{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T18:27:59Z","timestamp":1779301679752,"version":"3.51.4"},"reference-count":76,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,11,20]],"date-time":"2019-11-20T00:00:00Z","timestamp":1574208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Heavy metal monitoring in food-producing ecosystems can play an important role in human health safety. Since they are able to interfere with plants\u2019 physiochemical characteristics, which influence the optical properties of leaves, they can be measured by in-field spectroscopy. In this study, the predictive power of spectroscopic data is examined. Five treatments of heavy metal stress (Cu, Zn, Pb, Cr, and Cd) were applied to grapevine seedlings and hyperspectral data (350\u20132500 nm), and heavy metal contents were collected based on in-field and laboratory experiments. The partial least squares (PLS) method was used as a feature selection technique, and multiple linear regressions (MLR) and support vector machine (SVM) regression methods were applied for modelling purposes. Based on the PLS results, the wavelengths in the vicinity of 2431, 809, 489, and 616 nm; 2032, 883, 665, 564, 688, and 437 nm; 1865, 728, 692, 683, and 356 nm; 863, 2044, 415, 652, 713, and 1036 nm; and 1373, 631, 744, and 438 nm were found most sensitive for the estimation of Cu, Zn, Pb, Cr, and Cd contents in the grapevine leaves, respectively. Therefore, visible and red-edge regions were found most suitable for estimating heavy metal contents in the present study. Heavy metals played a significant role in reforming the spectral pattern of stressed grapevine compared to healthy samples, meaning that in the best structures of the SVM regression models, the concentrations of Cu, Zn, Pb, Cr, and Cd were estimated with R2 rates of 0.56, 0.85, 0.71, 0.80, and 0.86 in the testing set, respectively. The results confirm the efficiency of in-field spectroscopy in estimating heavy metals content in grapevine foliage.<\/jats:p>","DOI":"10.3390\/rs11232731","type":"journal-article","created":{"date-parts":[[2019,11,20]],"date-time":"2019-11-20T11:06:03Z","timestamp":1574247963000},"page":"2731","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8924-7143","authenticated-orcid":false,"given":"Mohsen","family":"Mirzaei","sequence":"first","affiliation":[{"name":"Environmental Pollutions, Grape Environmental Science Department, Research Institute for Grapes and Raisin (RIGR), Malayer University, Malayer 65719-95863, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6313-2081","authenticated-orcid":false,"given":"Jochem","family":"Verrelst","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), Parc Cient\u00edfic, Universitat de Val\u00e8ncia, 46980 Paterna, Val\u00e8ncia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Safar","family":"Marofi","sequence":"additional","affiliation":[{"name":"Grape Environmental Science Department, Research Institute for Grapes and Raisin (RIGR), Malayer University &amp; Water Science Engineering Department, Bu-Ali Sina University, Hamedan 65178, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mozhgan","family":"Abbasi","sequence":"additional","affiliation":[{"name":"Faculty of Natural Resource and Earth Science, Shahrekord University, Shahrekord 8815648456, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5108-1993","authenticated-orcid":false,"given":"Hossein","family":"Azadi","sequence":"additional","affiliation":[{"name":"Department of Geography, Ghent University, 9000 Ghent, Belgium"},{"name":"Research Group Climate Change and Security, Institute of Geography, University of Hamburg, 20146 Hamburg, Germany"},{"name":"ISUMADECIP, Faculty of Environmental Science and Engineering, Babe\u015f-Bolyai University, 400084 Cluj-Napoca, Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, P., Huang, F., and Liu, X. 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