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云是地基红外高光谱仪器探测大气的重要干扰源，有效云检测不可或缺．水汽干扰和高云识别精度低是云检测面临的两个关键挑战．本文利用大气红外光谱探测仪（ASSIST）在云南丽江、西藏墨脱和西藏日土的观测数据，分析了晴空和有云条件下的光谱特征差异，并据此提出了一种光谱特征增强的机器学习云检测方法．结合同步观测的激光雷达、气象站及全天空成像仪数据，系统评估了该方法在不同相对湿度（RH）和不同云底高度（CBH）条件下的检测性能．实验结果表明：该方法与激光雷达检测结果的一致性高达97.61%．在不同RH条件下，该方法精度均优于使用原始光谱特征的方法，尤其在RH＞70%时，对晴空光谱的识别精度提升明显，从86.01%提高至91.89%．同样，在不同CBH条件下，新方法也展现出优于使用原始光谱特征方法的性能，特别在识别3 km＜CBH≤5 km的中云和CBH＞5 km的高云时，精度提升尤为明显．当3 km＜CBH≤5时，精度从95.45%提升至98.64%；当CBH＞5 km时，精度从87.5%提升至91.67%．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.
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Keywords:
- ground-based infrared hyperspectroscopy /
- remote sensing /
- machine learning /
- cloud detection
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