{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T17:12:55Z","timestamp":1781716375548,"version":"3.54.5"},"reference-count":59,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T00:00:00Z","timestamp":1667347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Centre for Research and Development of Poland","award":["POIR.04.01.04-00-0009\/19"],"award-info":[{"award-number":["POIR.04.01.04-00-0009\/19"]}]},{"name":"The National Centre for Research and Development of Poland","award":["07\/010\/BKM22\/1017"],"award-info":[{"award-number":["07\/010\/BKM22\/1017"]}]},{"name":"Silesian University of Technology","award":["POIR.04.01.04-00-0009\/19"],"award-info":[{"award-number":["POIR.04.01.04-00-0009\/19"]}]},{"name":"Silesian University of Technology","award":["07\/010\/BKM22\/1017"],"award-info":[{"award-number":["07\/010\/BKM22\/1017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recent advancements in hyperspectral remote sensing bring exciting opportunities for various domains. Precision agriculture is one of the most widely-researched examples here, as it can benefit from the non-invasiveness and enormous scalability of the Earth observation solutions. In this paper, we focus on estimating the chlorophyll level in leaves using hyperspectral images\u2014capturing this information may help farmers optimize their agricultural practices and is pivotal in planning the plants\u2019 treatment procedures. Although there are machine learning algorithms for this task, they are often validated over private datasets; therefore, their performance and generalization capabilities are virtually impossible to compare. We tackle this issue and introduce an open dataset including the hyperspectral and in situ ground-truth data, together with a validation procedure which is suggested to follow while investigating the emerging approaches for chlorophyll analysis with the use of our dataset. The experiments not only provided the solid baseline results obtained using 15 machine learning models over the introduced training-test dataset splits but also showed that it is possible to substantially improve the capabilities of the basic data-driven models. We believe that our work can become an important step toward standardizing the way the community validates algorithms for estimating chlorophyll-related parameters, and may be pivotal in consolidating the state of the art in the field by providing a clear and fair way of comparing new techniques over real data.<\/jats:p>","DOI":"10.3390\/rs14215526","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T03:53:07Z","timestamp":1667447587000},"page":"5526","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Unbiasing the Estimation of Chlorophyll from Hyperspectral Images: A Benchmark Dataset, Validation Procedure and Baseline Results"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1089-1778","authenticated-orcid":false,"given":"Bogdan","family":"Ruszczak","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering, Automatic Control and Informatics, Department of Informatics, Opole University of Technology, Pr\u00f3szkowska 76, 45-758 Opole, Poland"},{"name":"KP Labs, Konarskiego 18C, 44-100 Gliwice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6180-9979","authenticated-orcid":false,"given":"Agata M.","family":"Wijata","sequence":"additional","affiliation":[{"name":"KP Labs, Konarskiego 18C, 44-100 Gliwice, Poland"},{"name":"Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4026-1569","authenticated-orcid":false,"given":"Jakub","family":"Nalepa","sequence":"additional","affiliation":[{"name":"KP Labs, Konarskiego 18C, 44-100 Gliwice, Poland"},{"name":"Faculty of Automatic Control, Electronics and Computer Science, Department of Algorithmics and Software, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,2]]},"reference":[{"key":"ref_1","first-page":"2012","article-title":"Exploring the Potential of Spatially Downscaled Solar-Induced Chlorophyll Fluorescence to Monitor Drought Effects on Gross Primary Production in Winter Wheat","volume":"15","author":"Shen","year":"2022","journal-title":"IEEE J-STARS"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"48633","DOI":"10.1109\/ACCESS.2022.3168862","article-title":"Recognition of Drought Stress State of Tomato Seedling Based on Chlorophyll Fluorescence Imaging","volume":"10","author":"Long","year":"2022","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ol\u00e1h, V., Hepp, A., Irfan, M., and M\u00e9sz\u00e1ros, I. 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