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Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data

Yang, Jiajia; Zhou, Ji; Göttsche, Frank-Michael; Long, Zhiyong; Ma, Jin; Luo, Ren

Abstract (englisch):
Land surface temperature (LST) is an important indicator of global ecological environment and climate change. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the recently launched Sentinel-3 satellites provides high-quality observations for estimating global LST. The algorithm of the official SLSTR LST product is a split-window algorithm (SWA) that implicitly assumes and utilizes knowledge of land surface emissivity (LSE). The main objective of this study is to investigate alternative SLSTR LST retrieval algorithms with an explicit use of LSE. Seventeen widely accepted SWAs, which explicitly utilize LSE, were selected as candidate algorithms. First, the SWAs were trained using a comprehensive global simulation dataset. Then, using simulation data as well as in-situ LST, the SWAs were evaluated according to their sensitivity and accuracy: eleven algorithms showed good training accuracy and nine of them exhibited low sensitivity to uncertainties in LSE and column water vapor content. Evaluation based on two global simulation datasets and a regional simulation dataset showed that these nine SWAs had similar accuracy with negligible systematic errors and RMSEs lower than 1.0 K. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000118905
Veröffentlicht am 08.07.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung - Atmosphärische Spurenstoffe und Fernerkundung (IMK-ASF)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 09.2020
Sprache Englisch
Identifikator ISSN: 0303-2434
KITopen-ID: 1000118905
Erschienen in International journal of applied earth observation and geoinformation
Band 91
Seiten Article: 102136
Nachgewiesen in Web of Science
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