Enhancing nighttime cloud detection for moderate resolution imagers using a transformer based deep learning network

Accurate cloud detection is essential for the quantitative applications of satellite imager observations, but nighttime cloud detection has challenges due to limited spectral bands, for example, physical methods using only infrared (IR) bands without using spatial textures as input for cloud detection often result in high uncertainties, especially in some situations such as cryosphere surface. Although numerous segmentation-style deep learning cloud detection algorithms have proposed in previous studies, they are inadequate for nighttime due to the difficulty in acquiring two-dimensional truth data for training and validation. To overcome these challenges, the Transformer based Nighttime Cloud Detection (TNCD) framework, which integrates spatial features and utilizes an advanced Transformer architecture with relative position encoding, layer scaling, and channel attention mechanisms, is proposed and investigated for nighttime cloud detection. The model was trained on labels derived from CALIOP data, utilizing a dataset comprising nearly one hundred million segments from MODIS. Independent validation indicates that TNCD achieves robust and consistent performance across various scenarios, with an overall accuracy (OA) of 93.26 % and over 90 % in cryosphere regions. The proposed algorithm avoids the pattern noise appeared in the traditional physical methodology due to the utilization of auxiliary data at coarser resolutions, it also mitigates the negative impact of stripes in IR images for cloud detection. Moreover, TNCD shows high transferable practicability across sensors, with over 90 % OA for MERSI. More importantly, our research underscores the importance of water vapor absorption bands for nighttime cloud detection over the cryosphere. TNCD's high accuracy and robustness provide unique methodology that could be used operationally for nighttime cloud detection.