{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T02:57:23Z","timestamp":1774493843150,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T00:00:00Z","timestamp":1724630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"PGP Project: Pioneering to Precision by Ravendsown Limited"},{"name":"New Zealand Ministry for Primary Industries"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The ability to differentiate species is not adequate for modern forage breeding programs. The measurement of persistence is currently a bottleneck in the breeding system that limits the throughput of cultivars to the marketplace and prevents it from being selected as a trait. The use of hyperspectral data obtained through remote sensing offers the potential to reduce guesswork by identifying the distribution of pasture species, but only if such data alone can distinguish the subtle differences within species, i.e., cultivars. The implementation of this technology faces many challenges due to the spectral and temporal variability of species. To understand the spectral variability between and within species groups, differentiation using hyperspectral data from monoculture plots of turf species was utilized. Spectral data were collected over a year using an ASD FieldSpec\u00ae and canopy pasture probe (CAPP). The plots consisted of monocultures of various species, and cultivars (a total of 10 plots). Linear discriminant analysis (LDA) was conducted on the full spectrum and reduced band data. This technique successfully differentiated the species with high accuracy (&gt;98%). We demonstrate the potential of hyperspectral data and analysis techniques to accurately separate differences down to cultivar level. We also show that diurnal variation is measurable in the spectra but does not preclude differentiation.<\/jats:p>","DOI":"10.3390\/rs16173142","type":"journal-article","created":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T05:16:24Z","timestamp":1724649384000},"page":"3142","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Hyperspectral Data Can Differentiate Species and Cultivars of C3 and C4 Turf Despite Measurable Diurnal Variation"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5133-3870","authenticated-orcid":false,"given":"Thomas A.","family":"Cushnahan","sequence":"first","affiliation":[{"name":"School of Agriculture and Environment, College of Sciences, Massey University, Palmerston North 4442, New Zealand"},{"name":"AgResearch Ltd., Grasslands Research Centre, Private Bag 11008, Palmerston North 4410, New Zealand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4094-874X","authenticated-orcid":false,"given":"Miles C. E.","family":"Grafton","sequence":"additional","affiliation":[{"name":"School of Agriculture and Environment, College of Sciences, Massey University, Palmerston North 4442, New Zealand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8283-8936","authenticated-orcid":false,"given":"Diane","family":"Pearson","sequence":"additional","affiliation":[{"name":"School of Agriculture and Environment, College of Sciences, Massey University, Palmerston North 4442, New Zealand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8476-3619","authenticated-orcid":false,"given":"Thiagarajah","family":"Ramilan","sequence":"additional","affiliation":[{"name":"School of Agriculture and Environment, College of Sciences, Massey University, Palmerston North 4442, New Zealand"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"161","DOI":"10.33584\/jnzg.2018.80.339","article-title":"The measurement of perennial ryegrass persistence","volume":"80","author":"Dodd","year":"2018","journal-title":"J. N. Z. Grassl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1093\/jpe\/rtv077","article-title":"Observer error in vegetation surveys: A review","volume":"9","author":"Morrison","year":"2015","journal-title":"J. Plant Ecol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1109\/36.239916","article-title":"Solar zenith angle effects on forest canopy hemispherical reflectances calculated with a geometric-optical bidirectional reflectance model","volume":"31","author":"Schaaf","year":"1993","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/0034-4257(91)90071-D","article-title":"Solar zenith angle effects on vegetation indices in tallgrass prairie","volume":"38","author":"Middleton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1080\/01431168708954776","article-title":"Soil and sun angle interactions on partial canopy spectra","volume":"8","author":"Huete","year":"1987","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ma, X., Huete, A., Tran, N.N., Bi, J., Gao, S., and Zeng, Y. (2020). Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8. Remote Sens., 12.","DOI":"10.3390\/rs12081339"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.isprsjprs.2022.12.002","article-title":"Impact of sun-view geometry on canopy spectral reflectance variability","volume":"196","author":"Jafarbiglu","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TGRS.1983.350484","article-title":"Diurnal patterns of wheat spectral reflectances","volume":"2","author":"Pinter","year":"1983","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9458","DOI":"10.1073\/pnas.0914299107","article-title":"Circadian control of carbohydrate availability for growth in Arabidopsis plants at night","volume":"107","author":"Graf","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1146\/annurev.arplant.47.1.655","article-title":"Regulation of light harvesting in green plants","volume":"47","author":"Horton","year":"1996","journal-title":"Annu. Rev. Plant Physiol. Plant Mol. Biol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"259","DOI":"10.2307\/3546011","article-title":"Integrated screening validates primary axes of specialisation in plants","volume":"79","author":"Grime","year":"1997","journal-title":"Oikos"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1111\/j.1469-8137.2010.03536.x","article-title":"Sources of variability in canopy reflectance and the convergent properties of plants","volume":"189","author":"Ollinger","year":"2011","journal-title":"New Phytol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Girolamo-Neto, C.D., Sanches, I.D.A., Neves, A.K., Prudente, V.H.R., K\u00f6rting, T.S., Picoli, M.C.A., and Arag\u00e3o, L.E.O.e.C.d. (2019). Assessment of texture features for bermudagrass (cynodon dactylon) detection in sugarcane plantations. Drones, 3.","DOI":"10.3390\/drones3020036"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.rse.2013.07.025","article-title":"Phenology-assisted classification of C3 and C4 grasses in the U.S. Great Plains and their climate dependency with MODIS time series","volume":"138","author":"Wang","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_15","unstructured":"Lilienthal, H., Wilde, P., and Schnug, E. (August, January 31). Proximal hyperspectral sensing in plant breeding. Proceedings of the 13th International Conference on Precision Agriculture, St. Louis, MI, USA."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.tplants.2013.09.008","article-title":"Field high-throughput phenotyping: The new crop breeding frontier","volume":"19","author":"Araus","year":"2014","journal-title":"Trends Plant Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1093\/aob\/mcs026","article-title":"Advanced phenotyping offers opportunities for improved breeding of forage and turf species","volume":"110","author":"Walter","year":"2012","journal-title":"Ann. Bot."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ghamkhar, K. (2023). Phenomics for the Improvement of Crop Adaptation. Plant Genetic Resources for the 21st Century, Apple Academic Press. [1st ed.].","DOI":"10.1201\/9781003302957-10"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s11119-014-9376-3","article-title":"Turfgrass spectral reflectance: Simulating satellite monitoring of spectral signatures of main C3 and C4 species","volume":"16","author":"Caturegli","year":"2015","journal-title":"Precis. Agric"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5959","DOI":"10.1080\/01431160902791895","article-title":"Grass species differentiation through canopy hyperspectral reflectance","volume":"30","author":"Irisarri","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Caturegli, L., Corniglia, M., Gaetani, M., Grossi, N., Magni, S., Migliazzi, M., Angelini, L., Mazzoncini, M., Silvestri, N., and Fontanelli, M. (2016). Unmanned aerial vehicle to estimate nitrogen status of turfgrasses. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0158268"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1080\/01431161.2019.1641762","article-title":"Normalized Difference Vegetation Index versus Dark Green Colour Index to estimate nitrogen status on bermudagrass hybrid and tall fescue","volume":"41","author":"Caturegli","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Caturegli, L., Matteoli, S., Gaetani, M., Grossi, N., Magni, S., Minelli, A., Corsini, G., Remorini, D., and Volterrani, M. (2020). Effects of water stress on spectral reflectance of bermudagrass. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-72006-6"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Mar\u00edn, J., Yousfi, S., Mauri, P.V., Parra, L., Lloret, J., and Masaguer, A. (2020). RGB vegetation indices, NDVI, and biomass as indicators to evaluate C3 and C4 turfgrass under different water conditions. Sustainability, 12.","DOI":"10.3390\/su12062160"},{"key":"ref_25","unstructured":"Fotia, K., Ntoulas, N., Koliopanos, C., Tsirogiannis, I., and Nektarios, P. (2016, January 5\u22127). Utilization of reflectance indices to evaluate the impact of grey or recycled irrigation water on Festuca arundinacea turfgrass. Proceedings of the International Symposium on Sensing Plant Water Status-Methods and Applications in Horticultural Science 1197, Potsdam, Germany."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/S0034-4257(02)00196-7","article-title":"Spectral discrimination of vegetation types in a coastal wetland","volume":"85","author":"Schmidt","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"M\u00f6ckel, T., Dalmayne, J., Schmid, B., Prentice, H., and Hall, K. (2016). Airborne Hyperspectral Data Predict Fine-Scale Plant Species Diversity in Grazed Dry Grasslands. Remote Sens., 8.","DOI":"10.3390\/rs8020133"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.rse.2005.08.001","article-title":"Indicators of plant species richness in AVIRIS spectra of a mesic grassland","volume":"98","author":"Carter","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/S0034-4257(03)00069-5","article-title":"A comparison of three approaches for predicting C4 species cover of northern mixed grass prairie","volume":"86","author":"Davidson","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"9171","DOI":"10.1080\/01431161.2010.550646","article-title":"Mapping C3 and C4 plant functional types using separated solar-induced chlorophyll fluorescence from hyperspectral data","volume":"32","author":"Liu","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S0034-4257(02)00010-X","article-title":"Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and de-velopmental stages","volume":"81","author":"Sims","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_32","first-page":"175","article-title":"Energy relations and carbohydrate partitioning in turfgrasses","volume":"32","author":"Hull","year":"1992","journal-title":"Turfgrass"},{"key":"ref_33","unstructured":"Sanches, I.D.A. (2009). Hyperspectral Proximal Sensing of the Botanical Composition and Nutrient Content of New Zealand Pastures: A Thesis Presented in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy in Earth Science. [Ph.D. Thesis, Massey University]."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2309","DOI":"10.1080\/01431160802549377","article-title":"Large, durable and low-cost reflectance standard for field remote sensing applications","volume":"30","author":"Sanches","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2759","DOI":"10.1080\/01431160802555820","article-title":"Broadleaf species recognition with in situ hyperspectral data","volume":"30","author":"Pu","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lu, Y., Saeys, W., Kim, M., Peng, Y., and Lu, R. (2020). Hyperspectral imaging technology for quality and safety evaluation of horticultural products: A review and celebration of the past 20-year progress. Postharvest Biol. Technol., 170.","DOI":"10.1016\/j.postharvbio.2020.111318"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1366\/0003702894202201","article-title":"Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra","volume":"43","author":"Barnes","year":"1989","journal-title":"Appl. Spectrosc."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/0034-4257(94)90136-8","article-title":"Reflectance Indexes Associated with Physiological-Changes in Nitrogen-Limited and Water-Limited Sunflower Leaves","volume":"48","author":"Penuelas","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.biosystemseng.2021.12.008","article-title":"Exploring hyperspectral reflectance indices for the estimation of water and nitrogen status of spinach","volume":"214","author":"Rubo","year":"2022","journal-title":"Biosyst. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/S0034-4257(98)00032-7","article-title":"Derivative Analysis of Hyperspectral Data","volume":"66","author":"Tsai","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_41","unstructured":"Stevens, A., and Ramirez\u2013Lopez, L. (2024, August 19). An Introduction to the Prospectr Package.  R Package Vignette. 2014, Version 0.1.3. Available online: https:\/\/cran.r-project.org\/web\/packages\/prospectr\/index.html."},{"key":"ref_42","unstructured":"Welling, M. (2006). Fisher Linear Discriminant Analysis, Department of Computer Science, University of Toronto."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J.H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer. [2nd ed.].","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_44","unstructured":"Bhardwaj, A., and Verma, P. (2015). A Textbook on Pattern Recognition, Alpha Science International."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1109\/TNN.2002.806647","article-title":"Face recognition using LDA-based algorithms","volume":"14","author":"Lu","year":"2003","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1198\/016214502753479248","article-title":"Comparison of discrimination methods for the classification of tumors using gene expression data","volume":"97","author":"Dudoit","year":"2002","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Acquah, G.E., Via, B.K., Billor, N., Fasina, O.O., and Eckhardt, L.G. (2016). Identifying plant part composition of forest logging residue using infrared spectral data and linear discriminant analysis. Sensors, 16.","DOI":"10.3390\/s16091375"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/0003-2670(96)00142-0","article-title":"Comparison of regularized discriminant analysis linear discriminant analysis and quadratic discriminant analysis applied to NIR data","volume":"329","author":"Wu","year":"1996","journal-title":"Anal. Chim. Acta"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral remote sensing data analysis and future challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1109\/TGRS.2008.2005729","article-title":"Classification of hyperspectral images with regularized linear discriminant analysis","volume":"47","author":"Bandos","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Baier, D., Decker, R., and Schmidt-Thieme, L. (2005). klaR Analyzing German Business Cycles. Data Analysis and Decision Support, Springer.","DOI":"10.1007\/3-540-28397-8"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"8494","DOI":"10.3390\/rs6098494","article-title":"Plant Species Discrimination in a Tropical Wetland Using In Situ Hyperspectral Data","volume":"6","author":"Prospere","year":"2014","journal-title":"Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isprsjprs.2007.05.006","article-title":"A hyperspectral band selector for plant species discrimination","volume":"62","author":"Vaiphasa","year":"2007","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.rse.2005.12.011","article-title":"A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method","volume":"101","author":"Cho","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.rse.2006.06.010","article-title":"Intra- and inter-class spectral variability of tropical tree species at La Selva, Costa Rica: Implications for species identification using HYDICE imagery","volume":"105","author":"Zhang","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1037\/0033-2909.95.1.156","article-title":"Issues in the use and interpretation of discriminant analysis","volume":"95","author":"Huberty","year":"1984","journal-title":"Psychol. Bull."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/S0034-4257(98)00014-5","article-title":"Biophysical and Biochemical Sources of Variability in Canopy Reflectance","volume":"64","author":"Asner","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.geoderma.2005.04.025","article-title":"Global soil characterization with VNIR diffuse reflectance spectroscopy","volume":"132","author":"Brown","year":"2006","journal-title":"Geoderma"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the mean accuracy of statistical pattern recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"Inf. Theory IEEE Trans."},{"key":"ref_60","first-page":"4133","article-title":"Improving discrimination of savanna tree species through a multiple-endmember spectral angle mapper approach: Canopy-level analysis","volume":"48","author":"Cho","year":"2010","journal-title":"Geosci. Remote Sens. IEEE Trans."},{"key":"ref_61","unstructured":"Sobhan, I., Vaiphasa, C., and Skidmore, A. (2024, August 19). Spectral regions for maximizing species discrimination. Species Discrimination Hyperspectral Perspective, Available online: https:\/\/www.google.com\/search?q=Species+discrimination+from+a+hyperspectral+perspective&oq=Species+discrimination+from+a+hyperspectral+perspective&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIGCAEQRRg8MgYIAhBFGDzSAQczMjNqMGo0qAIAsAIB&sourceid=chrome&ie=UTF-8."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/0034-4257(90)90055-Q","article-title":"High resolution derivative spectra in remote sensing","volume":"33","author":"Steven","year":"1990","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Betteridge, K., and Haynes, D. (1986, January 1). Altering the growth pattern of kikuyu pastures with temperate grasses. Proceedings of the Proceedings of the New Zealand Grassland Association, Whangarei, New Zealand.","DOI":"10.33584\/jnzg.1986.47.1748"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1724","DOI":"10.1080\/01431161.2012.725958","article-title":"Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression","volume":"34","author":"Axelsson","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/0034-4257(89)90069-2","article-title":"Remote sensing of foliar chemistry","volume":"30","author":"Curran","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(92)90059-S","article-title":"A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency","volume":"41","author":"Gamon","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1016\/j.snb.2018.06.121","article-title":"Discrimination between abiotic and biotic drought stress in tomatoes using hyperspectral imaging","volume":"273","author":"Strajnar","year":"2018","journal-title":"Sens. Actuators B Chem."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/0034-4257(94)90013-2","article-title":"How unique are spectral signatures?","volume":"49","author":"Price","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_69","unstructured":"Sobhan, M.I. (2007). Species Discrimination from a Hyperspectral Perspective, Wageningen University and Research."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.isprsjprs.2014.01.010","article-title":"Spectral monitoring of moorland plant phenology to identify a temporal window for hyperspectral remote sensing of peatland","volume":"90","author":"Cole","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3142\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:42:48Z","timestamp":1760110968000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3142"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,26]]},"references-count":70,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16173142"],"URL":"https:\/\/doi.org\/10.3390\/rs16173142","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,26]]}}}