{
  "abstract": "Accurate land cover information is fundamental for monitoring land-use dynamics, supporting sustainable agriculture, and informing environmental and climate-related decision-making; however, detailed and globally consistent crop-specific datasets, particularly for rice, remain limited. The study presents RiceExtent v1.0, a harmonized global rice-specific land cover dataset athigh (10 m) and moderate (250 m) resolution, developed by integrating verified local, national, and regional rice maps with ESA WorldCover v200 using a hierarchical multi-criteria dataset ranking and spatial merging approach that prioritizes source accuracy and spatial detail. Using an extent-based merging strategy that aggregates multi-source rice data, RiceExtent derives a maximum observed rice cultivation extent rather than annual mean estimates, thereby capturing double and triple cropping patterns, fragmented smallholder fields, and inter-annual variability often underrepresented in other products. The final product was evaluated using area-based statistical comparison metrics against Spatial Production Allocation Model (SPAM), RiceAtlas, and GloRice, along with USDA rice production statistics. Validation using independent ground-truth samples was performed using True Positive Rate (TPR). The developed dataset captures an estimated global rice area of ~ 1.67 million km² and shows broad agreement with official statistics and improving representation in Southeast Asia, South Asia, Africa and the Americas. RiceExtent v1.0, provided through an open repository ( https://dataverse.tdl.org/dataset.xhtml?persistentId=doi:10.18738/T8/JYAEHV ) with accompanying Python script, offers a robust resource for agricultural monitoring, crop and hydrological modeling, and climate impact assessments, while enabling collaborative refinement of global rice mapping.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "name": "Akansha Patel"
    },
    {
      "name": "Arun Bawa"
    },
    {
      "name": "Seonggyu Park"
    },
    {
      "name": "Celray James Chawanda",
      "orcid": "0000-0002-8880-9431"
    },
    {
      "name": "Sang-Min Jun"
    },
    {
      "name": "Soon-Kun Choi"
    },
    {
      "name": "Sushil Kumar Himanshu"
    },
    {
      "name": "Raghavan Srinivasan"
    },
    {
      "name": "Jaehak Jeong"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b5cm585wn7jz8.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-70276-0",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-70276-0",
  "id": "b5cm585wn7jz8",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-70276-0",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
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      "lcc",
      "R"
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      "openalex",
      "2303|Remote Sensing in Agriculture"
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  "openalex": "W7104382264",
  "pdf_url": "https://www.nature.com/articles/s41598-026-70276-0.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-21",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/b5cm585wn7jz8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:33Z",
      "native_id": "10.1038/s41598-026-70276-0",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-70276-0"
    },
    {
      "first_seen": "2026-10-30T13:45:54Z",
      "native_id": "f11c9b8260804f718dc8a8a23168a50e",
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      "url": "https://doi.org/10.1038/s41598-026-70276-0"
    }
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  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2303|Remote Sensing in Agriculture",
      "subfield": "ecology"
    }
  ],
  "title": "RiceExtent v1.0: a global rice-specific land cover product from multi-source data integration",
  "title_text": "RiceExtent v1.0: a global rice-specific land cover product from multi-source data integration",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:54Z",
  "url": "https://research.draconitic.com/paper/b5cm585wn7jz8",
  "venue": "Scientific Reports",
  "versions": []
}