{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T05:05:41Z","timestamp":1772773541451,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,4,19]],"date-time":"2024-04-19T00:00:00Z","timestamp":1713484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"https:\/\/CarbonMapper.org"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the increasing availability and volume of remote sensing data, imaging spectroscopy is an expanding tool for agricultural studies. One of the fundamental applications in agricultural research is crop mapping and classification. Previous studies have mostly focused at local to regional scales, and classifications were usually performed for a limited number of crop types. Leveraging fine spatial resolution (60 cm) imaging spectroscopy data collected by the Global Airborne Observatory (GAO), we investigated canopy-level spectral variations in 16 crop species from different agricultural regions in the U.S. Inter-specific differences were quantified through principal component analysis (PCA) of crop spectra and their Euclidean distances in the PC space. We also classified the crop species using support vector machines (SVM), demonstrating high classification accuracy with a test kappa of 0.97. A separate test with an independent dataset also returned high accuracy (kappa = 0.95). Classification using full reflectance spectral data (320 bands) and selected optimal wavebands from the literature resulted in similar classification accuracies. We demonstrated that classification involving diverse crop species is achievable, and we encourage further testing based on moderate spatial resolution imaging spectrometer data.<\/jats:p>","DOI":"10.3390\/rs16081447","type":"journal-article","created":{"date-parts":[[2024,4,19]],"date-time":"2024-04-19T06:28:09Z","timestamp":1713508089000},"page":"1447","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Canopy-Level Spectral Variation and Classification of Diverse Crop Species with Fine Spatial Resolution Imaging Spectroscopy"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9848-6894","authenticated-orcid":false,"given":"Jie","family":"Dai","sequence":"first","affiliation":[{"name":"Center for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ 85287, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7617-888X","authenticated-orcid":false,"given":"Marcel","family":"K\u00f6nig","sequence":"additional","affiliation":[{"name":"Center for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ 85287, USA"}]},{"given":"Elahe","family":"Jamalinia","sequence":"additional","affiliation":[{"name":"Center for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ 85287, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1928-1442","authenticated-orcid":false,"given":"Kelly L.","family":"Hondula","sequence":"additional","affiliation":[{"name":"Center for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ 85287, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0428-2909","authenticated-orcid":false,"given":"Nicholas R.","family":"Vaughn","sequence":"additional","affiliation":[{"name":"Center for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ 85287, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5381-9533","authenticated-orcid":false,"given":"Joseph","family":"Heckler","sequence":"additional","affiliation":[{"name":"Center for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ 85287, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7893-6421","authenticated-orcid":false,"given":"Gregory P.","family":"Asner","sequence":"additional","affiliation":[{"name":"Center for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ 85287, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,19]]},"reference":[{"key":"ref_1","unstructured":"(2024, March 11). THE 17 GOALS|Sustainable Development. Available online: https:\/\/sdgs.un.org\/goals."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/07352689.2011.554355","article-title":"Environmental Impact of Different Agricultural Management Practices: Conventional vs. Organic Agriculture","volume":"30","author":"Gomiero","year":"2011","journal-title":"Crit. Rev. Plant Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","article-title":"Remote Sensing for Agricultural Applications: A Meta-Review","volume":"236","author":"Weiss","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6094","DOI":"10.1080\/01431161.2012.680617","article-title":"Crop Classification Modelling Using Remote Sensing and Environmental Data in the Greater Platte River Basin, USA","volume":"33","author":"Howard","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1080\/01431160600702673","article-title":"Suitable Remote Sensing Method and Data for Mapping and Measuring Active Crop Fields","volume":"28","author":"Xie","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"124905","DOI":"10.1016\/j.jhydrol.2020.124905","article-title":"A Review of Remote Sensing Applications in Agriculture for Food Security: Crop Growth and Yield, Irrigation, and Crop Losses","volume":"586","author":"Karthikeyan","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/0034-4257(91)90016-Y","article-title":"Global Land Cover Classification by Remote Sensing: Present Capabilities and Future Possibilities","volume":"35","author":"Townshend","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/S0034-4257(02)00136-0","article-title":"Estimating Impervious Surface Distribution by Spectral Mixture Analysis","volume":"84","author":"Wu","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2016.03.008","article-title":"Optical Remotely Sensed Time Series Data for Land Cover Classification: A Review","volume":"116","author":"White","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yi, Z., Jia, L., and Chen, Q. (2020). Crop Classification Using Multi-Temporal Sentinel-2 Data in the Shiyang River Basin of China. Remote Sens., 12.","DOI":"10.5194\/egusphere-egu2020-20926"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.rse.2018.11.032","article-title":"Deep Learning Based Multi-Temporal Crop Classification","volume":"221","author":"Zhong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"108106","DOI":"10.1016\/j.ecolind.2021.108106","article-title":"Remote Sensing of Spectral Diversity: A New Methodological Approach to Account for Spatio-Temporal Dissimilarities between Plant Communities","volume":"130","author":"Rossi","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"111218","DOI":"10.1016\/j.rse.2019.111218","article-title":"Remote Sensing of Terrestrial Plant Biodiversity","volume":"231","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1007\/s10712-019-09511-5","article-title":"Assessing Vegetation Function with Imaging Spectroscopy","volume":"40","author":"Gamon","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"S67","DOI":"10.1016\/j.rse.2008.10.019","article-title":"Retrieval of Foliar Information about Plant Pigment Systems from High Resolution Spectroscopy","volume":"113","author":"Ustin","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/S0034-4257(98)00064-9","article-title":"Imaging Spectroscopy and the Airborne Visible\/Infrared Imaging Spectrometer (AVIRIS)","volume":"65","author":"Green","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lu, B., Dao, P.D., Liu, J., He, Y., and Shang, J. (2020). Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture. Remote Sens., 12.","DOI":"10.3390\/rs12162659"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1007\/s10712-018-9492-0","article-title":"Spaceborne Imaging Spectroscopy for Sustainable Agriculture: Contributions and Challenges","volume":"40","author":"Hank","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Agilandeeswari, L., Prabukumar, M., Radhesyam, V., Phaneendra, K.L.N.B., and Farhan, A. (2022). Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images. Appl. Sci., 12.","DOI":"10.3390\/app12031670"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"715","DOI":"10.14358\/PERS.22-00039R2","article-title":"New Generation Hyperspectral Sensors DESIS and PRISMA Provide Improved Agricultural Crop Classifications","volume":"88","author":"Aneece","year":"2022","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1016\/j.isprsjprs.2011.05.001","article-title":"Use of Field Reflectance Data for Crop Mapping Using Airborne Hyperspectral Image","volume":"66","author":"Nidamanuri","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhao, Z., and Yin, C. (2022). Fine Crop Classification Based on UAV Hyperspectral Images and Random Forest. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11040252"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wei, L., Yu, M., Liang, Y., Yuan, Z., Huang, C., Li, R., and Yu, Y. (2019). Precise Crop Classification Using Spectral-Spatial-Location Fusion Based on Conditional Random Fields for UAV-Borne Hyperspectral Remote Sensing Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11172011"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/j.rse.2012.06.012","article-title":"Carnegie Airborne Observatory-2: Increasing Science Data Dimensionality via High-Fidelity Multi-Sensor Fusion","volume":"124","author":"Asner","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_27","unstructured":"(2024, March 11). ARMS III Farm Production Regions Map, Available online: https:\/\/www.nass.usda.gov\/Charts_and_Maps\/Farm_Production_Expenditures\/reg_map_c.php."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"113836","DOI":"10.1016\/j.rse.2023.113836","article-title":"A General Methodology for the Quantification of Crop Canopy Nitrogen across Diverse Species Using Airborne Imaging Spectroscopy","volume":"298","author":"Dai","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.rse.2014.11.011","article-title":"Quantifying Forest Canopy Traits: Imaging Spectroscopy versus Field Survey","volume":"158","author":"Asner","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Seeley, M.M., Martin, R.E., Vaughn, N.R., Thompson, D.R., Dai, J., and Asner, G.P. (2023). Quantifying the Variation in Reflectance Spectra of Metrosideros Polymorpha Canopies across Environmental Gradients. Remote Sens., 15.","DOI":"10.3390\/rs15061614"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1016\/j.jqsrt.2010.03.007","article-title":"Brightness-Normalized Partial Least Squares Regression for Hyperspectral Data","volume":"111","author":"Feilhauer","year":"2010","journal-title":"J. Quant. Spectrosc. Radiat. Transf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1002\/eap.1669","article-title":"The Spatial Sensitivity of the Spectral Diversity\u2013Biodiversity Relationship: An Experimental Test in a Prairie Grassland","volume":"28","author":"Wang","year":"2018","journal-title":"Ecol. Appl."},{"key":"ref_33","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach Learn"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1080\/01431160110040323","article-title":"An Assessment of Support Vector Machines for Land Cover Classification","volume":"23","author":"Huang","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1604","DOI":"10.1080\/2150704X.2015.1019015","article-title":"Comparison of Support Vector Machine, Artificial Neural Network, and Spectral Angle Mapper Algorithms for Crop Classification Using LISS IV Data","volume":"36","author":"Kumar","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1080\/15481603.2015.1114199","article-title":"A Support Vector Machine Classifier Based on a New Kernel Function Model for Hyperspectral Data","volume":"53","author":"Lin","year":"2016","journal-title":"GIScience Remote Sens."},{"key":"ref_38","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":"IEEE Trans. Inf. Theory"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1109\/JSTARS.2013.2252601","article-title":"Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Reflectance and Hyperion\/EO-1 Data","volume":"6","author":"Thenkabail","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Aneece, I., and Thenkabail, P. (2018). Accuracies Achieved in Classifying Five Leading World Crop Types and Their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine. Remote Sens., 10.","DOI":"10.3390\/rs10122027"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Aneece, I., and Thenkabail, P.S. (2021). Classifying Crop Types Using Two Generations of Hyperspectral Sensors (Hyperion and DESIS) with Machine Learning on the Cloud. Remote Sens., 13.","DOI":"10.3390\/rs13224704"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Czaplewski, R.L. (1994). Variance Approximations for Assessments of Classification Accuracy.","DOI":"10.2737\/RM-RP-316"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Jamalinia, E., Dai, J., Vaughn, N., Hondula, K., K\u00f6nig, M., Heckler, J., and Asner, G. (2023, January 16\u201321). Application of Imaging Spectroscopy to Quantify Fractional Cover Over Agricultural Lands. Proceedings of the IGARSS 2023\u20142023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA.","DOI":"10.1109\/IGARSS52108.2023.10283149"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2015.05.007","article-title":"Differentiating Plant Species within and across Diverse Ecosystems with Imaging Spectroscopy","volume":"167","author":"Roth","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"112303","DOI":"10.1016\/j.rse.2021.112303","article-title":"Hyperspectral Imagery to Monitor Crop Nutrient Status within and across Growing Seasons","volume":"255","author":"Liu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Cawse-Nicholson, K., Raiho, A.M., Thompson, D.R., Hulley, G.C., Miller, C.E., Miner, K.R., Poulter, B., Schimel, D., Schneider, F.D., and Townsend, P.A. (2022). Intrinsic Dimensionality as a Metric for the Impact of Mission Design Parameters. J. Geophys. Res. Biogeosci., 127.","DOI":"10.1029\/2022JG006876"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"044518","DOI":"10.1117\/1.JRS.16.044518","article-title":"Spectral Dimensionality of Imaging Spectroscopy Data over Diverse Landscapes and Spatial Resolutions","volume":"16","author":"Dai","year":"2022","journal-title":"J. Appl. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/8\/1447\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:30:56Z","timestamp":1760106656000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/8\/1447"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,19]]},"references-count":47,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["rs16081447"],"URL":"https:\/\/doi.org\/10.3390\/rs16081447","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,19]]}}}