Effective cloud and cloud shadow detection is a critical prerequisite for accurate retrieval of concentrations of atmospheric methane (CH4) or other trace gases in hyperspectral remote sensing. This challenge is especially pertinent for MethaneSAT, a satellite mission launched in March 2024, to fill a significant data gap in terms of resolution, precision and swath between coarse-resolution global mappers and fine-scale point-source imagers of methane, and for its airborne companion mission, MethaneAIR. MethaneSAT delivers hyperspectral data at an intermediate spatial resolution (approx. 100 x 400, m), whereas MethaneAIR provides even finer resolution (approx. 25 m), enabling the development of highly detailed maps of concentrations that enable quantification of both the sources and rates of emissions. In this study, we use machine learning methods to address the cloud and cloud shadow detection problem for sensors with these high spatial resolutions. Cloud and cloud shadows in remote sensing data need to be effectively screened out as they bias methane retrievals in remote sensing imagery and impact the quantification of emissions. We deploy and evaluate conventional techniques-including Iterative Logistic Regression (ILR) and Multilayer Perceptron (MLP)-with advanced deep learning architectures, namely U-Net and a Spectral Channel Attention Network (SCAN) method. Our results show that conventional methods struggle with spatial coherence and boundary definition, affecting the detection of clouds and cloud shadows. Deep learning models substantially improve detection quality: U-Net performs best in preserving spatial structure, while SCAN excels at capturing fine boundary details... Our data and code is publicly available at: https://doi.org/10.7910/DVN/IKLZOJ

翻译：有效的云及云影检测是高光谱遥感中精确反演大气甲烷（CH₄）或其他痕量气体浓度的关键前提。这一挑战对于MethaneSAT（2024年3月发射的卫星任务）及其机载伴随任务MethaneAIR尤为重要，该任务旨在填补粗分辨率全球甲烷测绘仪与精细点源成像仪之间在分辨率、精度和幅宽方面的显著数据空白。MethaneSAT提供中等空间分辨率（约100×400米）的高光谱数据，而MethaneAIR则提供更精细的分辨率（约25米），从而能够生成高细节浓度的分布图，实现对排放源及排放速率的量化。本研究采用机器学习方法，针对具备此类高空间分辨率的传感器，解决云及云影检测问题。遥感数据中的云及云影需被有效剔除，因其会导致遥感影像中甲烷反演结果产生偏差，并影响排放量化的准确性。我们部署并评估了传统技术——包括迭代逻辑回归（ILR）和多层感知机（MLP）——以及先进的深度学习架构，即U-Net和光谱通道注意力网络（SCAN）方法。结果表明，传统方法在空间连贯性和边界定义方面存在不足，影响了云及云影的检测效果。深度学习模型显著提升了检测质量：U-Net在保持空间结构方面表现最佳，而SCAN在捕捉精细边界细节方面尤为突出……我们的数据与代码公开于：https://doi.org/10.7910/DVN/IKLZOJ