Dynamic threshold cloud detection algorithms for MODIS and Landsat 8 data

Abstract

Cloud detection is a key processing step before extracting information of earth surface from the earth observation data. Lots of schemes have been developed for cloud detection, static threshold method is the main method that is widely used in cloud detection. However, for the huge difference between different land objects, it is much difficult to find a proper threshold to detect the cloudy pixel from clear sky, especially, when the land covered by the thin or broken cloud. Therefore, a dynamic threshold cloud detection algorithm was proposed in this paper to improve the cloud detection. A priori monthly surface reflectance database was constructed using MODIS surface reflectance products and used to estimate the surface reflectance for dynamic threshold determination. Dynamic thresholds were determined by the simulation relationships between the apparent reflectance and the surface reflectance under clear conditions with 6S model. MODIS and Landsat 8 OLI data were selected to perform the experiments. Results showed that this new algorithm demonstrated better detection results of different cloud types over different land types.