Cloud Mask Detection by Combining Active and Passive Remote Sensing Data

Clouds cover nearly two-thirds of Earth’s surface, making reliable cloud mask data essential for remote sensing applications and atmospheric research. This study develops a TrAdaBoost transfer learning framework that integrates active CALIOP and passive MODIS observations to enable unified, high-accuracy cloud detection across FY-4A/AGRI, FY-4B/AGRI, and Himawari-8/9 AHI sensors. The proposed TrAdaBoost Cloud Mask algorithm (TCM) achieves robust performance in dual validations with CALIPSO VFM and MOD35/MYD35, attaining a hit rate (HR) above 0.85 and a cloudy probability of detection (PODcld) exceeding 0.89. Relative to official products, TCM consistently delivers higher accuracy, with the most pronounced gains on FY-4A/AGRI. SHAP interpretability analysis highlights that 0.47 μm albedo, 10.8/10.4 μm and 12.0/12.4 μm brightness temperatures and geometric factors such as solar zenith angles (SZA) and satellite zenith angles (VZA) are key contributors influencing cloud detection. Multidimensional consistency assessments further indicate strong inter-sensor agreement under diverse SZA and land cover conditions, underscoring the stability and generalizability of TCM. These results provide a robust foundation for the advancement of multi-source satellite cloud mask algorithms and the development of cloud data products integrated.