A spectral feature enhancement-driven machine learning method for cloud detection using ground-based infrared hyperspectral data

Clouds exert a significant influence on infrared radiation, making cloud detection a crucial step in the application of infrared hyperspectral data. Ground-based infrared hyperspectrometers are capable of measuring downward atmospheric thermal radiation with high temporal resolution; however, their spectral radiance measurements are strongly affected by atmospheric conditions. In particular, water vapor interference and the limited accuracy in high-cloud identification constitute two key challenges for ground-based infrared hyperspectral cloud detection. Conventional threshold-based cloud detection methods struggle to adapt to varying locations and dynamically changing atmospheric conditions, whereas machine learning approaches achieve cloud detection with higher accuracy, greater robustness, and improved automation. Building on the advantages of machine learning, observational data from the Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST) collected at Lijiang (Yunnan), Motuo (Tibet), and Ritu (Tibet) were used to analyze the spectral differences between clear-sky and cloudy conditions in this study. Based on these differences, we propose a spectral feature enhancement-driven machine learning method for cloud detection. Finally, by incorporating synchronous observations from lidar, meteorological stations, and all-sky imagers, the proposed method is systematically evaluated under varying relative humidity (RH) and cloud base height (CBH) conditions. Experimental results show that the proposed method achieves a high consistency of up to 97.61% with lidar-based detection results. Under different RH conditions, the proposed method outperforms the approach based on original spectral features. Notably, when RH＞70%, the accuracy of clear-sky spectral identification improves significantly, increasing from 86.01% to 91.89%. Similarly, under different CBH conditions, the proposed method also demonstrates superior performance compared to the approach using original spectral features. In particular, the accuracy improvements are especially notable when identifying mid-level clouds with 3 km＜CBH≤5 km, as well as high-level clouds with CBH＞5 km. When 3 km＜CBH≤5 km, the accuracy increases from 95.45% to 98.64% and when CBH＞5 km, the accuracy improves from 87.5% to 91.67%. The proposed method significantly enhances the automation and accuracy of cloud detection in ground-based infrared hyperspectral radiance data, thereby providing higher-quality fundamental datasets to support subsequent applications such as radiative transfer simulation, remote sensing parameter retrieval, and data assimilation in numerical weather prediction (NWP) models.