{
  "abstract": "Introduction: Mangrove extent maps lack the taxonomic information needed for carbon assessment, restoration evaluation, and management. Taxon mapping at 10 m remains constrained by spectral similarity, mixed pixels, limited reference coverage, and spatial dependence.\n\nMethods: We developed the Inverted-Residual Spectral One-Dimensional Convolutional Neural Network (IR-Spec1D) as a compact classifier for a 14-feature Sentinel-1/2 optical and radar vector. To capture nonlinear optical–radar relationships compactly, inverted-residual and depthwise-separable transformations learn feature interactions, while Efficient Channel Attention emphasizes informative learned channels. Spectral-Domain Dropout reduces dependence on individual inputs through training-time feature masking. The workflow combines this configuration with spatial evaluation, post hoc temperature scaling, predictive-entropy analysis, and frozen transfer assessment. The six-category source taxonomy includes the B. sexangula / B. gymnorhiza complex. Six classifiers were evaluated using April 2024 observations from the selected Dongzhai Harbor study area.\n\nResults: IR-Spec1D achieved the highest mean overall accuracy (0.9720) and macro-F1 (0.9406) under five spatially grouped cross-validation folds. Performance remained comparatively high with a 100-m exclusion buffer. Held-out source-domain diagnostics showed that temperature scaling reduced mean expected calibration error from 0.0346 to 0.0125 without changing categorical predictions. Predictive entropy ranked cross-validated source-domain errors with an AUROC of 0.8808. The frozen ensemble retained an overall accuracy of 0.9450 in both same-site temporal evaluations, whereas four-category closed-set geographic transfer to Gaoqiao yielded 0.7118.\n\nDiscussion: The combined evidence characterizes taxon discrimination, held-out probability behavior, and performance retention under specified transfer conditions within a common mangrove-mapping workflow.",
  "abstract_shown": true,
  "affiliations": [
    "College of Artificial Intelligence, Hainan Normal University",
    "Key Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education",
    "Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University",
    "College of Life Sciences, Hainan Normal University"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Guohao Liu"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Shulei Wu"
    },
    {
      "affs": [
        0
      ],
      "name": "Yaoru Wang"
    },
    {
      "affs": [
        2
      ],
      "name": "Yihui Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Huandong Chen"
    },
    {
      "affs": [
        3
      ],
      "name": "Yukai Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhongqiang Wu"
    },
    {
      "affs": [
        0
      ],
      "name": "Nan Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fw7z3jg2y64eh.bib",
  "cited_by": 0,
  "doi": "10.3389/fpls.2026.1967679",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13719328/?tool=EBI",
  "id": "fw7z3jg2y64eh",
  "issns": [
    "1664-462X"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1967679/full",
  "language": "eng",
  "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": [
    [
      "heading",
      "Original Research"
    ],
    [
      "openalex",
      "2214|Remote-Sensing Image Classification"
    ]
  ],
  "openalex": "W7222536447",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13719328/pdf/?tool=EBI",
  "pmcid": "PMC13719328",
  "pmid": "42964012",
  "published": "2026-10-16",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/fw7z3jg2y64eh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:25Z",
      "native_id": "10.3389/fpls.2026.1967679",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpls.2026.1967679/full"
    },
    {
      "first_seen": "2026-10-31T12:21:36Z",
      "native_id": "PMC:PMC13719328",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13719328"
    },
    {
      "first_seen": "2026-11-02T12:23:19Z",
      "native_id": "PMC13719328",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fpls.2026.1967679"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "issn:1664-462X",
      "subfield": "plant-biology"
    }
  ],
  "title": "Mangrove taxon mapping from Sentinel-1/2 using a compact one-dimensional network with probability calibration, predictive entropy, and frozen transfer evaluation",
  "title_text": "Mangrove taxon mapping from Sentinel-1/2 using a compact one-dimensional network with probability calibration, predictive entropy, and frozen transfer evaluation",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:20:24Z",
  "url": "https://research.draconitic.com/paper/fw7z3jg2y64eh",
  "venue": "Frontiers in Plant Science",
  "versions": []
}