{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T21:37:24Z","timestamp":1775943444112,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T00:00:00Z","timestamp":1720137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Sciences and Engineering Research Council Canada","award":["RGPIN-2022-05288"],"award-info":[{"award-number":["RGPIN-2022-05288"]}]},{"name":"Natural Sciences and Engineering Research Council Canada","award":["WP2150"],"award-info":[{"award-number":["WP2150"]}]},{"name":"ESA\/SERCO IDEAS-QA4EO","award":["RGPIN-2022-05288"],"award-info":[{"award-number":["RGPIN-2022-05288"]}]},{"name":"ESA\/SERCO IDEAS-QA4EO","award":["WP2150"],"award-info":[{"award-number":["WP2150"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Optical satellite data products (e.g., Sentinel-2, PlanetScope, Landsat) require proper validation across diverse ecosystems. This has conventionally been achieved using airborne and more recently unmanned aerial vehicle (UAV) based hyperspectral sensors which constrain operations by both their cost and complexity of use. The MicaSense Altum is an accessible multispectral sensor that integrates a radiometric thermal camera with 5 bands (475 nm\u2013840 nm). In this work we assess the spectral reflectance accuracy of a UAV-mounted MicaSense Altum at 25, 50, 75, and 100 m AGL flight altitudes using the manufacturer provided panel-based reflectance conversion technique for atmospheric correction at the Mer Bleue peatland supersite near Ottawa, Canada. Altum derived spectral reflectance was evaluated through comparison of measurements of six known nominal reflectance calibration panels to in situ spectroradiometer and hyperspectral UAV reflectance products. We found that the Altum sensor saturates in the 475 nm band viewing the 18% reflectance panel, and for all brighter panels for the 475, 560, and 668 nm bands. The Altum was assessed against pre-classified hummock-hollow-lawn microtopographic features using band level pair-wise comparisons and common vegetation indices to investigate the sensor\u2019s viability as a validation tool of PlanetScope Dove 8 band and Sentinel-2A satellite products. We conclude that the use of the Altum needs careful consideration, and its field deployment and reflectance output does not meet the necessary cal\/val requirements in the peatland site.<\/jats:p>","DOI":"10.3390\/rs16132463","type":"journal-article","created":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T11:46:05Z","timestamp":1720179965000},"page":"2463","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Limitations of a Multispectral UAV Sensor for Satellite Validation and Mapping Complex Vegetation"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0356-9369","authenticated-orcid":false,"given":"Brendan","family":"Cottrell","sequence":"first","affiliation":[{"name":"Applied Remote Sensing Lab, Department of Geography, McGill University, Montreal, QC H3A 0B9, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1676-481X","authenticated-orcid":false,"given":"Margaret","family":"Kalacska","sequence":"additional","affiliation":[{"name":"Applied Remote Sensing Lab, Department of Geography, McGill University, Montreal, QC H3A 0B9, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0287-8960","authenticated-orcid":false,"given":"Juan-Pablo","family":"Arroyo-Mora","sequence":"additional","affiliation":[{"name":"National Research Council of Canada, Flight Research Laboratory, Ottawa, ON K1A 0R6, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2534-2977","authenticated-orcid":false,"given":"Oliver","family":"Lucanus","sequence":"additional","affiliation":[{"name":"Applied Remote Sensing Lab, Department of Geography, McGill University, Montreal, QC H3A 0B9, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7810-7356","authenticated-orcid":false,"given":"Deep","family":"Inamdar","sequence":"additional","affiliation":[{"name":"Applied Remote Sensing Lab, Department of Geography, McGill University, Montreal, QC H3A 0B9, Canada"}]},{"given":"Trond","family":"L\u00f8ke","sequence":"additional","affiliation":[{"name":"Norsk Elektro Optikk, 0667 Oslo, Norway"}]},{"given":"Raymond J.","family":"Soffer","sequence":"additional","affiliation":[{"name":"National Research Council of Canada, Flight Research Laboratory, Ottawa, ON K1A 0R6, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"de Castro, A.I., Shi, Y., Maja, J.M., and Pe\u00f1a, J.M. (2021). UAVs for Vegetation Monitoring: Overview and Recent Scientific Contributions. Remote Sens., 13.","DOI":"10.3390\/rs13112139"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1139\/as-2016-0008","article-title":"UAV Photogrammetry for Mapping Vegetation in the Low-Arctic","volume":"2","author":"Fraser","year":"2016","journal-title":"Arct. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.3390\/rs70101074","article-title":"UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis","volume":"7","author":"Feng","year":"2015","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yao, H., Qin, R., and Chen, X. (2019). Unmanned Aerial Vehicle for Remote Sensing Applications\u2014A Review. Remote Sens., 11.","DOI":"10.3390\/rs11121443"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1080\/07038992.1995.10874621","article-title":"Airborne Multispectral Digital Camera and Video Sensors: A Critical Review of System Designs and Applications","volume":"21","author":"King","year":"1995","journal-title":"Can. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.isprsjprs.2018.09.008","article-title":"UAV-Based Multispectral Remote Sensing for Precision Agriculture: A Comparison between Different Cameras","volume":"146","author":"Deng","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Abdollahnejad, A., and Panagiotidis, D. (2020). Tree Species Classification and Health Status Assessment for a Mixed Broadleaf-Conifer Forest with UAS Multispectral Imaging. Remote Sens., 12.","DOI":"10.3390\/rs12223722"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1080\/22797254.2018.1562848","article-title":"Using RPAS for the Detection of Archaeological Objects Using Multispectral and Thermal Imaging","volume":"52","author":"Raeva","year":"2019","journal-title":"Eur. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lynch, P., Blesius, L., and Hines, E. (2020). Classification of Urban Area Using Multispectral Indices for Urban Planning. Remote Sens., 12.","DOI":"10.3390\/rs12152503"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1080\/07038992.2019.1650334","article-title":"Validation of Airborne Hyperspectral Imagery from Laboratory Panel Characterization to Image Quality Assessment: Implications for an Arctic Peatland Surrogate Simulation Site","volume":"45","author":"Soffer","year":"2019","journal-title":"Can. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cao, H., Gu, X., Wei, X., Yu, T., and Zhang, H. (2020). Lookup Table Approach for Radiometric Calibration of Miniaturized Multispectral Camera Mounted on an Unmanned Aerial Vehicle. Remote Sens., 12.","DOI":"10.3390\/rs12244012"},{"key":"ref_12","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_13","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_14","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_15","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.isprsjprs.2019.01.016","article-title":"Radiometric Calibration Assessments for UAS-Borne Multispectral Cameras: Laboratory and Field Protocols","volume":"149","author":"Cao","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hakala, T., Markelin, L., Honkavaara, E., Scott, B., Theocharous, T., Nevalainen, O., N\u00e4si, R., Suomalainen, J., Viljanen, N., and Greenwell, C. (2018). Direct Reflectance Measurements from Drones: Sensor Absolute Radiometric Calibration and System Tests for Forest Reflectance Characterization. Sensors, 18.","DOI":"10.3390\/s18051417"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1016\/j.rse.2018.07.021","article-title":"Evaluation of Phenospectral Dynamics with Sentinel-2A Using a Bottom-up Approach in a Northern Ombrotrophic Peatland","volume":"216","author":"Kalacska","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1002\/2017RG000562","article-title":"Validation Practices for Satellite-Based Earth Observation Data across Communities","volume":"55","author":"Loew","year":"2017","journal-title":"Rev. Geophys."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"102875","DOI":"10.1016\/j.earscirev.2019.102875","article-title":"Advances in Quantitative Remote Sensing Product Validation: Overview and Current Status","volume":"196","author":"Wu","year":"2019","journal-title":"Earth-Sci. Rev."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, C. (2021). At-Sensor Radiometric Correction of a Multispectral Camera (RedEdge) for sUAS Vegetation Mapping. Sensors, 21.","DOI":"10.3390\/s21248224"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Mamaghani, B., and Salvaggio, C. (2019). Multispectral Sensor Calibration and Characterization for sUAS Remote Sensing. Sensors, 19.","DOI":"10.3390\/s19204453"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e26913","DOI":"10.1016\/j.heliyon.2024.e26913","article-title":"Crop Mapping in Smallholder Farms Using Unmanned Aerial Vehicle Imagery and Geospatial Cloud Computing Infrastructure","volume":"10","author":"Gokool","year":"2024","journal-title":"Heliyon"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Pe\u00f1a, R., V\u00e9lez, S., Vacas, R., Mart\u00edn, H., and \u00c1lvarez, S. (2023). Remote Sensing for Sustainable Pistachio Cultivation and Improved Quality Traits Evaluation through Thermal and Non-Thermal UAV Vegetation Indices. Appl. Sci., 13.","DOI":"10.3390\/app13137716"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Buunk, T., V\u00e9lez, S., Ariza-Sent\u00eds, M., and Valente, J. (2023). Comparing Nadir and Oblique Thermal Imagery in UAV-Based 3D Crop Water Stress Index Applications for Precision Viticulture with LiDAR Validation. Sensors, 23.","DOI":"10.3390\/s23208625"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1016\/j.jia.2022.12.007","article-title":"Combining the Critical Nitrogen Concentration and Machine Learning Algorithms to Estimate Nitrogen Deficiency in Rice from UAV Hyperspectral Data","volume":"22","author":"Yu","year":"2023","journal-title":"J. Integr. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chancia, R., Bates, T., Vanden Heuvel, J., 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_27","doi-asserted-by":"crossref","unstructured":"Zhang, X., Han, L., Dong, Y., Shi, Y., Huang, W., Han, L., Gonz\u00e1lez-Moreno, P., Ma, H., Ye, H., and Sobeih, T. (2019). A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images. Remote Sens., 11.","DOI":"10.3390\/rs11131554"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kuswidiyanto, L.W., Noh, H.-H., and Han, X. (2022). Plant Disease Diagnosis Using Deep Learning Based on Aerial Hyperspectral Images: A Review. Remote Sens., 14.","DOI":"10.3390\/rs14236031"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lafleur, P.M., Roulet, N.T., Bubier, J.L., Frolking, S., and Moore, T.R. (2003). Interannual Variability in the Peatland-Atmosphere Carbon Dioxide Exchange at an Ombrotrophic Bog. Glob. Biogeochem. Cycles, 17.","DOI":"10.1029\/2002GB001983"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s10533-015-0170-8","article-title":"Inter-Annual Variability in Water Table Depth Controls Net Ecosystem Carbon Dioxide Exchange in a Boreal Bog","volume":"127","author":"Strachan","year":"2016","journal-title":"Biogeochemistry"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3071","DOI":"10.1029\/2000JD900588","article-title":"Annual Cycle of CO2 Exchange at a Bog Peatland","volume":"106","author":"Lafleur","year":"2001","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1346","DOI":"10.1002\/eco.1731","article-title":"Ecohydrological Feedbacks in Peatlands: An Empirical Test of the Relationship among Vegetation, Microtopography and Water Table","volume":"9","author":"Malhotra","year":"2016","journal-title":"Ecohydrology"},{"key":"ref_33","unstructured":"(2024, March 26). CEOS Land Product Validation Subgroup, Available online: https:\/\/lpvs.gsfc.nasa.gov\/LPV_Supersites\/LPVsites.html."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1007\/s10021-008-9138-z","article-title":"Regular Surface Patterning of Peatlands: Confronting Theory with Field Data","volume":"11","author":"Eppinga","year":"2008","journal-title":"Ecosystems"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kalacska, M., Arroyo-Mora, J.P., Soffer, R.J., Roulet, N.T., Moore, T.R., Humphreys, E., Leblanc, G., Lucanus, O., and Inamdar, D. (2018). Estimating Peatland Water Table Depth and Net Ecosystem Exchange: A Comparison between Satellite and Airborne Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10050687"},{"key":"ref_36","unstructured":"(2023, May 12). User Guide for MicaSense Sensors. Available online: https:\/\/support.micasense.com\/hc\/en-us\/articles\/360039671254-User-Guide-for-MicaSense-Sensors."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Niro, F., Goryl, P., Dransfeld, S., Boccia, V., Gascon, F., Adams, J., Themann, B., Scifoni, S., and Doxani, G. (2021). European Space Agency (ESA) Calibration\/Validation Strategy for Optical Land-Imaging Satellites and Pathway towards Interoperability. Remote Sens., 13.","DOI":"10.3390\/rs13153003"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Arroyo-Mora, J.P., Kalacska, M., Soffer, R.J., and Lucanus, O. (2021, January 11\u201316). Comparison of Calibration Panels from Field Spectroscopy and UAV Hyperspectral Imagery Acquired Under Diffuse Illumination. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553791"},{"key":"ref_39","unstructured":"Soffer, R., Arroyo-Mora, J.P., Kalacska, M., Ifimov, G., and Leblanc, G. (2023, May 11). Mer Bleue QA4EO Airborne Hyperspectral Imagery. Borealis, V1. Available online: https:\/\/borealisdata.ca\/dataset.xhtml?persistentId=doi:10.5683\/SP3\/RMGOIW."},{"key":"ref_40","first-page":"64","article-title":"Real-Time Hyperspectral Image Processing for UAV Applications, Using HySpex Mjolnir-1024","volume":"10198","author":"Koirala","year":"2017","journal-title":"Proc. SPIE"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"(2023, May 11). ISPRS-Archives-Drone Data Atmospheric Correction Concept for Multi- and Hyperspectral Imagery\u2013The DROACOR Model. Available online: https:\/\/isprs-archives.copernicus.org\/articles\/XLIII-B3-2020\/473\/2020\/.","DOI":"10.5194\/isprs-archives-XLIII-B3-2020-473-2020"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"S92","DOI":"10.1016\/j.rse.2007.08.001","article-title":"Progress in Field Spectroscopy","volume":"113","author":"Milton","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Elmer, K., Soffer, R.J., Arroyo-Mora, J.P., and Kalacska, M. (2020). ASDToolkit: A Novel MATLAB Processing Toolbox for ASD Field Spectroscopy Data. Data, 5.","DOI":"10.20944\/preprints202008.0535.v1"},{"key":"ref_44","unstructured":"(2024, May 09). PySpectra\/PySpectra at Master \u00b7 Pmlrsg\/PySpectra \u00b7 GitHub. Available online: https:\/\/github.com\/pmlrsg\/PySpectra\/tree\/master\/PySpectra."},{"key":"ref_45","unstructured":"(2023, May 23). Open Access Hub. Available online: https:\/\/scihub.copernicus.eu\/."},{"key":"ref_46","unstructured":"(2023, May 23). Understanding PlanetScope Instruments. Available online: https:\/\/developers.planet.com\/docs\/apis\/data\/sensors\/."},{"key":"ref_47","unstructured":"(2023, May 23). PlanetScope ESA Archive-Earth Online. Available online: https:\/\/earth.esa.int\/eogateway\/catalog\/planetscope-esa-archive."},{"key":"ref_48","unstructured":"Soffer, R. (2014, January 13\u201318). Contamination of Ground Spectral Measurements Due to Operator Proximity. Session WE3.09: Calibration and Validation and standards in support of Spaceborne Imaging Spectroscopy Missions I, Paper 4152. Proceedings of the IEEE Geoscience And Remote Sensing Society 2014\/35th Canadian Symposium for Remote Ssensing, Quebec City, QC, Canada."},{"key":"ref_49","unstructured":"(2024, May 11). GitHub\u2014Micasense\/Imageprocessing: MicaSense RedEdge and Altum Image Processing Tutorials. Available online: https:\/\/github.com\/micasense\/imageprocessing."},{"key":"ref_50","unstructured":"(2023, May 23). ExifTool by Phil Harvey. Available online: https:\/\/exiftool.org\/."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"e1353691","DOI":"10.1155\/2017\/1353691","article-title":"Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Kalacska, M., Lucanus, O., Arroyo-Mora, J.P., Lalibert\u00e9, \u00c9., Elmer, K., Leblanc, G., and Groves, A. (2020). Accuracy of 3D Landscape Reconstruction without Ground Control Points Using Different UAS Platforms. Drones, 4.","DOI":"10.3390\/drones4020013"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"451","DOI":"10.5194\/isprs-archives-XLIII-B1-2020-451-2020","article-title":"high accuracy direct georeferencing of the altum multi-spectral uav camera and its application to high throughput plant phenotyping","volume":"XLIII-B1-2020","author":"Hutton","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_54","unstructured":"(2024, April 15). Best Practices: Collecting Data with MicaSense Sensors\u2013MicaSense Knowledge Base. Available online: https:\/\/support.micasense.com\/hc\/en-us\/articles\/224893167-Best-practices-Collecting-Data-with-MicaSense-Sensors."},{"key":"ref_55","unstructured":"(2024, April 15). Spectral Characteristics Viewer|Landsat Missions, Available online: https:\/\/landsat.usgs.gov\/spectral-characteristics-viewer."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Daniels, L., Eeckhout, E., Wieme, J., Dejaegher, Y., Audenaert, K., and Maes, W.H. (2023). Identifying the Optimal Radiometric Calibration Method for UAV-Based Multispectral Imaging. Remote Sens., 15.","DOI":"10.3390\/rs15112909"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Poncet, A.M., Knappenberger, T., Brodbeck, C., Fogle, M., Shaw, J.N., and Ortiz, B.V. (2019). Multispectral UAS Data Accuracy for Different Radiometric Calibration Methods. Remote Sens., 11.","DOI":"10.3390\/rs11161917"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zarzar, C., Dash, P., Dyer, J., Moorhead, R., and Hathcock, L. (2020). Development of a Simplified Radiometric Calibration Framework for Water-Based and Rapid Deployment Unmanned Aerial System (UAS) Operations. Drones, 4.","DOI":"10.20944\/preprints202003.0469.v1"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Tu, Y.-H., Phinn, S., Johansen, K., and Robson, A. (2018). Assessing Radiometric Correction Approaches for Multi-Spectral UAS Imagery for Horticultural Applications. Remote Sens., 10.","DOI":"10.20944\/preprints201809.0584.v1"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1139\/juvs-2018-0018","article-title":"Vegetation Monitoring Using Multispectral Sensors\u2014Best Practices and Lessons Learned from High Latitudes","volume":"7","author":"Assmann","year":"2019","journal-title":"J. Unmanned Veh. Sys."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Inamdar, D., Kalacska, M., Leblanc, G., and Arroyo-Mora, J.P. (2020). Characterizing and Mitigating Sensor Generated Spatial Correlations in Airborne Hyperspectral Imaging Data. Remote Sens., 12.","DOI":"10.3390\/rs12040641"},{"key":"ref_62","first-page":"101998","article-title":"Spatial Response Resampling (SR2): Accounting for the Spatial Point Spread Function in Hyperspectral Image Resampling","volume":"10","author":"Inamdar","year":"2023","journal-title":"Methods X"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1139\/dsa-2023-0003","article-title":"Optical Reflectance across Spatial Scales\u2014An Intercomparison of Transect-Based Hyperspectral, Drone, and Satellite Reflectance Data for Dry Season Rangeland","volume":"11","author":"Slade","year":"2023","journal-title":"Drone Syst. Appl."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Messina, G., Pe\u00f1a, J.M., Vizzari, M., and Modica, G. (2020). A Comparison of UAV and Satellites Multispectral Imagery in Monitoring Onion Crop. An Application in the \u2018Cipolla Rossa Di Tropea\u2019 (Italy). Remote Sens., 12.","DOI":"10.3390\/rs12203424"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1007\/s12524-020-01251-z","article-title":"Calibration of Satellite Imagery with Multispectral UAV Imagery","volume":"49","author":"Jain","year":"2021","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_66","first-page":"102396","article-title":"Integration of Satellite Imagery and in Situ Soil Moisture Data for Estimating Irrigation Water Requirements","volume":"102","author":"Ihuoma","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.agwat.2015.01.020","article-title":"UAVs Challenge to Assess Water Stress for Sustainable Agriculture","volume":"153","author":"Gago","year":"2015","journal-title":"Agric. Water Manag."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.rse.2011.10.007","article-title":"Fluorescence, Temperature and Narrow-Band Indices Acquired from a UAV Platform for Water Stress Detection Using a Micro-Hyperspectral Imager and a Thermal Camera","volume":"117","author":"Berni","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.3390\/rs4051392","article-title":"An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds","volume":"4","author":"Turner","year":"2012","journal-title":"Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Khaliq, A., Comba, L., Biglia, A., Ricauda Aimonino, D., Chiaberge, M., and Gay, P. (2019). Comparison of Satellite and UAV-Based Multispectral Imagery for Vineyard Variability Assessment. Remote Sens., 11.","DOI":"10.3390\/rs11040436"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1007\/s11119-021-09861-4","article-title":"Determining Nitrogen Deficiencies for Maize Using Various Remote Sensing Indices","volume":"23","author":"Burns","year":"2022","journal-title":"Precis. Agric."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1002\/agj2.20595","article-title":"Review on Unmanned Aerial Vehicles, Remote Sensors, Imagery Processing, and Their Applications in Agriculture","volume":"113","author":"Olson","year":"2021","journal-title":"Agron. J."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2463\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:10:35Z","timestamp":1760109035000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2463"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,5]]},"references-count":72,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["rs16132463"],"URL":"https:\/\/doi.org\/10.3390\/rs16132463","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,5]]}}}