{
  "abstract": "L-band Vegetation Optical Depth (L-VOD) has emerged as a critical remote sensing proxy for monitoring global aboveground biomass (AGB) dynamics. Persistent methodological ambiguities, including the absence of standardized protocols for deriving AGB from L-VOD and the use of space-for-time assumption underpinning temporal predictions, pose challenges to the reliability of AGB estimates. In this study, we conducted a comprehensive evaluation on the current methodology using the SMOS-ICV2 L-VOD dataset and five AGB reference datasets. We find that all the existing fitting methods generally capture the AGB spatial variation, achieving 69-77 % variance explanation. Yet integrating tree cover significantly improves the AGB predictions in regions with small and medium L-VOD values. However, all methods fail to capture the spatial variation of AGB references in dense rainforests with L-VOD > 1, where the AGB reference data also show large discrepancies. Testing on space-for-time assumption reveals that spatial AGB sensitivities to L-VOD tend to be larger than temporal sensitivities. We suggest incorporating long-term in situ observations and remotely sensed vegetation structural data to understand the discrepancy between the AGB-L-VOD sensitivities and to improve AGB predictions. By providing a comprehensive evaluation of fitting methods, our results offer a cautionary tale to the use of L-VOD data to infer AGB dynamics and the necessity of developing long-term field-based biomass change datasets for further constraining and evaluating AGB predictions from remote sensing observations.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "Yuan Zhang"
    },
    {
      "name": "Philippe Ciais"
    },
    {
      "name": "Jean-Pierre Wigneron"
    },
    {
      "name": "Jérôme Chave"
    },
    {
      "name": "Nan Cong"
    },
    {
      "name": "Xiaojun Li"
    },
    {
      "name": "Yan Yang"
    },
    {
      "name": "Sassan Saatchi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fsv1hdfv70x6k.bib",
  "cited_by": 2,
  "doi": "10.1016/j.jag.2025.104931",
  "fulltext_url": null,
  "id": "fsv1hdfv70x6k",
  "issns": [
    "1569-8432",
    "1872-826X"
  ],
  "landing_url": "https://hal.science/hal-05838936v1",
  "language": "en",
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.sdu"
    ],
    [
      "hal",
      "1.sdu.envi"
    ],
    [
      "hal",
      "1.sdu.ocean"
    ],
    [
      "openalex",
      "2305|Soil Moisture and Remote Sensing"
    ]
  ],
  "openalex": "W4415693781",
  "pdf_url": "https://hal.science/hal-05838936/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/fsv1hdfv70x6k.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:01Z",
      "native_id": "hal-05838936",
      "source": "hal",
      "url": "https://hal.science/hal-05838936v1"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "hal:1.sdu.envi",
      "subfield": "environmental-science"
    },
    {
      "field": "earth",
      "origin": "hal:1.sdu.ocean",
      "subfield": "oceanography"
    }
  ],
  "title": "How accurately does L band vegetation optical depth predict aboveground biomass?",
  "title_text": "How accurately does L band vegetation optical depth predict aboveground biomass?",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:01Z",
  "url": "https://research.draconitic.com/paper/fsv1hdfv70x6k",
  "venue": "International Journal of Applied Earth Observation and Geoinformation",
  "versions": []
}