{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T16:46:52Z","timestamp":1784134012934,"version":"3.55.0"},"reference-count":35,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T00:00:00Z","timestamp":1604880000000},"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>The advent of up-to-date hyperspectral technologies, and their increasing performance both spectrally and spatially, allows for new and exciting studies and practical applications in agriculture (soils and crops) and vegetation mapping and monitoring atregional (satellite platforms) andwithin-field (airplanes, drones and ground-based platforms) scales. Within this context, the special issue has included eleven international research studies using different hyperspectral datasets (from the Visible to the Shortwave Infrared spectral region) for agricultural soil, crop and vegetation modelling, mapping, and monitoring. Different classification methods (Support Vector Machine, Random Forest, Artificial Neural Network, Decision Tree) and crop canopy\/leaf biophysical parameters (e.g., chlorophyll content) estimation methods (partial least squares and multiple linear regressions) have been evaluated. Further, drone-based hyperspectral mapping by combining bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods are also examined. A review article on the recent advances of hyperspectral imaging technology and applications in agriculture is also included in this issue. The special issue is intended to help researchers and farmers involved in precision agriculture technology and practices to a better comprehension of strengths and limitations of the application of hyperspectral measurements for agriculture and vegetation monitoring. The studies published herein can be used by the agriculture and vegetation research and management communities to improve the characterization and evaluation of biophysical variables and processes, as well as for a more accurate prediction of plant nutrient using existing and forthcoming hyperspectral remote sensing technologies. <\/jats:p>","DOI":"10.3390\/rs12213665","type":"journal-article","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T14:10:41Z","timestamp":1605017441000},"page":"3665","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Special Issue \u201cHyperspectral Remote Sensing of Agriculture and Vegetation\u201d"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8311-8615","authenticated-orcid":false,"given":"Simone","family":"Pascucci","sequence":"first","affiliation":[{"name":"Institute of Methodologies for Environmental Analysis (CNR IMAA), National Research Council, 85050 Tito Scalo, PZ, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0587-8926","authenticated-orcid":false,"given":"Stefano","family":"Pignatti","sequence":"additional","affiliation":[{"name":"Institute of Methodologies for Environmental Analysis (CNR IMAA), National Research Council, 85050 Tito Scalo, PZ, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3091-7680","authenticated-orcid":false,"given":"Raffaele","family":"Casa","sequence":"additional","affiliation":[{"name":"Department of Agricultural and Forestry scieNcEs (DAFNE), Tuscia University Via San Camillo de Lellis, 01100 Viterbo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7512-0574","authenticated-orcid":false,"given":"Roshanak","family":"Darvishzadeh","sequence":"additional","affiliation":[{"name":"ITC\u2014Faculty of Geo-Information Science and Earth Observation, Department of Natural Resources, University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,9]]},"reference":[{"key":"ref_1","first-page":"101919","article-title":"Evaluating the performance of PROSPECT in the retrieval of leaf traits across canopy throughout the growing season","volume":"83","author":"Gara","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_2","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_3","first-page":"848","article-title":"Hyperspectral remote sensing of agriculture","volume":"108","author":"Sahoo","year":"2015","journal-title":"Curr. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ad\u00e3o, T., Hru\u0161ka, J., P\u00e1dua, L., Bessa, J., Peres, E., Morais, R., and Sousa, J. (2017). Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry. Remote Sens., 9.","DOI":"10.3390\/rs9111110"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.rse.2013.08.002","article-title":"Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission","volume":"139","author":"Mariotto","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.isprsjprs.2015.08.001","article-title":"Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation","volume":"108","author":"Marshall","year":"2015","journal-title":"ISPRS J. Photogramm."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Transon, J., d\u2019Andrimont, R., Maugnard, A., and Defourny, P. (2018). Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context. Remote Sens., 10.","DOI":"10.3390\/rs10020157"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1109\/LGRS.2013.2252877","article-title":"Spatial resolution effects on chlorophyll fluorescence retrieval in a heterogeneous canopy using hyperspectral imagery and radiative transfer simulation","volume":"10","author":"Suarez","year":"2013","journal-title":"IEEE Geosci. Remote Soc."},{"key":"ref_9","first-page":"1784","article-title":"Comparing the Performance of Multispectral and Hyperspectral Images for Estimating Vegetation Properties","volume":"12","author":"Lu","year":"2019","journal-title":"IEEE J. Stars"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Darvishzadeh, R., Wang, T., Skidmore, A.K., Vrieling, A., O\u2019Connor, B., Gara, T.W., Ens, B.J., and Marc, P. (2019). Analysis of Sentinel-2 and RapidEye for Retrieval of Leaf Area Index in a Saltmarsh Using a Radiative Transfer Model. Remote Sens., 11.","DOI":"10.3390\/rs11060671"},{"key":"ref_11","first-page":"58","article-title":"Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model","volume":"79","author":"Darvishzadeh","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","first-page":"1","article-title":"Mapping leaf area index in a mixed temperate forest using Fenix airborne hyperspectral data and Gaussian processes regression","volume":"95","author":"Xie","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.isprsjprs.2016.09.015","article-title":"Retrieval of forest leaf functional traits from HySpex imagery using radiative transfer models and continuous wavelet analysis","volume":"122","author":"Ali","year":"2016","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.geoderma.2012.12.016","article-title":"A comparison of sensor resolution and calibration strategies for soil texture estimation from hyperspectral remote sensing","volume":"197","author":"Casa","year":"2013","journal-title":"Geoderma"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.rse.2016.03.025","article-title":"Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon","volume":"179","author":"Castaldi","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"15561","DOI":"10.3390\/rs71115561","article-title":"Reducing the Influence of Soil Moisture on the Estimation of Clay from Hyperspectral Data: A Case Study Using Simulated PRISMA Data","volume":"7","author":"Castaldi","year":"2015","journal-title":"Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Castaldi, F., Chabrillat, S., Jones, A., Vreys, K., Bomans, B., and van Wesemael, B. (2018). Soil Organic Carbon Estimation in Croplands by Hyperspectral Remote APEX Data Using the LUCAS Topsoil Database. Remote Sens., 10.","DOI":"10.3390\/rs10020153"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Stafford, J.V. (2019). UAV-based hyperspectral imaging for weed discrimination in maize. Precision Agriculture \u201819, Wageningen Academic Publishers.","DOI":"10.3920\/978-90-8686-888-9"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yue, J., Feng, H., Yang, G., and Li, Z. (2018). A comparison of regression techniques for estimation of above-ground winter wheat biomass using near-surface spectroscopy. Remote Sens., 10.","DOI":"10.3390\/rs10010066"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"vzj2012.0201","DOI":"10.2136\/vzj2012.0201","article-title":"Geophysical and Hyperspectral Data Fusion Techniques for In-Field Estimation of Soil Properties","volume":"12","author":"Casa","year":"2013","journal-title":"Vadose Zone J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"43","DOI":"10.4081\/ija.2012.e43","article-title":"Potential of hyperspectral remote sensing for field scale soil mapping and precision agriculture applications","volume":"7","author":"Casa","year":"2012","journal-title":"Ital. J. Agron."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Upreti, D., Pignatti, S., Pascucci, S., Tolomio, M., Huang, W., and Casa, R. (2020). Bayesian Calibration of the Aquacrop-OS Model for Durum Wheat by Assimilation of Canopy Cover Retrieved from VEN\u00b5S Satellite Data. Remote Sens., 12.","DOI":"10.3390\/rs12162666"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.agrformet.2018.02.010","article-title":"Mapping forest canopy nitrogen content by inversion of coupled leaf-canopy radiative transfer models from airborne hyperspectral imagery","volume":"253","author":"Wang","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"8003","DOI":"10.1364\/AO.397844","article-title":"Using continous wavelet analysis for monitoring wheat yellow rust in different infestation stages based on unmanned aerial vehicle hyperspectral images","volume":"59","author":"Zheng","year":"2020","journal-title":"Appl. Opt."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Jiang, C., Chen, Y., Wu, H., Li, W., Zhou, H., Bo, Y., and Hyypp\u00e4, J. (2019). Study of a high spectral resolution hyperspectral LiDAR in vegetation red edge parameters extraction. Remote Sens., 11.","DOI":"10.3390\/rs11172007"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Xie, M., Wang, Z., Huete, A., Brown, L.A., Wang, H., Xie, Q., and Ding, Y. (2019). Estimating Peanut Leaf Chlorophyll Content with Dorsiventral Leaf Adjusted Indices: Minimizing the Impact of Spectral Differences between Adaxial and Abaxial Leaf Surfaces. Remote Sens., 11.","DOI":"10.3390\/rs11182148"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mirzaei, M., Verrelst, J., Marofi, S., Abbasi, M., and Azadi, H. (2019). Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis. Remote Sens., 11.","DOI":"10.3390\/rs11232731"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yan, Y., Deng, L., Liu, X., and Zhu, L. (2019). Application of UAV-Based Multi-Angle Hyperspectral Remote Sensing in Fine Vegetation Classification. Remote Sens., 11.","DOI":"10.3390\/rs11232753"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Osco, L.P., Ramos, A.P.M., Moriya, \u00c9.A.S., Bavaresco, L.G., Lima, B.C.D., Estrabis, N., and Imai, N.N. (2019). Modeling hyperspectral response of water-stress induced lettuce plants using artificial neural networks. Remote Sens., 11.","DOI":"10.3390\/rs11232797"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hennessy, A., Clarke, K., and Lewis, M. (2020). Hyperspectral Classification of Plants: A Review of Waveband Selection Generalisability. Remote Sens., 12.","DOI":"10.3390\/rs12010113"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Sabat-Tomala, A., Raczko, E., and Zagajewski, B. (2020). Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data. Remote Sens., 12.","DOI":"10.3390\/rs12030516"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Osco, L.P., Ramos, A.P.M., Faita Pinheiro, M.M., Moriya, \u00c9.A.S., Imai, N.N., Estrabis, N., and Li, J. (2020). A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements. Remote Sens., 12.","DOI":"10.3390\/rs12060906"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","unstructured":"Zhang, J., Sun, H., Gao, D., Qiao, L., Liu, N., Li, M., and Zhang, Y. (2020). Detection of Canopy Chlorophyll Content of Corn Based on Continuous Wavelet Transform Analysis. Remote Sens., 12.","DOI":"10.3390\/rs12172741"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Flynn, K.C., Frazier, A.E., and Admas, S. (2020). Nutrient Prediction for Tef (Eragrostis tef) Plant and Grain with Hyperspectral Data and Partial Least Squares Regression: Replicating Methods and Results across Environments. Remote Sens., 12.","DOI":"10.3390\/rs12182867"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/21\/3665\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:30:59Z","timestamp":1760178659000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/21\/3665"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,9]]},"references-count":35,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["rs12213665"],"URL":"https:\/\/doi.org\/10.3390\/rs12213665","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,9]]}}}