Development of a split-window algorithm for estimating sea surface temperature from the Chinese Gaofen-5 data

• Published in: International Journal of Remote Sensing
• Main Authors: Chen, Yuanyuan, Duan, Si-Bo, Labed, Jélila, Li, Zhao-Liang
• Other Authors: Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2019
• Subjects: [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces; environment; Sea ice
• Online Access: https://hal.archives-ouvertes.fr/hal-02377663; https://hal.archives-ouvertes.fr/hal-02377663/document; https://hal.archives-ouvertes.fr/hal-02377663/file/Chenyy.pdf; https://doi.org/10.1080/01431161.2018.1488295

International audience Sea surface temperature (SST) is an essential climate variable that can be used to assess climate change. One kind of method commonly used to estimate SST based on remote-sensing measurements is the split-window (SW) algorithm. However, the derivation of the linear SW algorithm does not appear to reflect reality because some assumptions and approximations were used. Moreover, the quadratic SW equation cannot be interpreted theoretically although it maintains the structure of the linear SW equation. The Gaofen-5 (GF-5) satellite launch is planned for 2018. Focusing on exploring the mechanism of the SW algorithm using GF-5 data, this study investigated the assumptions and approximations used to derive the linear SW technique. Two revised equations of these assumptions and approximations were developed. Combining the revised equations, a nonlinear SW algorithm was obtained that could be simplified to the quadratic equation. Compared with previous research, this study focuses more on the theoretical interpretation and improves our understanding of the semi-empirical quadratic SW equation. The matchup data set produced by the European Organization for the Exploitation of Meteorological Satellites Ocean and Sea Ice Satellite Application Facility was used to validate the quadratic SW algorithm. A bias of −0.05 K and a RMSE of 0.53 K were obtained.