{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T12:11:21Z","timestamp":1774267881618,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T00:00:00Z","timestamp":1661904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"DATI\u2014Digital Agriculture Technologies for Irrigation","award":["1585\/2020"],"award-info":[{"award-number":["1585\/2020"]}]},{"name":"DATI\u2014Digital Agriculture Technologies for Irrigation","award":["UIDB\/04033\/2020"],"award-info":[{"award-number":["UIDB\/04033\/2020"]}]},{"name":"EU","award":["1585\/2020"],"award-info":[{"award-number":["1585\/2020"]}]},{"name":"EU","award":["UIDB\/04033\/2020"],"award-info":[{"award-number":["UIDB\/04033\/2020"]}]},{"name":"FCT\u2014Portuguese Foundation for Science and Technology","award":["1585\/2020"],"award-info":[{"award-number":["1585\/2020"]}]},{"name":"FCT\u2014Portuguese Foundation for Science and Technology","award":["UIDB\/04033\/2020"],"award-info":[{"award-number":["UIDB\/04033\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Hyperspectral aerial imagery is becoming increasingly available due to both technology evolution and a somewhat affordable price tag. However, selecting a proper UAV + hyperspectral sensor combo to use in specific contexts is still challenging and lacks proper documental support. While selecting an UAV is more straightforward as it mostly relates with sensor compatibility, autonomy, reliability and cost, a hyperspectral sensor has much more to be considered. This note provides an assessment of two hyperspectral sensors (push-broom and snapshot) regarding practicality and suitability, within a precision viticulture context. The aim is to provide researchers, agronomists, winegrowers and UAV pilots with dependable data collection protocols and methods, enabling them to achieve faster processing techniques and helping to integrate multiple data sources. Furthermore, both the benefits and drawbacks of using each technology within a precision viticulture context are also highlighted. Hyperspectral sensors, UAVs, flight operations, and the processing methodology for each imaging type\u2019 datasets are presented through a qualitative and quantitative analysis. For this purpose, four vineyards in two countries were selected as case studies. This supports the extrapolation of both advantages and issues related with the two types of hyperspectral sensors used, in different contexts. Sensors\u2019 performance was compared through the evaluation of field operations complexity, processing time and qualitative accuracy of the results, namely the quality of the generated hyperspectral mosaics. The results shown an overall excellent geometrical quality, with no distortions or overlapping faults for both technologies, using the proposed mosaicking process and reconstruction. By resorting to the multi-site assessment, the qualitative and quantitative exchange of information throughout the UAV hyperspectral community is facilitated. In addition, all the major benefits and drawbacks of each hyperspectral sensor regarding its operation and data features are identified. Lastly, the operational complexity in the context of precision agriculture is also presented.<\/jats:p>","DOI":"10.3390\/s22176574","type":"journal-article","created":{"date-parts":[[2022,9,1]],"date-time":"2022-09-01T03:55:38Z","timestamp":1662004538000},"page":"6574","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["UAV-Based Hyperspectral Monitoring Using Push-Broom and Snapshot Sensors: A Multisite Assessment for Precision Viticulture Applications"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4533-930X","authenticated-orcid":false,"given":"Joaquim J.","family":"Sousa","sequence":"first","affiliation":[{"name":"Engineering Department, School of Science and Technology, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Centre for Robotics in Industry and Intelligent Systems (CRIIS), INESC Technology and Science (INESCTEC), 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9184-0707","authenticated-orcid":false,"given":"Piero","family":"Toscano","sequence":"additional","affiliation":[{"name":"Institute of BioEconomy, National Research Council (CNR-IBE), Via G. Caproni, 8, 50145 Florence, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8244-2985","authenticated-orcid":false,"given":"Alessandro","family":"Matese","sequence":"additional","affiliation":[{"name":"Institute of BioEconomy, National Research Council (CNR-IBE), Via G. Caproni, 8, 50145 Florence, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0065-1113","authenticated-orcid":false,"given":"Salvatore Filippo","family":"Di Gennaro","sequence":"additional","affiliation":[{"name":"Institute of BioEconomy, National Research Council (CNR-IBE), Via G. Caproni, 8, 50145 Florence, Italy"}]},{"given":"Andrea","family":"Berton","sequence":"additional","affiliation":[{"name":"Institute of Geosciences and Earth Resources, National Research Council (CNR-IGG), Via Moruzzi 1, 56124 Pisa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4195-7709","authenticated-orcid":false,"given":"Matteo","family":"Gatti","sequence":"additional","affiliation":[{"name":"Department of Sustainable Crop Production (DI.PRO.VE.S.), Universit\u00e0 Cattolica del Sacro Cuore, Via E. Parmense 84, 29122 Piacenza, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7238-2613","authenticated-orcid":false,"given":"Stefano","family":"Poni","sequence":"additional","affiliation":[{"name":"Department of Sustainable Crop Production (DI.PRO.VE.S.), Universit\u00e0 Cattolica del Sacro Cuore, Via E. Parmense 84, 29122 Piacenza, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7570-9773","authenticated-orcid":false,"given":"Lu\u00eds","family":"P\u00e1dua","sequence":"additional","affiliation":[{"name":"Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]},{"given":"Jon\u00e1\u0161","family":"Hru\u0161ka","sequence":"additional","affiliation":[{"name":"Engineering Department, School of Science and Technology, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2440-9153","authenticated-orcid":false,"given":"Raul","family":"Morais","sequence":"additional","affiliation":[{"name":"Engineering Department, School of Science and Technology, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5669-7976","authenticated-orcid":false,"given":"Emanuel","family":"Peres","sequence":"additional","affiliation":[{"name":"Engineering Department, School of Science and Technology, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,31]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/S0169-1368(99)00007-4","article-title":"Remote Sensing for Mineral Exploration","volume":"14","author":"Sabins","year":"1999","journal-title":"Ore Geol. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Pascucci, S., Pignatti, S., Casa, R., Darvishzadeh, R., and Huang, W. (2020). Special Issue \u201cHyperspectral Remote Sensing of Agriculture and Vegetation\u201d. Remote Sens., 12.","DOI":"10.3390\/rs12213665"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"919","DOI":"10.20870\/oeno-one.2020.54.4.4028","article-title":"Comparison between Satellite and Ground Data with UAV-Based Information to Analyse Vineyard Spatio-Temporal Variability","volume":"54","author":"Pastonchi","year":"2020","journal-title":"Oeno One"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"P\u00e1dua, L., Marques, P., Martins, L., Sousa, A., Peres, E., and Sousa, J.J. (2020). Monitoring of Chestnut Trees Using Machine Learning Techniques Applied to UAV-Based Multispectral Data. Remote Sens., 12.","DOI":"10.3390\/rs12183032"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Di Gennaro, S.F., Dainelli, R., Palliotti, A., Toscano, P., and Matese, A. (2019). Sentinel-2 Validation for Spatial Variability Assessment in Overhead Trellis System Viticulture versus UAV and Agronomic Data. Remote Sens., 11.","DOI":"10.3390\/rs11212573"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jiao, L., Sun, W., Yang, G., Ren, G., and Liu, Y. (2019). A Hierarchical Classification Framework of Satellite Multispectral\/Hyperspectral Images for Mapping Coastal Wetlands. Remote Sens., 11.","DOI":"10.3390\/rs11192238"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"P\u00e1dua, L., Ant\u00e3o-Geraldes, A.M., Sousa, J.J., Rodrigues, M.\u00c2., Oliveira, V., Santos, D., Miguens, M.F.P., and Castro, J.P. (2022). Water Hyacinth (Eichhornia Crassipes) Detection Using Coarse and High Resolution Multispectral Data. Drones, 6.","DOI":"10.3390\/drones6020047"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Song, A., Choi, J., Han, Y., and Kim, Y. (2018). Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks. Remote Sens., 10.","DOI":"10.3390\/rs10111827"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"P\u00e1dua, L., Guimar\u00e3es, N., Ad\u00e3o, T., Sousa, A., Peres, E., and Sousa, J.J. (2020). Effectiveness of Sentinel-2 in Multi-Temporal Post-Fire Monitoring When Compared with UAV Imagery. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9040225"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Luo, H., Zhang, P., Wang, J., Wang, G., and Meng, F. (2019). Traffic Patrolling Routing Problem with Drones in an Urban Road System. Sensors, 19.","DOI":"10.3390\/s19235164"},{"key":"ref_12","unstructured":"Campbell, J.B., and Wynne, R.H. (2011). Introduction to Remote Sensing, Guilford Press."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Guimar\u00e3es, N., P\u00e1dua, L., Marques, P., Silva, N., Peres, E., and Sousa, J.J. (2020). Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities. Remote Sens., 12.","DOI":"10.3390\/rs12061046"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Pabian, F.V., Renda, G., Jungwirth, R., Kim, L.K., Wolfart, E., Cojazzi, G.G., and Janssens, W.A. (2020). Commercial Satellite Imagery: An Evolving Tool in the Non-Proliferation Verification and Monitoring Toolkit. Nuclear Non-proliferation and Arms Control Verification, Springer.","DOI":"10.1007\/978-3-030-29537-0_24"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2349","DOI":"10.1080\/01431161.2017.1297548","article-title":"UAS, Sensors, and Data Processing in Agroforestry: A Review towards Practical Applications","volume":"38","author":"Vanko","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2781","DOI":"10.1109\/JSTARS.2021.3059451","article-title":"A Hyperspectral Image Classification Method Using Multifeature Vectors and Optimized KELM","volume":"14","author":"Chen","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","unstructured":"(2020, September 27). PrecisionHawk Beyond the Edge-How Advanced Drones, Sensors, and Flight Operations Are Redefining the Limits of Remote Sensing. Available online: https:\/\/www.precisionhawk.com\/sensors\/advanced-sensors-and-data-collection\/."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"An, Z., Wang, X., Li, B., Xiang, Z., and Zhang, B. (2022). Robust Visual Tracking for UAVs with Dynamic Feature Weight Selection. Appl. Intell., 1\u201314.","DOI":"10.1007\/s10489-022-03719-6"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2721","DOI":"10.1038\/s41598-021-81652-3","article-title":"Beyond the Traditional NDVI Index as a Key Factor to Mainstream the Use of UAV in Precision Viticulture","volume":"11","author":"Matese","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.procs.2017.11.055","article-title":"Very High Resolution Aerial Data to Support Multi-Temporal Precision Agriculture Information Management","volume":"121","author":"Sousa","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1126\/science.228.4704.1147","article-title":"Imaging Spectrometry for Earth Remote Sensing","volume":"228","author":"Goetz","year":"1985","journal-title":"Science"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ball, D.W. (2001). The Basics of Spectroscopy, Spie Press.","DOI":"10.1117\/3.422981"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Grahn, H., and Geladi, P. (2007). Techniques and Applications of Hyperspectral Image Analysis, John Wiley & Sons.","DOI":"10.1002\/9780470010884"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ad\u00e3o, T., Hru\u0161ka, J., P\u00e1dua, L., Bessa, J., Peres, E., Morais, R., and Sousa, J.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_25","first-page":"102856","article-title":"Remote Sensing Image Fusion on 3D Scenarios: A Review of Applications for Agriculture and Forestry","volume":"112","author":"Jurado","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_26","first-page":"19","article-title":"Hyperspectral Imaging Systems","volume":"1","author":"Kerekes","year":"2007","journal-title":"Hyperspectral Data Exploit. Theory Appl."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Aasen, H., Honkavaara, E., Lucieer, A., and Zarco-Tejada, P.J. (2018). Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows. Remote Sens., 10.","DOI":"10.3390\/rs10071091"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.isprsjprs.2018.11.025","article-title":"Generating a Hyperspectral Digital Surface Model Using a Hyperspectral 2D Frame Camera","volume":"147","author":"Oliveira","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","unstructured":"Tommaselli, A.M., Oliveira, R.A., Nagai, L.Y., Imai, N.N., Miyoshi, G.T., Honkavaara, E., and Hakala, T. (2015). Assessment of Bands Coregistration of a Light-Weight Spectral Frame Camera for UAV. GeoUAV-ISPRS Geospat. Week, 192."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5006","DOI":"10.3390\/rs5105006","article-title":"Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture","volume":"5","author":"Honkavaara","year":"2013","journal-title":"Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Jakob, S., Zimmermann, R., and Gloaguen, R. (2017). The Need for Accurate Geometric and Radiometric Corrections of Drone-Borne Hyperspectral Data for Mineral Exploration: Mephysto\u2014A Toolbox for Pre-Processing Drone-Borne Hyperspectral Data. Remote Sens., 9.","DOI":"10.3390\/rs9010088"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"17450","DOI":"10.1038\/s41598-020-74422-0","article-title":"Detection of REEs with Lightweight UAV-Based Hyperspectral Imaging","volume":"10","author":"Booysen","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1007\/s11119-021-09790-2","article-title":"Forage Yield and Quality Estimation by Means of UAV and Hyperspectral Imaging","volume":"22","author":"Geipel","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chancia, R., Bates, T., Heuvel, J.V., and van Aardt, J. (2021). Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13214489"},{"key":"ref_35","unstructured":"\u010cerven\u00e1, L., Pinlov\u00e1, G., Lhot\u00e1kov\u00e1, Z., Neuwirthov\u00e1, E., Kupkov\u00e1, L., Pot\u016f\u010dkov\u00e1, M., Lys\u00e1k, J., Campbell, P., and Albrechtov\u00e1, J. (2022, January 6\u201311). Determination of Chlorophyll Content in Selected Grass Communities of KRKONO\u0160E Mts. Tundra Based on Laboratory Spectroscopy and Aerial Hyperspectral data. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Nice, France."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ge, X., Ding, J., Jin, X., Wang, J., Chen, X., Li, X., Liu, J., and Xie, B. (2021). Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region. Remote Sens., 13.","DOI":"10.3390\/rs13081562"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Vanegas, F., Bratanov, D., Powell, K., Weiss, J., and Gonzalez, F. (2018). A Novel Methodology for Improving Plant Pest Surveillance in Vineyards and Crops Using UAV-Based Hyperspectral and Spatial Data. Sensors, 18.","DOI":"10.3390\/s18010260"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Fan, J., Zhou, J., Wang, B., de Leon, N., Kaeppler, S.M., Lima, D.C., and Zhang, Z. (2022). Estimation of Maize Yield and Flowering Time Using Multi-Temporal UAV-Based Hyperspectral Data. Remote Sens., 14.","DOI":"10.3390\/rs14133052"},{"key":"ref_39","first-page":"102414","article-title":"Combining UAV-Based Hyperspectral and LiDAR Data for Mangrove Species Classification Using the Rotation Forest Algorithm","volume":"102","author":"Cao","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"106789","DOI":"10.1016\/j.ecss.2020.106789","article-title":"UAV-Mounted Hyperspectral Mapping of Intertidal Macroalgae","volume":"242","author":"Rossiter","year":"2020","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Di Gennaro, S.F., Toscano, P., Gatti, M., Poni, S., Berton, A., and Matese, A. (2022). Spectral Comparison of UAV-Based Hyper and Multispectral Cameras for Precision Viticulture. Remote Sens., 14.","DOI":"10.3390\/rs14030449"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"8722","DOI":"10.3389\/fpls.2022.898722","article-title":"Assessing Grapevine Biophysical Parameters From Unmanned Aerial Vehicles Hyperspectral Imagery","volume":"13","author":"Matese","year":"2022","journal-title":"Front. Plant Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"106298","DOI":"10.1016\/j.compag.2021.106298","article-title":"Detection and Mapping of Trees Infected with Citrus Gummosis Using UAV Hyperspectral Data","volume":"188","author":"Moriya","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"400","DOI":"10.3390\/agriengineering4020027","article-title":"Prediction of Potassium in Peach Leaves Using Hyperspectral Imaging and Multivariate Analysis","volume":"4","author":"Abenina","year":"2022","journal-title":"AgriEngineering"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"109160","DOI":"10.1016\/j.ecolind.2022.109160","article-title":"Multi-Source Remote Sensing Recognition of Plant Communities at the Reach Scale of the Vistula River, Poland","volume":"142","author":"Demarchi","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"N\u00e4si, R., Viljanen, N., Kaivosoja, J., Alhonoja, K., Hakala, T., Markelin, L., and Honkavaara, E. (2018). Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features. Remote Sens., 10.","DOI":"10.3390\/rs10071082"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Tao, H., Feng, H., Xu, L., Miao, M., Yang, G., Yang, X., and Fan, L. (2020). Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images. Sensors, 20.","DOI":"10.3390\/s20041231"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Yue, J., Feng, H., Jin, X., Yuan, H., Li, Z., Zhou, C., Yang, G., and Tian, Q. (2018). A Comparison of Crop Parameters Estimation Using Images from UAV-Mounted Snapshot Hyperspectral Sensor and High-Definition Digital Camera. Remote Sens., 10.","DOI":"10.3390\/rs10071138"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Shin, J.-I., Cho, Y.-M., Lim, P.-C., Lee, H.-M., Ahn, H.-Y., Park, C.-W., and Kim, T. (2020). Relative Radiometric Calibration Using Tie Points and Optimal Path Selection for UAV Images. Remote Sens., 12.","DOI":"10.3390\/rs12111726"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Guo, Y., Senthilnath, J., Wu, W., Zhang, X., Zeng, Z., and Huang, H. (2019). Radiometric Calibration for Multispectral Camera of Different Imaging Conditions Mounted on a UAV Platform. Sustainability, 11.","DOI":"10.3390\/su11040978"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Xu, K., Gong, Y., Fang, S., Wang, K., Lin, Z., and Wang, F. (2019). Radiometric Calibration of UAV Remote Sensing Image with Spectral Angle Constraint. Remote Sens., 11.","DOI":"10.3390\/rs11111291"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Yuan, H., Yang, G., Li, C., Wang, Y., Liu, J., Yu, H., Feng, H., Xu, B., Zhao, X., and Yang, X. (2017). Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models. Remote Sens., 9.","DOI":"10.3390\/rs9040309"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1088\/0022-3735\/16\/4\/013","article-title":"Instrument Function for Ebert and Czerny-Turner Scanning Monochromators Used with Long Straight Slits","volume":"16","author":"Kay","year":"1983","journal-title":"J. Phys. E Sci. Instrum."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.isprsjprs.2015.08.002","article-title":"Generating 3D Hyperspectral Information with Lightweight UAV Snapshot Cameras for Vegetation Monitoring: From Camera Calibration to Quality Assurance","volume":"108","author":"Aasen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2630","DOI":"10.1109\/JSTARS.2014.2329891","article-title":"Spectral Calibration of Hyperspectral Data Observed from a Hyperspectrometer Loaded on an Unmanned Aerial Vehicle Platform","volume":"7","author":"Liu","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Yang, G., Li, C., Wang, Y., Yuan, H., Feng, H., Xu, B., and Yang, X. (2017). The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager. Remote Sens., 9.","DOI":"10.3390\/rs9070642"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Barreto, M.A.P., Johansen, K., Angel, Y., and McCabe, M.F. (2019). Radiometric Assessment of a UAV-Based Push-Broom Hyperspectral Camera. Sensors, 19.","DOI":"10.3390\/s19214699"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1876","DOI":"10.1109\/JSTARS.2015.2422716","article-title":"A Simplified Empirical Line Method of Radiometric Calibration for Small Unmanned Aircraft Systems-Based Remote Sensing","volume":"8","author":"Wang","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"F46","DOI":"10.1364\/AO.47.000F46","article-title":"Correction of Systematic Spatial Noise in Push-Broom Hyperspectral Sensors: Application to CHRIS\/PROBA Images","volume":"47","author":"Alonso","year":"2008","journal-title":"Appl. Opt."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2653","DOI":"10.1080\/014311699211994","article-title":"The Use of the Empirical Line Method to Calibrate Remotely Sensed Data to Reflectance","volume":"20","author":"Smith","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.compag.2014.07.001","article-title":"Vicarious Radiometric Calibration of a Multispectral Sensor from an Aerial Trike Applied to Precision Agriculture","volume":"108","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_62","unstructured":"(2022, July 07). UgCS Ground Station Software | UgCS PC Mission Planning. Available online: https:\/\/www.ugcs.com\/."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.eja.2014.01.004","article-title":"Tree Height Quantification Using very High Resolution Imagery Acquired from an Unmanned Aerial Vehicle (UAV) and Automatic 3D Photo-Reconstruction Methods","volume":"55","author":"Angileri","year":"2014","journal-title":"Eur. J. Agron."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"11933","DOI":"10.3390\/rs70911933","article-title":"The Impact of the Calibration Method on the Accuracy of Point Clouds Derived Using Unmanned Aerial Vehicle Multi-View Stereopsis","volume":"7","author":"Harwin","year":"2015","journal-title":"Remote Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6574\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:21:01Z","timestamp":1760142061000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6574"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,31]]},"references-count":64,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22176574"],"URL":"https:\/\/doi.org\/10.3390\/s22176574","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,31]]}}}