mongolia Autonomous Region Science and Technology Plan 2023YFDZ0078) and the Higher Education Reform and Development Project – Subsidy Fund for Doctoral Degree Program in Soil and Water Conservation and Desertification Prevention and Control (Financial Funds for 2025).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Reprints and permissions
About this article
Cite this article
Chen, G., Jian, C., Zhang, Y. et al. Interpretable machine learning reveals predictability and spatially heterogeneous factors associated with the RSEI in China.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-72438-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-72438-6
Keywords
- Remote sensing ecological index
- Eco-environmental quality
- Interpretable machine learning
- Short-term prediction
- Associated factors
- Spatial heterogeneity
Source: Ecology - nature.com
