A New Cloud Detection Method Supported by GlobeLand30 Data Set

Abstract

In terms of traditional threshold methods, uniform thresholds are used for cloud detection based on remote sensing images; however, due to complex surface structures and cloud conditions, such an approach is typically difficult to effectively implement for high-precision cloud detection. To solve this problem, a new cloud detection algorithm is proposed based on global land cover data. Specifically, a high spatial-resolution at 30-m Global Land Cover Data set with global coverage was employed as background data for image inversions, which further supported cloud detection in remote sensing images. Notably, threshold settings can be varied for different land cover types. Such an algorithm can effectively improve the accuracy of cloud pixel identification for thin and broken clouds, even over bright areas. Moreover, Landsat 5 data are used to perform cloud detection experiments based on this algorithm. The thresholds are considering land cover variations. The thresholds of land cover types spatiotemporally vary, such as vegetation, differed by latitude and over time. In addition, six common land cover types are selected for cloud detection experiments. Then, validations analyses are conducted through visual interpretation and the results indicated that the algorithm is capable of achieving a high cloud detection accuracy. Specifically, the overall RMSE of cloud cover is 4.44%, and the accuracies of cloud and clear-sky pixel identifications is 86.5% and 98.7%, respectively.