{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T14:44:13Z","timestamp":1781621053588,"version":"3.54.5"},"reference-count":78,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,3,27]],"date-time":"2021-03-27T00:00:00Z","timestamp":1616803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001655","name":"Deutscher Akademischer Austauschdienst","doi-asserted-by":"publisher","award":["57429870"],"award-info":[{"award-number":["57429870"]}],"id":[{"id":"10.13039\/501100001655","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the development of low-cost, lightweight, integrated thermal infrared-multispectral cameras, unmanned aerial systems (UAS) have recently become a flexible complement to eddy covariance (EC) station methods for mapping surface energy fluxes of vegetated areas. These sensors facilitate the measurement of several site characteristics in one flight (e.g., radiometric temperature, vegetation indices, vegetation structure), which can be used alongside in-situ meteorology data to provide spatially-distributed estimates of energy fluxes at very high resolution. Here we test one such system (MicaSense Altum) integrated into an off-the-shelf long-range vertical take-off and landing (VTOL) unmanned aerial vehicle, and apply and evaluate our method by comparing flux estimates with EC-derived data, with specific and novel focus on heterogeneous vegetation communities at three different sites in Germany. Firstly, we present an empirical method for calibrating airborne radiometric temperature in standard units (K) using the Altum multispectral and thermal infrared instrument. Then we provide detailed methods using the two-source energy balance model (TSEB) for mapping net radiation (Rn), sensible (H), latent (LE) and ground (G) heat fluxes at &lt;0.82 m resolution, with root mean square errors (RMSE) less than 45, 37, 39, 52 W m\u22122 respectively. Converting to radiometric temperature using our empirical method resulted in a 19% reduction in RMSE across all fluxes compared to the standard conversion equation provided by the manufacturer. Our results show the potential of this UAS for mapping energy fluxes at high resolution over large areas in different conditions, but also highlight the need for further surveys of different vegetation types and land uses.<\/jats:p>","DOI":"10.3390\/rs13071286","type":"journal-article","created":{"date-parts":[[2021,3,28]],"date-time":"2021-03-28T23:27:25Z","timestamp":1616974045000},"page":"1286","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["High Spatial and Temporal Resolution Energy Flux Mapping of Different Land Covers Using an Off-the-Shelf Unmanned Aerial System"],"prefix":"10.3390","volume":"13","author":[{"given":"Jake E.","family":"Simpson","sequence":"first","affiliation":[{"name":"Institute of Geography, University of Augsburg, Alter Postweg 118, 86159 Augsburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fenner","family":"Holman","sequence":"additional","affiliation":[{"name":"Institute of Geography, University of Augsburg, Alter Postweg 118, 86159 Augsburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4250-6424","authenticated-orcid":false,"given":"Hector","family":"Nieto","sequence":"additional","affiliation":[{"name":"COMPLUTIG S.L., Colegios 2, 28801 Alcal\u00e1 de Henares, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9700-2771","authenticated-orcid":false,"given":"Ingo","family":"Voelksch","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, 82467 Garmisch-Partenkirchen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8789-163X","authenticated-orcid":false,"given":"Matthias","family":"Mauder","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, 82467 Garmisch-Partenkirchen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janina","family":"Klatt","sequence":"additional","affiliation":[{"name":"Chair of Vegetation Ecology, Institute of Ecology and Landscape, Department Landscape Architecture, Weihenstephan-Triesdorf University of Applied Sciences, Am Hofgarten 1, 85354 Freising, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6244-4705","authenticated-orcid":false,"given":"Peter","family":"Fiener","sequence":"additional","affiliation":[{"name":"Institute of Geography, University of Augsburg, Alter Postweg 118, 86159 Augsburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9919-7613","authenticated-orcid":false,"given":"Jed O.","family":"Kaplan","sequence":"additional","affiliation":[{"name":"Institute of Geography, University of Augsburg, Alter Postweg 118, 86159 Augsburg, Germany"},{"name":"Department of Earth Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-017-02810-8","article-title":"The mark of vegetation change on Earth\u2019s surface energy balance","volume":"9","author":"Duveiller","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2066","DOI":"10.1002\/joc.2061","article-title":"Investigating the climate impacts of global land cover change in the community climate system model","volume":"30","author":"Lawrence","year":"2010","journal-title":"Int. J. Climatol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1126\/science.215.4539.1498","article-title":"Influence of Land-Surface Evapotranspiration on the Earth\u2019s Climate","volume":"215","author":"Shukla","year":"1982","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1444","DOI":"10.1126\/science.1155121","article-title":"Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests","volume":"320","author":"Bonan","year":"2008","journal-title":"Science"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"053002","DOI":"10.1088\/1748-9326\/aa6b3f","article-title":"Biophysical effects on temperature and precipitation due to land cover change","volume":"12","author":"Perugini","year":"2017","journal-title":"Environ. Res. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1146\/annurev-environ-102017-030136","article-title":"The Effects of Tropical Vegetation on Rainfall","volume":"43","author":"Spracklen","year":"2018","journal-title":"Annu. Rev. Environ. Resour."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/0168-1923(95)02265-Y","article-title":"Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature","volume":"77","author":"Norman","year":"1995","journal-title":"Agric. For. Meteorol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1023\/A:1008168910634","article-title":"Thermal infrared remote sensing for analysis of landscape ecological processes: Methods and applications","volume":"14","author":"Quattrochi","year":"1999","journal-title":"Landsc. Ecol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.agrformet.2006.12.009","article-title":"Agroforestry management as an adaptive strategy against potential microclimate extremes in coffee agriculture","volume":"144","author":"Lin","year":"2007","journal-title":"Agric. For. Meteorol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1038\/nclimate2430","article-title":"Effects of tropical deforestation on climate and agriculture","volume":"5","author":"Lawrence","year":"2015","journal-title":"Nat. Clim. Chang."},{"key":"ref_11","unstructured":"Liang, S., and Wang, J. (2019). Advanced Remote Sensing: Terrestrial Information Extraction and Applications, Elsevier."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2136\/vzj2018.03.0060","article-title":"The TERENO pre-alpine observatory: Integrating meteorological, hydrological, and biogeochemical measurements and modeling","volume":"17","author":"Kiese","year":"2018","journal-title":"Vadose Zone J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1515\/intag-2017-0044","article-title":"ICOS eddy covariance flux-station site setup: A review","volume":"32","author":"Rebmann","year":"2018","journal-title":"Int. Agrophysics"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1061\/(ASCE)0733-9437(2007)133:4(395)","article-title":"Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)\u2014Applications","volume":"133","author":"Allen","year":"2007","journal-title":"J. Irrig. Drain. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Reis, T.G., Monteiro, R.O.C., Albuquerque, M.G., Espinoza, J.M.A., Ferreira, J.A.C., and Moreria, E.G. (2017, January 2\u20136). Actual Evapotranspiration Estimated By Orbital Sensors, Uav and Meteorological Station for Vineyards in the Southern Brazil. Proceedings of the IV Inovagri International Meeting, Fortaleza, Brazil.","DOI":"10.7127\/iv-inovagri-meeting-2017-res4150694"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"697","DOI":"10.5194\/hess-20-697-2016","article-title":"Estimating evaporation with thermal UAV data and two-source energy balance models","volume":"20","author":"Hoffmann","year":"2016","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5021","DOI":"10.5194\/bg-11-5021-2014","article-title":"Remotely sensed land-surface energy fluxes at sub-field scale in heterogeneous agricultural landscape and coniferous plantation","volume":"11","author":"Guzinski","year":"2014","journal-title":"Biogeosciences"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/s00271-018-0585-9","article-title":"Evaluation of TSEB turbulent fluxes using different methods for the retrieval of soil and canopy component temperatures from UAV thermal and multispectral imagery","volume":"37","author":"Nieto","year":"2019","journal-title":"Irrig. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5141","DOI":"10.1080\/01431161.2018.1471550","article-title":"Estimation of evapotranspiration of temperate grassland based on high-resolution thermal and visible range imagery from unmanned aerial systems","volume":"39","author":"Brenner","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3003","DOI":"10.1080\/01431161.2017.1280202","article-title":"Estimating spatially distributed turbulent heat fluxes from high-resolution thermal imagery acquired with a UAV system","volume":"38","author":"Brenner","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Niu, H., Zhao, T., Wang, D., and Chen, Y. (2019). Evapotranspiration Estimation with UAVs in Agriculture: A Review. Preprints.","DOI":"10.20944\/preprints201907.0124.v1"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Andreu, A., Dube, T., Nieto, H., Mudau, A.E., Gonz\u00e1lez-Dugo, M.P., Guzinski, R., and H\u00fclsmann, S. (2019). Remote sensing of water use and water stress in the African savanna ecosystem at local scale \u2013 Development and validation of a monitoring tool. Phys. Chem. Earth.","DOI":"10.1016\/j.pce.2019.02.004"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Burchard-Levine, V., Nieto, H., Ria\u00f1o, D., Migliavacca, M., El-Madany, T.S., Perez-Priego, O., Carrara, A., and Mart\u00edn, M.P. (2020). Seasonal adaptation of the thermal-based two-source energy balance model for estimating evapotranspiration in a semiarid tree-grass ecosystem. Remote Sens., 12.","DOI":"10.3390\/rs12060904"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xia, T., Kustas, W.P., Anderson, M.C., Alfieri, J.G., Gao, F., McKee, L., Prueger, J.H., Geli, H.M.E., Neale, C.M.U., and Sanchez, L. (2016). Mapping evapotranspiration with high-resolution aircraft imagery over vineyards using one-and two-source modeling schemes. Hydrol. Earth Syst. Sci., 20.","DOI":"10.5194\/hessd-12-11905-2015"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Alhassan, A., and Jin, M. (2020). Evapotranspiration in the Tono Reservoir Catchment in Upper East Region of Ghana Estimated by a Novel TSEB Approach from ASTER Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12030569"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s00271-009-0177-9","article-title":"Estimating hourly crop ET using a two-source energy balance model and multispectral airborne imagery","volume":"28","author":"Gowda","year":"2009","journal-title":"Irrig. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2295","DOI":"10.1002\/hyp.7336","article-title":"Effects of topography on the spatial distribution of evapotranspiration over a complex terrain using two-source energy balance model with ASTER data","volume":"23","author":"Kafle","year":"2009","journal-title":"Hydrol. Process."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Torres-Rua, A. (2017). Vicarious Calibration of sUAS Microbolometer Temperature Imagery for Estimation of Radiometric Land Surface Temperature. Sensors, 17.","DOI":"10.3390\/s17071499"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Weslien, P., Klemedtsson, L., Eklundh, L., Kelly, J., Kljun, N., Olsson, P.O., Mihai, L., Liljeblad, B., Weslien, P., and Klemedtsson, 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_30","doi-asserted-by":"crossref","unstructured":"Sagan, V., Maimaitijiang, M., Sidike, P., Eblimit, K., Peterson, K., 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_31","doi-asserted-by":"crossref","unstructured":"Guzinski, R., Nieto, H., Sandholt, I., and Karamitilios, G. (2020). Modelling high-resolution actual evapotranspiration through Sentinel-2 and Sentinel-3 data fusion. Remote Sens., 12.","DOI":"10.3390\/rs12091433"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, R., Jin, Y., Kandelous, M.M., Zaccaria, D., Sanden, B.L., Snyder, R.L., Jiang, J., and Hopmans, J.W. (2017). Evapotranspiration estimate over an almond orchard using Landsat satellite observations. Remote Sens., 9.","DOI":"10.3390\/rs9050436"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/s00271-018-0610-z","article-title":"Influence of wind direction on the surface roughness of vineyards","volume":"37","author":"Alfieri","year":"2019","journal-title":"Irrig. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1023\/A:1018991015119","article-title":"An analytical footprint model for non-neutral stratification","volume":"99","author":"Kormann","year":"2001","journal-title":"Boundary-Layer Meteorol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s10546-020-00529-6","article-title":"Surface-Energy-Balance Closure over Land: A Review","volume":"177","author":"Mauder","year":"2020","journal-title":"Boundary-Layer Meteorol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.rse.2013.05.010","article-title":"Using radiometric surface temperature for surface energy flux estimation in Mediterranean drylands from a two-source perspective","volume":"136","author":"Morillas","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Tilahun, T. (2019). High-Resolution Mapping of Subsurface Tile Drainage in Agricultural Fields Using an Unmanned Aerial System (UAS). Univ. Res. Symp.","DOI":"10.3390\/hydrology8010002"},{"key":"ref_38","unstructured":"Hutton, J.J., Lipa, G., Baustian, D., Sulik, J., and Bruce, R.W. (2020). High Accuracy Direct Georeferencing of the Altum Multispectral UAV Camera and its Application to High Throughput Plant Phenotyping. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"429","DOI":"10.5194\/isprs-archives-XLIII-B3-2020-429-2020","article-title":"Using multitemporal hyper-and multispectral UAV imaging for detecting bark beetle infestation on norway spruce","volume":"43","author":"Honkavaara","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Miller, I.J., Schieber, B., De Bey, Z., Benner, E., Ortiz, J.D., Girdner, J., Patel, P., Coradazzi, D.G., Henriques, J., and Forsyth, J. (2020, January 24\u201324). Analyzing crop health in vineyards through a multispectral imaging and drone system. Proceedings of the 2020 Systems and Information Engineering Design Symposium, SIEDS, Charlottesville, VA, USA.","DOI":"10.1109\/SIEDS49339.2020.9106671"},{"key":"ref_41","unstructured":"(2021, January 18). ICOS-Deutschland ICOS: Graswang (C3). Available online: https:\/\/www.icos-infrastruktur.de\/en\/icos-d\/komponenten\/oekosysteme\/beobachtungsstandorte\/graswang-c3\/."},{"key":"ref_42","unstructured":"(2021, January 18). ICOS-Deutschland ICOS: Fendt (C1). Available online: https:\/\/www.icos-infrastruktur.de\/en\/icos-d\/komponenten\/oekosysteme\/beobachtungsstandorte\/fendt-c1\/."},{"key":"ref_43","unstructured":"(2021, January 18). ICOS-Deutschland ICOS: Mooseurach (C3). Available online: https:\/\/www.icos-infrastruktur.de\/en\/icos-d\/komponenten\/oekosysteme\/beobachtungsstandorte\/mooseurach-c3\/."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3477","DOI":"10.5194\/bg-11-3477-2014","article-title":"Can a bog drained for forestry be a stronger carbon sink than a natural bog forest?","volume":"11","author":"Hommeltenberg","year":"2014","journal-title":"Biogeosciences"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2273","DOI":"10.5194\/amt-7-2273-2014","article-title":"Towards a consistent eddy-covariance processing: An intercomparison of EddyPro and TK3","volume":"7","author":"Fratini","year":"2014","journal-title":"Atmos. Meas. Tech."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Foken, T., Aubinet, M., and Leuning, R. (2012). The Eddy Covariance Method. Eddy Covariance, Springer.","DOI":"10.1007\/978-94-007-2351-1"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.agrformet.2012.09.006","article-title":"A strategy for quality and uncertainty assessment of long-term eddy-covariance measurements","volume":"169","author":"Mauder","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.agrformet.2017.06.008","article-title":"Experimental evaluation of flux footprint models","volume":"246","author":"Heidbach","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3695","DOI":"10.5194\/gmd-8-3695-2015","article-title":"A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP)","volume":"8","author":"Kljun","year":"2015","journal-title":"Geosci. Model Dev."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.jhydrol.2013.11.047","article-title":"High-resolution characterization of a semiarid watershed: Implications on evapotranspiration estimates","volume":"509","author":"Templeton","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1890\/06-0922.1","article-title":"The energy balance closure problem: An overview","volume":"18","author":"Foken","year":"2008","journal-title":"Ecol. Appl."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1002\/hyp.11397","article-title":"Evaluation of energy balance closure adjustment methods by independent evapotranspiration estimates from lysimeters and hydrological simulations","volume":"32","author":"Mauder","year":"2018","journal-title":"Hydrol. Process."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1002\/qj.49711146910","article-title":"Evaporation from sparse crops-an energy combination theory","volume":"111","author":"Shuttleworth","year":"1985","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1175\/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2","article-title":"On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters","volume":"100","author":"Priestley","year":"1972","journal-title":"Mon. Weather Rev."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Bellvert, J., Jofre-\u0108ekalovi\u0107, C., Pelech\u00e1, A., Mata, M., and Nieto, H. (2020). Feasibility of Using the Two-Source Energy Balance Model (TSEB) with Sentinel-2 and Sentinel-3 Images to Analyze the Spatio-Temporal Variability of Vine Water Status in a Vineyard. Remote Sens., 12.","DOI":"10.3390\/rs12142299"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.advwatres.2012.06.004","article-title":"Two-source energy balance model estimates of evapotranspiration using component and composite surface temperatures","volume":"50","author":"Colaizzi","year":"2012","journal-title":"Adv. Water Resour."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2071","DOI":"10.1016\/j.agrformet.2009.05.016","article-title":"Advances in thermal infrared remote sensing for land surface modeling","volume":"149","author":"Kustas","year":"2009","journal-title":"Agric. For. Meteorol."},{"key":"ref_58","unstructured":"(2021, January 05). Wind Energy Data for Switzerland. Available online: https:\/\/wind-data.ch\/tools\/profile.php?h=3.25&v=2.17&z0=0.2&abfrage=Aktualisieren."},{"key":"ref_59","unstructured":"(2020, May 07). FLIR Tech Note: Radiometric Temperature Measurements. Available online: https:\/\/www.flir.com\/globalassets\/guidebooks\/suas-radiometric-tech-note-en.pdf."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/S0034-4257(01)00272-3","article-title":"Temperature and emissivity separation from multispectral thermal infrared observations","volume":"79","author":"Schmugge","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"633","DOI":"10.21105\/joss.00633","article-title":"ijtiff: An R package providing TIFF I\/O for ImageJ users","volume":"3","author":"Nolan","year":"2018","journal-title":"J. Open Source Softw."},{"key":"ref_62","unstructured":"(2016). RStudio Team RStudio: Integrated Development for R., RStudio, Inc."},{"key":"ref_63","unstructured":"(2020, November 13). CloudCompare 3D Point Cloud and Mesh Processing Software; 2021. Available online: https:\/\/www.danielgm.net\/cc\/."},{"key":"ref_64","unstructured":"Isenburg, M. (2020, November 20). LAStools\u2014Efficient LiDAR Processing Software; 2011. Available online: https:\/\/rapidlasso.com\/lastools\/."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"112061","DOI":"10.1016\/j.rse.2020.112061","article-title":"lidR: An R package for analysis of Airborne Laser Scanning (ALS) data","volume":"251","author":"Roussel","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Pareeth, S., Karimi, P., Shafiei, M., De Fraiture, C., Pareeth, S., Karimi, P., Shafiei, M., and De Fraiture, C. (2019). Mapping Agricultural Landuse Patterns from Time Series of Landsat 8 Using Random Forest Based Hierarchial Approach. Remote Sens., 11.","DOI":"10.3390\/rs11050601"},{"key":"ref_67","unstructured":"Stefan, V. (2021, March 02). R\u2014Using Random Forests, Support Vector Machines and Neural Networks for a Pixel Based Supervised Classification of Sentinel-2 Multispectral Images. Available online: https:\/\/valentinitnelav.github.io\/satellite-image-classification-r\/#visualize-classifications."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.agrformet.2005.10.005","article-title":"CO2 fluxes in adjacent new and permanent temperate grasslands","volume":"135","author":"Byrne","year":"2005","journal-title":"Agric. For. Meteorol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"451","DOI":"10.5194\/bg-5-451-2008","article-title":"Quality control of CarboEurope flux data - Part 2: Inter-comparison of eddy-covariance software","volume":"5","author":"Mauder","year":"2008","journal-title":"Biogeosciences"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1007\/s10712-008-9037-z","article-title":"Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data","volume":"29","author":"Kalma","year":"2008","journal-title":"Surv. Geophys."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.ecolmodel.2008.05.006","article-title":"How to evaluate models: Observed vs. predicted or predicted vs. observed?","volume":"216","author":"Perelman","year":"2008","journal-title":"Ecol. Modell."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.2136\/vzj2009.0158","article-title":"Insights from Independent Evapotranspiration Estimates for Closing the Energy Balance: A Grassland Case Study","volume":"9","author":"Wohlfahrt","year":"2010","journal-title":"Vadose Zo. J."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.agrformet.2018.01.029","article-title":"Variability and bias in active and passive ground-based measurements of effective plant, wood and leaf area index","volume":"252","author":"Calders","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1007\/s00271-018-0586-8","article-title":"Utility of the two-source energy balance (TSEB) model in vine and interrow flux partitioning over the growing season","volume":"37","author":"Kustas","year":"2019","journal-title":"Irrig. Sci."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.5194\/hess-21-1809-2017","article-title":"A site-level comparison of lysimeter and eddy covariance flux measurements of evapotranspiration","volume":"21","author":"Hirschi","year":"2017","journal-title":"Hydrol. Earth Syst. Sci"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Burchard-Levine, V., Nieto, H., Ria\u00f1o, D., Migliavacca, M., El-Madany, T.S., Perez-Priego, O., Carrara, A., and Mart\u00edn, M.P. (2019). Adapting the thermal-based two-source energy balance model to estimate energy fluxes in a complex tree-grass ecosystem. Hydrol. Earth Syst. Sci. Discuss., 1\u201337.","DOI":"10.5194\/hess-2019-354"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.rse.2018.11.019","article-title":"Evaluating the feasibility of using Sentinel-2 and Sentinel-3 satellites for high-resolution evapotranspiration estimations","volume":"221","author":"Guzinski","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Nieto, H., Kustas, W.P., Alfieri, J.G., Gao, F., Hipps, L.E., Los, S., Prueger, J.H., McKee, L.G., and Anderson, M.C. (2019). Impact of different within-canopy wind attenuation formulations on modelling sensible heat flux using TSEB. Irrig. Sci., 37.","DOI":"10.1007\/s00271-018-0611-y"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1286\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:10:14Z","timestamp":1760364614000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/7\/1286"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,27]]},"references-count":78,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13071286"],"URL":"https:\/\/doi.org\/10.3390\/rs13071286","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,27]]}}}