{
  "abstract": "Long-term soil salinity mapping in arid oases is vulnerable to spatial dependence and interannual shifts, yet transfer to unsampled areas and years is rarely tested explicitly. We combined 1107 quality-controlled topsoil observations from the Yanqi Basin, China (2000, 2005, 2015, 2020, and 2025), with 30 environmental covariates. Ten algorithms and five input strategies were compared using site-grouped five-fold, 15-km spatial-block five-fold, and leave-one-year-out (LOYO) validation. Extremely Randomized Trees (ERT) provided the most stable overall performance (R 2 = 0.602, 0.597, and 0.501), whereas Transformer performed best under LOYO (R 2 = 0.523), showing that algorithm rankings depended on the transfer scenario. PCC, VIP, GRA, and GBM feature selection produced no consistent gains; ERT fitted with all 30 predictors was therefore used for mapping. Within the common valid support, the area with SSC ≥ 6 g kg −1 increased from 23.65% in 2000 to 36.41% in 2005, declined to 22.40% in 2015, and rose to 33.88% in 2025, indicating an expansion–relief–reintensification pattern. ERT-SHAP identified SI3, SI1, and Int1 as the leading predictive indicators, while groundwater depth added little independent information after the other covariates were included. ERT systematically underestimated SSC ≥ 10 g kg −1 (spatial-block bias, −4.426 g kg −1 ), requiring caution at the upper end of the observed range. The framework supports reproducible regional comparison and potential hotspot screening by combining spatiotemporal transfer tests, high-value diagnostics, attribution, and long-term mapping.",
  "abstract_shown": true,
  "affiliations": [
    "Xinjiang Key Laboratory of Hydraulic Engineering Security and Water Disasters Prevention, Urumqi 830052, China; College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China",
    "Xinjiang Key Laboratory of Hydraulic Engineering Security and Water Disasters Prevention, Urumqi 830052, China; College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China; Corresponding author at: College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China.",
    "Xinjiang Key Laboratory of Hydraulic Engineering Security and Water Disasters Prevention, Urumqi 830052, China; College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China; Xinjiang Water Conservancy Development Investment (Group) Co., Ltd., Urumqi 830000, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Zhipeng Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Pengnian Yang"
    },
    {
      "affs": [
        0
      ],
      "name": "Youyang Luo"
    },
    {
      "affs": [
        0
      ],
      "name": "Fanghong Han"
    },
    {
      "affs": [
        2
      ],
      "name": "Yongpeng Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Long Zhou"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b8tw7esr3378a.bib",
  "cited_by": 0,
  "doi": "10.1016/j.ecolind.2026.115475",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S1470160X26008769",
  "id": "b8tw7esr3378a",
  "issns": [
    "1470-160X",
    "1872-7034"
  ],
  "landing_url": "https://doi.org/10.1016/j.ecolind.2026.115475",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QH540-549.5"
    ],
    [
      "openalex",
      "2305|Soil Geostatistics and Mapping"
    ]
  ],
  "openalex": "W7212046661",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/b8tw7esr3378a.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:43:20Z",
      "native_id": "895e791c876f4d52a90fe23c7c473dea",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.ecolind.2026.115475"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2305|Soil Geostatistics and Mapping",
      "subfield": "environmental-science"
    }
  ],
  "title": "A 25-year assessment of soil salinity in an arid oasis: Spatiotemporal validation and SHAP attribution",
  "title_text": "A 25-year assessment of soil salinity in an arid oasis: Spatiotemporal validation and SHAP attribution",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:43:20Z",
  "url": "https://research.draconitic.com/paper/b8tw7esr3378a",
  "venue": "Ecological Indicators",
  "versions": []
}