{
  "abstract": "Hyperspectral remote sensing has become a key approach for retrieving crop parameters related to canopy structure, physiological status, biochemical composition and productivity. However, many current methods remain centered on task-specific empirical fitting between spectral features and target parameters. Their transferability is limited when crop growth varies across phenological stages, observation scales, sensor conditions and planting systems. Hyperspectral crop-parameter retrieval is moving toward an intelligent inversion paradigm, which is jointly supported by spatio-temporal-spectral representation, mechanistic constraints and trustworthy decision-making. Accordingly, we synthesize recent progress in crop spectral-response mechanisms, satellite-airborne-ground coordinated observation, spatio-temporal consistency processing, artificial intelligence (AI)-driven retrieval models and mechanism-data fusion. Particular attention is given to the integration of hyperspectral features with crop-growth processes and canopy radiative transfer. We also examine transferable spatio-temporal-spectral representations, applicability-domain assessment and uncertainty quantification. Future advances should move beyond improving the accuracy of multi-spatio-temporal observations and instead progress toward mechanism-informed intelligent retrieval that integrates crop-growth knowledge with deep learning. This approach should explicitly represent crop growth processes, spatial heterogeneity and applicability constraints, thereby supporting more reliable crop monitoring, growth diagnosis, yield prediction and decision-making in complex agricultural systems.",
  "abstract_shown": true,
  "affiliations": [
    "Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China",
    "Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China",
    "College of Geography and Environment, Liaocheng University, Liaocheng, China",
    "China Remote Sensing Satellite Ground Station, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Bing Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Dailiang Peng"
    },
    {
      "affs": [
        0
      ],
      "name": "Jinkang Hu"
    },
    {
      "affs": [
        1
      ],
      "name": "Zihang Lou"
    },
    {
      "affs": [
        2
      ],
      "name": "Ke Zheng"
    },
    {
      "affs": [
        0
      ],
      "name": "Quanjun Jiao"
    },
    {
      "affs": [
        3
      ],
      "name": "Shanshan Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Yizhou Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/40t9h1se3h6z1.bib",
  "cited_by": 0,
  "doi": "10.1080/10095020.2026.2754604",
  "fulltext_url": "https://www.tandfonline.com/doi/10.1080/10095020.2026.2754604",
  "id": "40t9h1se3h6z1",
  "issns": [
    "1993-5153",
    "1009-5020"
  ],
  "landing_url": "https://doi.org/10.1080/10095020.2026.2754604",
  "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",
      "GA1-1776"
    ],
    [
      "lcc",
      "QB275-343"
    ]
  ],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": "Taylor & Francis Group",
  "ris": "https://research.draconitic.com/paper/40t9h1se3h6z1.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T14:20:52Z",
      "native_id": "139e8a51ab184273bc441a07cd415e5f",
      "source": "doaj",
      "url": "https://doi.org/10.1080/10095020.2026.2754604"
    }
  ],
  "subjects": [
    {
      "field": "astronomy",
      "origin": "lcc:QB275-343",
      "subfield": "general"
    }
  ],
  "title": "Crop biophysical parameter estimation via AI-enhanced hyperspectral remote sensing",
  "title_text": "Crop biophysical parameter estimation via AI-enhanced hyperspectral remote sensing",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T14:20:17Z",
  "url": "https://research.draconitic.com/paper/40t9h1se3h6z1",
  "venue": "Geo-spatial Information Science",
  "versions": []
}