{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T01:33:25Z","timestamp":1777167205441,"version":"3.51.4"},"reference-count":58,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2014,3,27]],"date-time":"2014-03-27T00:00:00Z","timestamp":1395878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remotely sensed vegetation indices have been extensively used to quantify plant and soil characteristics. The objectives of this study were to: (i) compare vegetation indices developed at different scales for measuring canopy N content (g\u2219N\u2219m\u22122) and concentration (%); and (ii) evaluate the effects of soil background reflectance, cultivar, illumination and atmospheric conditions on the ability of vegetation indices to estimate canopy N content. Data were collected from two rainfed field sites cropped to wheat in Southern Italy (Foggia) and in Southeastern Australia (Horsham). From spectral readings, 25 vegetation indices were calculated. The Perpendicular Vegetation Index showed the best prediction of plant N concentration (%) (r2 = 0.81; standard error (SE) = 0.41%; p &lt; 0.001). The Canopy Chlorophyll Content Index showed the best predictive capability for canopy N content  (g\u2219N\u2219m\u22122) (r2 = 0.73; SE = 0.603; p &lt; 0.001). Canopy N content was best related to indices developed at the canopy scale and containing a red-edge wavelength. Canopy-scale indices were related to canopy N%, but such relationships needed to be normalized with biomass. Geographical location influenced mainly simple ratio or normalized indices, while indices that contained red-edge wavelengths were more robust and able to estimate canopy parameters more accurately.<\/jats:p>","DOI":"10.3390\/rs6042827","type":"journal-article","created":{"date-parts":[[2014,3,27]],"date-time":"2014-03-27T12:28:24Z","timestamp":1395923304000},"page":"2827-2844","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":83,"title":["Assessing the Robustness of Vegetation Indices to Estimate Wheat N in Mediterranean Environments"],"prefix":"10.3390","volume":"6","author":[{"given":"Davide","family":"Cammarano","sequence":"first","affiliation":[{"name":"Department of Agricultural & Biological Engineering, University of Florida, Gainesville,  FL 32611, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Glenn","family":"Fitzgerald","sequence":"additional","affiliation":[{"name":"Department of Primary Industries, 110 Natimuk Rd., Horsham 3401, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3091-7680","authenticated-orcid":false,"given":"Raffaele","family":"Casa","sequence":"additional","affiliation":[{"name":"Department of Crop Production, Universita' della Tuscia, Viterbo 01100, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2090-4616","authenticated-orcid":false,"given":"Bruno","family":"Basso","sequence":"additional","affiliation":[{"name":"Department of Geological Sciences, Michigan State University, East Lansing, MI 48824, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2014,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0034-4257(90)90051-M","article-title":"Multisite analyses of spectral-biophysical data for corn","volume":"33","author":"Wiegand","year":"1990","journal-title":"Remote Sens. 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