{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:57:58Z","timestamp":1775696278451,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T00:00:00Z","timestamp":1618272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Federal Institute of Hydrology","award":["-"],"award-info":[{"award-number":["-"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Over the Hahn\u00f6fer Nebenelbe, a part of the Elbe estuary near Hamburg, Germany, a combined aerial survey with an unmanned aerial system (UAV) and a gyrocopter was conducted to acquire information about the water surface temperatures. The water temperature in the estuary is important for biological processes and living conditions of riverine organisms. This study aimed to develop a workflow that allows for comparing and analysing surface temperatures acquired by two different remote sensing systems. The thermal infrared (TIR) datasets were compared with in situ measurements gathered during the data acquisition, where both TIR datasets showed a varying bias. Potential error sources regarding the absolute and relative accuracy were investigated and modelled based on the available measurements, including emissivity, atmosphere, skin effect at the water surface, camera flat field correction and calibration. The largest effects on the observed TIR water temperature had the camera calibration and the modelled atmospheric effects. After the correction steps, both datasets could be combined to create a multitemporal representation of the temperature pattern and profiles over the survey area\u2019s wadden flats.<\/jats:p>","DOI":"10.3390\/rs13081489","type":"journal-article","created":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T05:31:37Z","timestamp":1618291897000},"page":"1489","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Observing Water Surface Temperature from Two Different Airborne Platforms over Temporarily Flooded Wadden Areas at the Elbe Estuary\u2014Methods for Corrections and Analysis"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0391-6949","authenticated-orcid":false,"given":"Katharina","family":"Fricke","sequence":"first","affiliation":[{"name":"Federal Institute of Hydrology, Am Mainzer Tor 1, 56068 Koblenz, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4488-623X","authenticated-orcid":false,"given":"Bj\u00f6rn","family":"Baschek","sequence":"additional","affiliation":[{"name":"Federal Institute of Hydrology, Am Mainzer Tor 1, 56068 Koblenz, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1890-4839","authenticated-orcid":false,"given":"Alexander","family":"Jenal","sequence":"additional","affiliation":[{"name":"Application Center for Machine Learning and Sensor Technology (AMLS), Fraunhofer Institute for High Frequency Physics and Radar Techniques FHR, Joseph-Rovan-Allee 2, 53424 Remagen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2570-7788","authenticated-orcid":false,"given":"Caspar","family":"Kneer","sequence":"additional","affiliation":[{"name":"Application Center for Machine Learning and Sensor Technology (AMLS), Fraunhofer Institute for High Frequency Physics and Radar Techniques FHR, Joseph-Rovan-Allee 2, 53424 Remagen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5150-9697","authenticated-orcid":false,"given":"Immanuel","family":"Weber","sequence":"additional","affiliation":[{"name":"Application Center for Machine Learning and Sensor Technology (AMLS), Fraunhofer Institute for High Frequency Physics and Radar Techniques FHR, Joseph-Rovan-Allee 2, 53424 Remagen, Germany"}]},{"given":"Jens","family":"Bongartz","sequence":"additional","affiliation":[{"name":"Application Center for Machine Learning and Sensor Technology (AMLS), Fraunhofer Institute for High Frequency Physics and Radar Techniques FHR, Joseph-Rovan-Allee 2, 53424 Remagen, Germany"},{"name":"Department of Mathematics and Technology, University of Applied Science Koblenz, Joseph-Rovan-Allee 2, 53424 Remagen, Germany"}]},{"given":"Jens","family":"Wyrwa","sequence":"additional","affiliation":[{"name":"Federal Institute of Hydrology, Am Mainzer Tor 1, 56068 Koblenz, Germany"}]},{"given":"Andreas","family":"Sch\u00f6l","sequence":"additional","affiliation":[{"name":"Federal Institute of Hydrology, Am Mainzer Tor 1, 56068 Koblenz, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1002\/rra.1260","article-title":"Modelling the effects of thermal stratifica-tion on the oxygen budget of an impounded river","volume":"26","author":"Becker","year":"2010","journal-title":"River Res. 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