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Peer-reviewedCC BYGeocarto International

Hybrid retrieval method for leaf chlorophyll content mapping of winter wheat from EnMAP spaceborne imaging spectroscopy data

  • Rukhiddin Turaev1
  • Komil Khujakeldiev2
  • Nizom Farmonov3
  • Zalán Tobak4
  • József Szatmári4
  • Márton Kiss4
  • Márkó Sóti5
  • Suman Madhup5
  • Ramziddin Sharopov1
  • Kamola Khaitova1
  • Tashbaeva Khulkaroy1
  • Muxtorov Uzbekkhon6
6 more authors
  • Mokhigul Abdurakhimova6
  • Kosimdjon Rakhmonov6
  • Zokhid Mamatkulov6
  • Urinov Jamol2
  • Qodirov Isomiddin7
  • László Mucsi4
7 affiliations
  1. 1The Scientific and Design Institute “Uzdavyerloyikha”, Tashkent, Ministry of Agriculture of the Republic of Uzbekistan
  2. 2Department of Land Reclamation and Land Cadastre, Karshi State Technical University, Karshi, Uzbekistan
  3. 3Laboratory for Climatology and Remote Sensing, Department of Geography, Philipps-Universität Marburg, Marburg, Germany
  4. 4Department of Atmospheric and Geospatial Data Science, University of Szeged, Szeged, Hungary
  5. 5Doctoral School of Geosciences, University of Szeged, Szeged, Hungary
  6. 6Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, Tashkent, Uzbekistan
  7. 7Karshi University of Economics and Pedagogy, Karshi, Uzbekistan
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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.

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Cite

BibTeXRIS
BibTeX
@article{turaev2026hybrid,
  title = {{Hybrid retrieval method for leaf chlorophyll content mapping of winter wheat from EnMAP spaceborne imaging spectroscopy data}},
  author = {Rukhiddin Turaev and Komil Khujakeldiev and Nizom Farmonov and Zalán Tobak and József Szatmári and Márton Kiss and Márkó Sóti and Suman Madhup and Ramziddin Sharopov and Kamola Khaitova and Tashbaeva Khulkaroy and Muxtorov Uzbekkhon and Mokhigul Abdurakhimova and Kosimdjon Rakhmonov and Zokhid Mamatkulov and Urinov Jamol and Qodirov Isomiddin and László Mucsi},
  journal = {Geocarto International},
  year = {2026},
  month = nov,
  doi = {10.1080/10106049.2026.2743440},
  url = {https://doi.org/10.1080/10106049.2026.2743440},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Hybrid retrieval method for leaf chlorophyll content mapping of winter wheat from EnMAP spaceborne imaging spectroscopy data
AU  - Rukhiddin Turaev
AU  - Komil Khujakeldiev
AU  - Nizom Farmonov
AU  - Zalán Tobak
AU  - József Szatmári
AU  - Márton Kiss
AU  - Márkó Sóti
AU  - Suman Madhup
AU  - Ramziddin Sharopov
AU  - Kamola Khaitova
AU  - Tashbaeva Khulkaroy
AU  - Muxtorov Uzbekkhon
AU  - Mokhigul Abdurakhimova
AU  - Kosimdjon Rakhmonov
AU  - Zokhid Mamatkulov
AU  - Urinov Jamol
AU  - Qodirov Isomiddin
AU  - László Mucsi
PY  - 2026
DA  - 2026/11/07
JO  - Geocarto International
DO  - 10.1080/10106049.2026.2743440
UR  - https://doi.org/10.1080/10106049.2026.2743440
AB  - 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.
SN  - 1752-0762
SN  - 1010-6049
ER  -