{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T03:03:28Z","timestamp":1769915008568,"version":"3.49.0"},"reference-count":69,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T00:00:00Z","timestamp":1636329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000199","name":"United States Department of Agriculture","doi-asserted-by":"publisher","award":["2020-51181-32159"],"award-info":[{"award-number":["2020-51181-32159"]}],"id":[{"id":"10.13039\/100000199","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study aimed to identify the optimal sets of spectral bands for monitoring multiple grapevine nutrients in vineyards. We used spectral data spanning 400\u20132500 nm and leaf samples from 100 Concord grapevine canopies, lab-analyzed for six key nutrient values, to select the optimal bands for the nutrient regression models. The canopy spectral data were obtained with unmanned aerial systems (UAS), using push-broom imaging spectrometers (hyperspectral sensors). The novel use of UAS-based hyperspectral imagery to assess the grapevine nutrient status fills the gap between in situ spectral sampling and UAS-based multispectral imaging, avoiding their inherent trade-offs between spatial and spectral resolution. We found that an ensemble feature ranking method, utilizing six different machine learning feature selection methods, produced similar regression results as the standard PLSR feature selection and regression while generally selecting fewer wavelengths. We identified a set of biochemically consistent bands (606, 641, and 1494 nm) to predict the nitrogen content with an RMSE of 0.17% (using leave-one-out cross-validation) in samples with nitrogen contents ranging between 2.4 and 3.6%. Further studying is needed to confirm the relevance and consistency of the wavelengths selected for each nutrient model, but ensemble feature selection showed promise in identifying stable sets of wavelengths for assessing grapevine nutrient contents from canopy spectra.<\/jats:p>","DOI":"10.3390\/rs13214489","type":"journal-article","created":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T22:08:41Z","timestamp":1636409321000},"page":"4489","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7867-7674","authenticated-orcid":false,"given":"Robert","family":"Chancia","sequence":"first","affiliation":[{"name":"Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA"}]},{"given":"Terry","family":"Bates","sequence":"additional","affiliation":[{"name":"Horticulture Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA"},{"name":"Cornell Lake Erie Research and Extension Laboratory, Cornell AgriTech, Portland, NY 14769, USA"}]},{"given":"Justine","family":"Vanden Heuvel","sequence":"additional","affiliation":[{"name":"Horticulture Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA"}]},{"given":"Jan","family":"van Aardt","sequence":"additional","affiliation":[{"name":"Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,8]]},"reference":[{"key":"ref_1","unstructured":"National Agricultural Statistics Service (2021). 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