{
  "abstract": "Accurate mapping of leaf chlorophyll content (Cab) is critical for monitoring plant growth and maximising crop yields in sustainable precision farming. This study developed a nonparametric, hybrid retrieval method to map winter wheat (Triticum aestivum) Cab from EnMAP spaceborne hyperspectral data, using in situ data collected in southern Hungary. A simulated training dataset was optimised using principal component analysis, active learning (AL), and spectral and sampling reduction, then used to train a Gaussian process regression (GPR) model. Validation yielded R² = 0.76 and RMSE = 8.22 μg/cm². The best results were achieved after adding 101 AL samples to 50 starting samples (NRMSE = 15.72%). Adding non-vegetated (NV) spectra slightly reduced validation accuracy (NRMSE = 16.93%, R² = 0.74) but produced more consistent models able to handle spectral variability across the heterogeneous EnMAP scene. GPR also provides uncertainty estimates alongside trait retrievals, indicating model reliability.",
  "abstract_shown": true,
  "affiliations": [
    "The Scientific and Design Institute “Uzdavyerloyikha”, Tashkent, Ministry of Agriculture of the Republic of Uzbekistan",
    "Department of Land Reclamation and Land Cadastre, Karshi State Technical University, Karshi, Uzbekistan",
    "Laboratory for Climatology and Remote Sensing, Department of Geography, Philipps-Universität Marburg, Marburg, Germany",
    "Department of Atmospheric and Geospatial Data Science, University of Szeged, Szeged, Hungary",
    "Doctoral School of Geosciences, University of Szeged, Szeged, Hungary",
    "Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, Tashkent, Uzbekistan",
    "Karshi University of Economics and Pedagogy, Karshi, Uzbekistan"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 18,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Rukhiddin Turaev"
    },
    {
      "affs": [
        1
      ],
      "name": "Komil Khujakeldiev"
    },
    {
      "affs": [
        2
      ],
      "name": "Nizom Farmonov"
    },
    {
      "affs": [
        3
      ],
      "name": "Zalán Tobak"
    },
    {
      "affs": [
        3
      ],
      "name": "József Szatmári"
    },
    {
      "affs": [
        3
      ],
      "name": "Márton Kiss"
    },
    {
      "affs": [
        4
      ],
      "name": "Márkó Sóti"
    },
    {
      "affs": [
        4
      ],
      "name": "Suman Madhup"
    },
    {
      "affs": [
        0
      ],
      "name": "Ramziddin Sharopov"
    },
    {
      "affs": [
        0
      ],
      "name": "Kamola Khaitova"
    },
    {
      "affs": [
        0
      ],
      "name": "Tashbaeva Khulkaroy"
    },
    {
      "affs": [
        5
      ],
      "name": "Muxtorov Uzbekkhon"
    },
    {
      "affs": [
        5
      ],
      "name": "Mokhigul Abdurakhimova"
    },
    {
      "affs": [
        5
      ],
      "name": "Kosimdjon Rakhmonov"
    },
    {
      "affs": [
        5
      ],
      "name": "Zokhid Mamatkulov"
    },
    {
      "affs": [
        1
      ],
      "name": "Urinov Jamol"
    },
    {
      "affs": [
        6
      ],
      "name": "Qodirov Isomiddin"
    },
    {
      "affs": [
        3
      ],
      "name": "László Mucsi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2fy9h2fqycwy9.bib",
  "cited_by": 0,
  "doi": "10.1080/10106049.2026.2743440",
  "fulltext_url": "https://www.tandfonline.com/doi/10.1080/10106049.2026.2743440",
  "id": "2fy9h2fqycwy9",
  "issns": [
    "1752-0762",
    "1010-6049"
  ],
  "landing_url": "https://doi.org/10.1080/10106049.2026.2743440",
  "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",
      "GB3-5030"
    ],
    [
      "openalex",
      "2303|Remote Sensing in Agriculture"
    ]
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  "openalex": "W7225381324",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-07",
  "publisher": "Taylor & Francis Group",
  "ris": "https://research.draconitic.com/paper/2fy9h2fqycwy9.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T14:21:05Z",
      "native_id": "2e22a507c4b447609e61cb853763ddee",
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      "url": "https://doi.org/10.1080/10106049.2026.2743440"
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  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2303|Remote Sensing in Agriculture",
      "subfield": "ecology"
    }
  ],
  "title": "Hybrid retrieval method for leaf chlorophyll content mapping of winter wheat from EnMAP spaceborne imaging spectroscopy data",
  "title_text": "Hybrid retrieval method for leaf chlorophyll content mapping of winter wheat from EnMAP spaceborne imaging spectroscopy data",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T14:20:32Z",
  "url": "https://research.draconitic.com/paper/2fy9h2fqycwy9",
  "venue": "Geocarto International",
  "versions": []
}