Hybrid retrieval method for leaf chlorophyll content mapping of winter wheat from EnMAP spaceborne imaging spectroscopy data
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.
Authors
- László Mucsi (ORCID: https://orcid.org/0000-0002-5807-3742)
- József Szatmári (ORCID: https://orcid.org/0000-0002-7896-3363)
- Suman Madhup
- Zokhid Mamatkulov (ORCID: https://orcid.org/0000-0001-9853-856X)
- Márton Kiss (ORCID: https://orcid.org/0000-0002-5621-7976)
- Zalán Tobak (ORCID: https://orcid.org/0000-0002-4960-2198)
- Mokhigul Abdurakhimova (ORCID: https://orcid.org/0000-0002-9217-2293)
- Kosimdjon Rakhmonov
- Nizom Farmonov (ORCID: https://orcid.org/0000-0002-2491-9340)
- Kamola Khaitova (ORCID: https://orcid.org/0000-0003-4052-1065)
- Márkó Sóti (ORCID: https://orcid.org/0009-0003-9875-9946)
- Ramziddin N. Sharopov (ORCID: https://orcid.org/0009-0008-6259-7769)
- Rukhiddin Turaev
- Tashbaeva Khulkaroy
- Komil Khujakeldiev
- Urinov Jamol
- Muxtorov Uzbekkhon
- Qodirov Isomiddin
Institutions
- Philipps University of Marburg (DE)
- University of Szeged (HU)
- Tashkent Institute of Irrigation and Agricultural Mechanization Engineers (UZ)
- Karshi State Technical University (UZ)
Publication Details
- Journal
- Geocarto International
- Published
- 2026-11-06
- DOI
- https://doi.org/10.1080/10106049.2026.2743440
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Nemzeti Kutatási Fejlesztési és Innovációs Hivatal
- Szegedi Tudományegyetem