Generic Nighttime Cloud Detection Based on Long Wave Infrared (LWIR) and Data-Driven Model

Nighttime cloud detection of satellite imagery encounters challenges due to the limited availability of bands during night. Hence, to tack the issue of acquiring high-quality samples for nighttime cloud detection, based on the analysis of the thermal infrared band (TIN) radiation transmission process and the fact that daytime and nighttime have the same source of radiance in TIN, we propose a generic nighttime cloud detection method, which involves using daytime long wave infrared (8-14μm) data to construct samples that can be used to train data-driven models specifically designed for nighttime cloud detection. To access the applicability of this method, data from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite sensor was used to construct samples using the method and were applied for data-driven models. The results were validated by the Lidar cloud product and show that our nighttime cloud detection algorithm has higher accuracy compared to the official product (MYD35).