{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Guoqing Chen",
      "orcid": "0000-0002-9910-0946"
    },
    {
      "name": "Chao Jian"
    },
    {
      "name": "Yanhui Zhang"
    },
    {
      "name": "Xu Zheng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bm5rd1sdhvm5n.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-72438-6",
  "fulltext_url": null,
  "id": "bm5rd1sdhvm5n",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-72438-6",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2306|Land Use and Ecosystem Services"
    ]
  ],
  "openalex": "W7214907632",
  "pdf_url": "https://www.nature.com/articles/s41598-026-72438-6_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-30",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/bm5rd1sdhvm5n.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:19Z",
      "native_id": "10.1038/s41598-026-72438-6",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-72438-6"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2306|Land Use and Ecosystem Services",
      "subfield": "climate"
    }
  ],
  "title": "Interpretable machine learning reveals predictability and spatially heterogeneous factors associated with the RSEI in China",
  "title_text": "Interpretable machine learning reveals predictability and spatially heterogeneous factors associated with the RSEI in China",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:19Z",
  "url": "https://research.draconitic.com/paper/bm5rd1sdhvm5n",
  "venue": "Scientific Reports",
  "versions": []
}