Abstract
The Landsat 8 Operational Land Imager (OLI) is a high-resolution satellite sensor that is carried on the next-generation Landsat which was launched by the National Aeronautics and Space Administration (NASA) in 2013. Compared to the previous land observation satellites in this series, Landsat 8 OLI has been further optimized in regard to its band setting and data acquisition frequency. The application scope of the data and the ability of information extraction have been further expanded and enhanced. However, the existence of clouds reduces the efficiency and the quality of satellite data. Therefore, in order to identify cloud pixels with a high level of accuracy and efficiency, a variety of cloud detection methods have been developed for Landsat 8 OLI sensors. Distinct treatment methods have been adopted for identifying different surface backgrounds and various types of cloud characteristics in order to improve the accuracy of cloud detection. The present study compares and analyzes the detection results from five typical cloud detection algorithms for different cloud types over different ground types. In addition, it discusses the various cloud detection algorithms used for different types of adaptive backgrounds and clouds in order to shed light upon the comprehensive application of different methods. Subsequently, these findings may provide a reference for the development of an algorithm with a higher precision of cloud detection.
Highlights
Clouds cover approximately 67% of the earth’s surface, of which the amount of cloud over land and ocean is approximately 55% and 72%, respectively [1]
The existence of clouds reduces the quality of remote sensing images [2], and affects the accuracy and reliability of the inversion of surface and atmospheric parameters [3,4,5,6,7]
Five cloud detection algorithms were utilized to detect the data for different surface types of Landsat 8 Operational Land Imager (OLI)
Summary
Clouds cover approximately 67% of the earth’s surface, of which the amount of cloud over land and ocean is approximately 55% and 72%, respectively [1]. The existence of clouds reduces the quality of remote sensing images [2], and affects the accuracy and reliability of the inversion of surface and atmospheric parameters [3,4,5,6,7]. The most common cloud detection method is the threshold method, which sets specific thresholds for different bands or band combinations to distinguish clouds from the typical surface. It utilizes the reflectivity difference between clouds and the typical surface in visible to near-infrared bands and the brightness and temperature difference between mid-infrared and thermal infrared bands.
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