Cloud Screening Method in Complex Background Areas Containing Snow and Ice Based on Landsat 9 Images

Tingting Wu, Qing Liu and Ying Jing ()
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Tingting Wu: School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China
Qing Liu: School of Mechanical and Electrical Engineering, Huainan Normal University, Huainan 232038, China
Ying Jing: Business School, NingboTech University, Ningbo 315100, China

IJERPH, 2022, vol. 19, issue 20, 1-18

Abstract: The first step in the application of Landsat 9 imagery is cloud screening, and the International Satellite Cloud Climatology Project (ISCCP) has made cloud screening an important part of the World Climate Research Program. The accurate identification of clouds in remote sensing satellite images containing snow and ice on the subsurface has been a challenging task in the cloud screening process. It is imperative to fully exploit the characteristic heterogeneous information of the cloud and snow subsurface, to solve the problem of cloud–snow confusion in the snow and ice environment, and to carry out research on cloud screening technology without interference from the snow and ice subsurface. In view of this, this paper will systematically carry out research on cloud screening methods in snow and ice environments. In this paper, we propose the building of a cloud screening algorithm that takes into account the difficulty of the fact that snow and ice on the subsurface can easily interfere with cloud recognition, and the influence of an empirical threshold or statistical threshold that makes its application less effective, and then establish a dynamic threshold cloud screening algorithm that is suitable for snow and ice environments. The research results will provide new ideas and perspectives to solve the problem of surface-type interference that most of the existing cloud screening algorithms contend with.

Keywords: cloud screening; complex background areas; snow/ice; mixture tuned matched filtering; improved mixed monolithic sieving model (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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