A Universal Dynamic Threshold Cloud Detection Algorithm (UDTCDA) supported by a prior surface reflectance database. Issue 12 (24th June 2016)

• Record Type: Journal Article
• Authors: Sun, Lin; Wei, Jing; Wang, Jian; Mi, Xueting; Guo, Yamin; Lv, Yang; Yang, Yikun; Gan, Ping; Zhou, Xueying; Jia, Chen; Tian, Xinpeng
• Is Part Of: Journal of geophysical research. Volume 121:Issue 12(2016)
• Page Start: 7172
• Page End: 7196
• Publication Date: 2016-06-24
• Subjects: cloud detection -- UDTCDA -- surface reflectance database -- MODIS -- Landsat 8 OLI; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2015JD024722
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Conventional cloud detection methods are easily affected by mixed pixels, complex surface structures, and atmospheric factors, resulting in poor cloud detection results. To minimize these problems, a new Universal Dynamic Threshold Cloud Detection Algorithm (UDTCDA) supported by a priori surface reflectance database is proposed in this paper. A monthly surface reflectance database is constructed using long‐time‐sequenced MODerate resolution Imaging Spectroradiometer surface reflectance product (MOD09A1) to provide the surface reflectance of the underlying surfaces. The relationships between the apparent reflectance changes and the surface reflectance are simulated under different observation and atmospheric conditions with the 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) model, and the dynamic threshold cloud detection models are developed. Two typical remote sensing data with important application significance and different sensor parameters, MODIS and Landsat 8, are selected for cloud detection experiments. The results were validated against the visual interpretation of clouds and Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation cloud measurements. The results showed that the UDTCDA can obtain a high precision in cloud detection, correctly identifying cloudy pixels and clear‐sky pixels at rates greater than 80% with error rate and missing rate of less than 20%. The UDTCDA cloud product overall shows less estimation uncertainty than the current MODIS cloud mask products. Moreover, the UDTCDA can effectively reduce the effects of atmospheric factors and mixed pixels and can be applied to different satellite sensors to realize long‐term, large‐scale cloud detection operations. Key Points: UDTCDA with prior surface reflectance database support is proposed UDTCDA detects clouds more effectively than current MODIS cloud mask products UDTCDA can be applied to different satellite data and achieve large‐scale and long‐term cloud detection