{
  "abstract": "Accurate prediction of post-fire snow conditions does not necessarily establish how wildfire changed those conditions. We developed an explainable geospatial artificial intelligence framework combining multisource Earth observations, meteorological information, and matched before-after comparisons to examine seasonal snow persistence following the 2020 Creek Fire in California's Sierra Nevada. Harmonized Landsat Sentinel-2 observations were used to estimate the fraction of clear-sky observations containing snow during October-July for eleven water years, 2016-2026, on a 500 m analysis grid. We matched 3,778 snow-zone cells inside the fire perimeter to comparable unburned controls using terrain and pre-fire snow conditions; absolute standardized mean differences after matching were no greater than 0.047. HLS persistence agreed closely with MODIS, with a mean annual spatial correlation of 0.947. The landscape-average before-after control-impact contrast was +0.0017, with a year-level 95% confidence interval of -0.023 to +0.027. A stronger response emerged in the highest burn-severity class, where observed persistence increased by 0.026 relative to matched controls, equivalent to 2.6 percentage points. Under 5 km spatial cross-validation, XGBoost predicted raw post-fire persistence with an out-of-fold coefficient of determination of 0.811, whereas predictive skill for the fire-adjusted anomaly reached 0.046. Elevation and temperature together accounted for 65.7% of mean absolute model attribution. These findings distinguish a severity-associated optical snow response from the terrain-climate relationships that dominate predictive skill. Combining matched comparisons with explainable GeoAI provides a practical framework for forest monitoring that separates accurate environmental mapping from inference about disturbance effects.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.36345",
  "author_total": 2,
  "authors": [
    {
      "name": "Parastoo Farajpoor"
    },
    {
      "name": "Mohammadreza Narimani"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bjzrtp2krqrkj.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.36345",
  "fulltext_url": null,
  "id": "bjzrtp2krqrkj",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.36345",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "eess.IV"
    ],
    [
      "arxiv",
      "physics.geo-ph"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "openalex",
      "2306|Fire effects on ecosystems"
    ]
  ],
  "openalex": "W7214911537",
  "pdf_url": "https://arxiv.org/pdf/2609.36345",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bjzrtp2krqrkj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:31Z",
      "native_id": "2609.36345",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.36345"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.geo-ph",
      "subfield": "geophysics"
    },
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "From Wildfire Severity to Snow Persistence: A Multisource GeoAI Study of the 2020 Creek Fire",
  "title_text": "From Wildfire Severity to Snow Persistence: A Multisource GeoAI Study of the 2020 Creek Fire",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:41Z",
  "url": "https://research.draconitic.com/paper/bjzrtp2krqrkj",
  "venue": "arXiv",
  "versions": []
}