{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T02:23:29Z","timestamp":1774923809348,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,18]],"date-time":"2021-08-18T00:00:00Z","timestamp":1629244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004052","name":"King Abdullah University of Science and Technology","doi-asserted-by":"publisher","award":["URF\/1\/2550-1"],"award-info":[{"award-number":["URF\/1\/2550-1"]}],"id":[{"id":"10.13039\/501100004052","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004052","name":"King Abdullah University of Science and Technology","doi-asserted-by":"publisher","award":["URF\/1\/3413-01"],"award-info":[{"award-number":["URF\/1\/3413-01"]}],"id":[{"id":"10.13039\/501100004052","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The miniaturization of thermal infrared sensors suitable for integration with unmanned aerial vehicles (UAVs) has provided new opportunities to observe surface temperature at ultra-high spatial and temporal resolutions. In parallel, there has been a rapid development of software capable of streamlining the generation of orthomosaics. However, these approaches were developed to process optical and multi-spectral image data and were not designed to account for the often rapidly changing surface characteristics inherent in the collection and processing of thermal data. Although radiometric calibration and shutter correction of uncooled sensors have improved, the processing of thermal image data remains difficult due to (1) vignetting effects on the uncooled microbolometer focal plane array; (2) inconsistencies between images relative to in-flight effects (wind-speed and direction); (3) unsuitable methods for thermal infrared orthomosaic generation. Here, we use thermal infrared UAV data collected with a FLIR-based TeAx camera over an agricultural field at different times of the day to assess inconsistencies in orthophotos and their impact on UAV-based thermal infrared orthomosaics. Depending on the wind direction and speed, we found a significant difference in UAV-based surface temperature (up to 2 \u00b0C) within overlapping areas of neighboring flight lines, with orthophotos collected with tail wind being systematically cooler than those with head wind. To address these issues, we introduce a new swath-based mosaicking approach, which was compared to three standard blending modes for orthomosaic generation. The swath-based mosaicking approach improves the ability to identify rapid changes of surface temperature during data acquisition, corrects for the influence of flight direction relative to the wind orientation, and provides uncertainty (pixel-based standard deviation) maps to accompany the orthomosaic of surface temperature. It also produced more accurate temperature retrievals than the other three standard orthomosaicking methods, with a root mean square error of 1.2 \u00b0C when assessed against in situ measurements. As importantly, our findings demonstrate that thermal infrared data require appropriate processing to reduce inconsistencies between observations, and thus, improve the accuracy and utility of orthomosaics.<\/jats:p>","DOI":"10.3390\/rs13163255","type":"journal-article","created":{"date-parts":[[2021,8,18]],"date-time":"2021-08-18T22:51:00Z","timestamp":1629327060000},"page":"3255","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Overcoming the Challenges of Thermal Infrared Orthomosaics Using a Swath-Based Approach to Correct for Dynamic Temperature and Wind Effects"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2964-6409","authenticated-orcid":false,"given":"Yoann","family":"Malb\u00e9teau","sequence":"first","affiliation":[{"name":"Hydrology, Agriculture and Land Observation Group, Water Desalination and Reuse Center, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia"},{"name":"VanderSat, Wilhelminastraat 43a, 2011 VK Haarlem, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1889-9336","authenticated-orcid":false,"given":"Kasper","family":"Johansen","sequence":"additional","affiliation":[{"name":"Hydrology, Agriculture and Land Observation Group, Water Desalination and Reuse Center, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6768-2494","authenticated-orcid":false,"given":"Bruno","family":"Aragon","sequence":"additional","affiliation":[{"name":"Hydrology, Agriculture and Land Observation Group, Water Desalination and Reuse Center, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia"},{"name":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA"}]},{"given":"Samir K.","family":"Al-Mashhawari","sequence":"additional","affiliation":[{"name":"Hydrology, Agriculture and Land Observation Group, Water Desalination and Reuse Center, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1279-5272","authenticated-orcid":false,"given":"Matthew F.","family":"McCabe","sequence":"additional","affiliation":[{"name":"Hydrology, Agriculture and Land Observation Group, Water Desalination and Reuse Center, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,18]]},"reference":[{"key":"ref_1","first-page":"254","article-title":"Digital agriculture to design sustainable agricultural systems","volume":"3","author":"Basso","year":"2020","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1126\/science.1183899","article-title":"Precision Agriculture and Food Security","volume":"327","author":"Gebbers","year":"2010","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"111470","DOI":"10.1016\/j.rse.2019.111470","article-title":"No Pixel Left behind: Toward Integrating Earth Observations for Agriculture into the United Nations Sustainable Development Goals Framework","volume":"235","author":"Whitcraft","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3879","DOI":"10.5194\/hess-21-3879-2017","article-title":"The future of Earth observation in hydrology","volume":"21","author":"McCabe","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tmu\u0161i\u0107, G., Manfreda, S., Aasen, H., James, M.R., Gon\u00e7alves, G., Ben-Dor, E., Brook, A., Polinova, M., Arranz, J.J., and M\u00e9sz\u00e1ros, J. (2020). Current Practices in UAS-based Environmental Monitoring. Remote Sens., 12.","DOI":"10.3390\/rs12061001"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2014.02.013","article-title":"Unmanned Aerial Systems for Photogrammetry and Remote Sensing: A Review","volume":"92","author":"Colomina","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1890\/120150","article-title":"Lightweight Unmanned Aerial Vehicles will Revolutionize Spatial Ecology","volume":"11","author":"Anderson","year":"2013","journal-title":"Front. Ecol. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Vasterling, M., and Meyer, U. (2013). Challenges and Opportunities for UAV-Borne Thermal Imaging. Thermal Infrared Remote Sensing: Sensors, Methods, Applications, Springer.","DOI":"10.1007\/978-94-007-6639-6_4"},{"key":"ref_9","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 Correctionworkflows. Remote Sens., 10.","DOI":"10.3390\/rs10071091"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1016\/j.rse.2009.02.016","article-title":"Imaging Chlorophyll Fluorescence with an Airborne Narrow-Band Multispectral Camera for Vegetation Stress Detection","volume":"113","author":"Berni","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1109\/LGRS.2010.2079913","article-title":"Mini-UAV-Borne LIDAR for Fine-Scale Mapping","volume":"8","author":"Lin","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Malb\u00e9teau, Y., Parkes, S., Aragon, B., Rosas, J., and McCabe, M.F. (2018). Capturing the diurnal cycle of land surface temperature using an unmanned aerial vehicle. Remote Sens., 10.","DOI":"10.3390\/rs10091407"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ziliani, M.G., Parkes, S.D., Hoteit, I., and McCabe, M.F. (2018). Intra-Season Crop Height Variability at Commercial Farm Scales Using a Fixed-Wing UAV. Remote Sens., 10.","DOI":"10.3390\/rs10122007"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TGRS.2008.2010457","article-title":"Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring from an Unmanned Aerial Vehicle","volume":"47","author":"Berni","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1007\/s00271-012-0382-9","article-title":"Assessment of Vineyard Water Status Variability by Thermal and Multispectral Imagery Using an Unmanned Aerial Vehicle (UAV)","volume":"30","author":"Baluja","year":"2012","journal-title":"Irrig. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Manfreda, S., McCabe, M.F., Miller, P.E., Lucas, R., Madrigal, V.P., Mallinis, G., Dor, E.B., Helman, D., Estes, L., and Ciraolo, G. (2018). On the Use of Unmanned Aerial Systems for Environmental Monitoring. Remote Sens., 10.","DOI":"10.20944\/preprints201803.0097.v1"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1111\/ajgw.12173","article-title":"Vineyard Irrigation Scheduling Based on Airborne Thermal Imagery and Water Potential Thresholds","volume":"22","author":"Bellvert","year":"2016","journal-title":"Aust. J. Grape Wine Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.agwat.2016.08.026","article-title":"High-Resolution UAV-Based Thermal Imaging to Estimate the Instantaneous and Seasonal Variability of Plant Water Status within a Vineyard","volume":"183","author":"Santesteban","year":"2017","journal-title":"Agric. Water Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.3389\/fpls.2016.01131","article-title":"A Direct Comparison of Remote Sensing Approaches for High-Throughput Phenotyping in Plant Breeding","volume":"7","author":"Tattaris","year":"2016","journal-title":"Front. Plant Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.3389\/fpls.2017.01111","article-title":"Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives","volume":"8","author":"Yang","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1681","DOI":"10.3389\/fpls.2017.01681","article-title":"UAV-Based Thermal Imaging for High-Throughput Field Phenotyping of Black Poplar Response to Drought","volume":"8","author":"Ludovisi","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1007\/s11119-016-9449-6","article-title":"Field Phenotyping of Water Stress at Tree Scale by UAV-Sensed Imagery: New Insights for Thermal Acquisition and Calibration","volume":"17","author":"Virlet","year":"2016","journal-title":"Precis. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"111599","DOI":"10.1016\/j.rse.2019.111599","article-title":"Soybean Yield Prediction from UAV Using Multimodal Data Fusion and Deep Learning","volume":"237","author":"Maimaitijiang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1038\/s41477-018-0189-7","article-title":"Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations","volume":"4","author":"Camino","year":"2018","journal-title":"Nat. Plants"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.rse.2013.07.031","article-title":"High-Resolution Airborne Hyperspectral and Thermal Imagery for Early Detection of Verticillium Wilt of Olive Using Fluorescence, Temperature and Narrow-Band Spectral Indices","volume":"139","author":"Lucena","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sagan, V., Maimaitijiang, M., Sidike, P., Eblimit, K., Peterson, K.T., Hartling, S., Esposito, F., Khanal, K., Newcomb, M., and Pauli, D. (2019). UAV-Based High Resolution Thermal Imaging for Vegetation Monitoring, and Plant Phenotyping Using ICI 8640 P, FLIR Vue Pro R 640, and Thermomap Cameras. Remote Sens., 11.","DOI":"10.3390\/rs11030330"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Aragon, B., Johansen, K., Parkes, S., Malbeteau, Y., Al-Mashharawi, S., Al-Amoudi, T., Andrade, C.F., Turner, D., Lucieer, A., and McCabe, M.F. (2020). A Calibration Procedure for Field and UAV-Based Uncooled Thermal Infrared Instruments. Sensors, 20.","DOI":"10.3390\/s20113316"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kelly, J., Kljun, N., Olsson, P.O., Mihai, L., Liljeblad, B., Weslien, P., Klemedtsson, L., and Eklundh, L. (2019). Challenges and Best Practices for Deriving Temperature Data from an Uncalibrated UAV Thermal Infrared Camera. Remote Sens., 11.","DOI":"10.3390\/rs11050567"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"150","DOI":"10.3389\/fpls.2020.00150","article-title":"Assessment of Multi-Image Unmanned Aerial Vehicle Based High-Throughput Field Phenotyping of Canopy Temperature","volume":"11","author":"Perich","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1270","DOI":"10.3389\/fpls.2019.01270","article-title":"Maize canopy temperature extracted from UAV thermal and RGB imagery and its application in water stress monitoring","volume":"10","author":"Zhang","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1111\/phor.12216","article-title":"An advanced radiometric calibration approach for uncooled thermal cameras","volume":"33","author":"Lin","year":"2017","journal-title":"Photogramm. Rec."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"187","DOI":"10.5194\/jsss-4-187-2015","article-title":"Calibration of Uncooled Thermal Infrared Cameras","volume":"4","author":"Budzier","year":"2015","journal-title":"J. Sens. Sens. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ribeiro-Gomes, K., Hern\u00e1ndez-L\u00f3pez, D., Ortega, J.F., Ballesteros, R., Poblete, T., and Moreno, M.A. (2017). Uncooled Thermal Camera Calibration and Optimization of the Photogrammetry Process for UAV Applications in Agriculture. Sensors, 17.","DOI":"10.3390\/s17102173"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"061304","DOI":"10.1117\/1.OE.52.6.061304","article-title":"Correcting for Focal-Plane-Array Temperature Dependence in Microbolometer Infrared Cameras Lacking Thermal Stabilization","volume":"52","author":"Nugent","year":"2013","journal-title":"Opt. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4003","DOI":"10.3390\/rs6054003","article-title":"Spatial Co-Registration of Ultra-High Resolution Visible, Multispectral and Thermal Images Acquired with a Micro-UAV over Antarctic Moss Beds","volume":"6","author":"Turner","year":"2014","journal-title":"Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Acorsi, M.G., Gimenez, L.M., and Martello, M. (2020). Assessing the performance of a low-cost thermal camera in proximal and aerial conditions. Remote Sens., 12.","DOI":"10.3390\/rs12213591"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2738","DOI":"10.1109\/TGRS.2013.2265295","article-title":"Direct Georeferencing of Ultrahigh-Resolution UAV Imagery","volume":"52","author":"Turner","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s11263-007-0107-3","article-title":"Modeling the World from Internet Photo Collections","volume":"80","author":"Snavely","year":"2008","journal-title":"Int. J. Comput. Vis."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1080\/02626669609491522","article-title":"Use of Remote Sensing for Evapotranspiration Monitoring over Land Surfaces","volume":"41","author":"Kustas","year":"1996","journal-title":"Hydrol. Sci. J."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"892","DOI":"10.1109\/36.508406","article-title":"A Generalized Split-Window Algorithm for Retrieving Land-Surface Temperature from Space","volume":"34","author":"Wan","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Mesas-Carrascosa, F.-J., P\u00e9rez-Porras, F., Larriva, J.E.M.D., Frau, C.M., Ag\u00fcera-Vega, F., Carvajal-Ram\u00edrez, F., Mart\u00ednez-Carricondo, P., and Garc\u00eda-Ferrer, A. (2018). Drift Correction of Lightweight Microbolometer Thermal Sensors On-Board Unmanned Aerial Vehicles. Remote Sens., 10.","DOI":"10.3390\/rs10040615"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"370","DOI":"10.3389\/fpls.2019.00370","article-title":"Unmanned Aerial Vehicle-Based Phenotyping Using Morphometric and Spectral Analysis Can Quantify Responses of Wild Tomato Plants to Salinity Stress","volume":"30","author":"Johansen","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.compag.2017.07.026","article-title":"Recent advances in crop water stress detection","volume":"141","author":"Ihuoma","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_47","first-page":"188","article-title":"UV Light Acclimation Capacity of Leaf Photosynthetic and Photochemical Behaviour in Near-isohydric and Anisohydric Grapevines in Hot and Dry Environments","volume":"40","author":"Dettori","year":"2019","journal-title":"S. Afr. J. Enol. Vitic."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"105343","DOI":"10.1016\/j.compag.2020.105343","article-title":"CFD simulation and experimental verification of the spatial and temporal distributions of the downwash airflow of a quad-rotor agricultural UAV in hover","volume":"172","author":"Guo","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"28","DOI":"10.3389\/frai.2020.00028","article-title":"Predicting Biomass and Yield in a Tomato Phenotyping Experiment Using UAV Imagery and Random Forest","volume":"3","author":"Johansen","year":"2020","journal-title":"Front. Artif. Intell."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5047","DOI":"10.1080\/01431160802036474","article-title":"Linear Mixing in Thermal Infrared Temperature Retrieval","volume":"29","author":"McCabe","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Niu, H., Hollenbeck, D., Zhao, T., Wang, D., and Chen, Y. (2020). Evapotranspiration estimation with small UAVs in precision agriculture. Sensors, 20.","DOI":"10.3390\/s20226427"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"101465","DOI":"10.1016\/j.eti.2021.101465","article-title":"Remotely sensed identification of canopy characteristics using UAV-based imagery under unstable environmental conditions","volume":"22","author":"Awais","year":"2021","journal-title":"Environ. Technol. Innov."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.agwat.2019.03.034","article-title":"Current and potential capabilities of UAS for crop water productivity in precision agriculture","volume":"218","author":"Ezenne","year":"2019","journal-title":"Agric. Water Manag."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Han, X., Thomasson, J.A., Swaminathan, V., Wang, T., Siegfried, J., Raman, R., Rajan, N., and Neely, H. (2020). Field-based calibration of unmanned aerial vehicle thermal infrared imagery with temperature-controlled references. Sensors, 20.","DOI":"10.3390\/s20247098"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3255\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:46:16Z","timestamp":1760165176000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3255"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,18]]},"references-count":54,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13163255"],"URL":"https:\/\/doi.org\/10.3390\/rs13163255","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,18]]}}}