{
  "abstract": "Satellite-derived terrestrial chlorophyll (Chl) content is generally estimated using model inversion or simple relationships with vegetation indices, which require parameterization for each species or plant functional type. Model inversion approaches require essential external inputs, such as leaf area index (LAI), carotenoid content, and land-cover classification. Although satellite-derived LAI products are generated frequently enough to capture both natural and artificial vegetation disturbances, land-cover classification products are produced less frequently, and carotenoid content products are rarely available. Thus, this study aimed to address these limitations by directly estimating Chl content from GCOM-C satellite observations, thereby eliminating the requirement of external inputs. For direct retrieval, reflectance must vary with Chl content up to a sufficiently high level; among the two green bands in GCOM-C/SGLI, Band 6 satisfies this criterion. Therefore, we developed an estimation method using this band to derive a proposed ground-area-based total Chl content (Chl ground ), the vertically integrated Chl content from the ground up through different canopy layers. This offers a computationally efficient, near-real-time detection method without requiring ancillary satellite-derived information. The results indicate that the general, broad-leaf, needle-leaf, and grass models achieved R 2 values of 0.86, 0.93, 0.91, and 0.94, respectively, even under varying soil moisture and the fraction of water-body area within a satellite-vegetated pixel. This independently derived Chl ground metric provides new information and insights into vegetation dynamics. Monthly global Chl ground distribution maps generated in this study reveal that Chl ground differs from LAI and other ecological parameters in both spatial distribution and peak timing, consistent with general ecological knowledge.",
  "abstract_shown": true,
  "affiliations": [
    "Earth Observation Research Center, Japan Aerospace Exploration Agency (JAXA), Ibaraki, Japan; Green-Bio Technology Research Center, Ibaraki University, Ibaraki, Japan; Corresponding author at: Green-Bio Technology Research Center, Ibaraki University, Ibaraki, Japan.",
    "Faculty of Agriculture, Kyushu University, Fukuoka, Japan",
    "Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Kanagawa, Japan",
    "Graduate School of Environmental Science, Hokkaido University, Hokkaido, Japan",
    "Faculty of Applied Biology, Kyoto Institute of Technology, Kyoto, Japan",
    "Earth Observation Research Center, Japan Aerospace Exploration Agency (JAXA), Ibaraki, Japan"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Tomoko Kawaguchi Akitsu"
    },
    {
      "affs": [
        1
      ],
      "name": "Atsushi Kume"
    },
    {
      "affs": [
        2
      ],
      "name": "Roxanne Lai"
    },
    {
      "affs": [
        2
      ],
      "name": "Hideki Kobayashi"
    },
    {
      "affs": [
        3
      ],
      "name": "Tatsuro Nakaji"
    },
    {
      "affs": [
        4
      ],
      "name": "Yuko T. Hanba"
    },
    {
      "affs": [
        5
      ],
      "name": "Hiroshi Murakami"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7gg9x3smzaz9n.bib",
  "cited_by": 0,
  "doi": "10.1016/j.ecoinf.2026.104097",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S1574954126005054",
  "id": "7gg9x3smzaz9n",
  "issns": [
    "1574-9541",
    "1878-0512"
  ],
  "landing_url": "https://doi.org/10.1016/j.ecoinf.2026.104097",
  "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"
    ],
    [
      "lcc",
      "T58.5-58.64"
    ],
    [
      "openalex",
      "2303|Remote Sensing in Agriculture"
    ]
  ],
  "openalex": "W7222847814",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/7gg9x3smzaz9n.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:29Z",
      "native_id": "289f23b783f94ac58c4886a323c21043",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.ecoinf.2026.104097"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2303|Remote Sensing in Agriculture",
      "subfield": "ecology"
    }
  ],
  "title": "GCOM-C/SGLI ground-area-based chlorophyll content estimation: a computationally efficient algorithm free from LAI and vegetation classification",
  "title_text": "GCOM-C/SGLI ground-area-based chlorophyll content estimation: a computationally efficient algorithm free from LAI and vegetation classification",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:29Z",
  "url": "https://research.draconitic.com/paper/7gg9x3smzaz9n",
  "venue": "Ecological Informatics",
  "versions": []
}