Methane (CH4) is one of the most significant greenhouse gases responsible for about one-third of climate warming since preindustrial times, originating from various sources. Landfills are responsible for a large percentage of CH4 emissions, and population growth can boost these emissions. Therefore, it is vital to automate the process of CH4 monitoring over landfills. This study proposes a convolutional neural network (CNN) with an Atrous Spatial Pyramid Pooling (ASPP) mechanism, called PRISMethaNet, to automate the CH4 detection process using PRISMA satellite data in the 400–2500 nm spectral range. A total number of 41 PRISMA images from 17 landfill sites located in several countries, such as India, Nigeria, Mexico, Pakistan, Iran, and other regions, were used as our study areas. The PRISMethaNet model was trained using augmented data as the input, and plume masks were obtained from the matched filter (MF) algorithm. This novel proposed model successfully detected plumes with overall accuracy (OA), F1-score (F1), precision, and recall of 0.99, 0.96, 0.93, and 0.99, respectively, and quantification uncertainties ranging from 11 % to 58 %. An unboxing of the ASPP module using Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm demonstrated a strong relationship between larger dilation rates (DRs) and CH4 plume detectability. Importantly, the results highlighted that plume masks obtained by PRISMethaNet provided more accurate CH4 quantification rate compared to the statistical methods used in previous studies. In particular, the mean square error (MSE) for PRISMethaNet was approximately 1,102 kg/h, whereas the MSE for the commonly used statistical method was around 1,974 kg/h.

中文翻译：

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PRISMethaNet：一种使用 PRISMA 卫星数据进行垃圾填埋场甲烷检测的新型深度学习模型


甲烷 （CH4） 是自前工业化时代以来导致约三分之一气候变暖的最重要的温室气体之一，其来源多种多样。垃圾填埋场造成了很大一部分 CH4 排放，而人口增长会加剧这些排放。因此，对垃圾填埋场的 CH4 监测过程进行自动化至关重要。本研究提出了一种具有 Atrous 空间金字塔池化 （ASPP） 机制的卷积神经网络 （CNN），称为 PRISMethaNet，以使用 400-2500 nm 光谱范围内的 PRISMA 卫星数据自动化 CH4 检测过程。来自印度、尼日利亚、墨西哥、巴基斯坦、伊朗和其他地区等多个国家的 17 个垃圾填埋场的 41 张 PRISMA 图像共被用作我们的研究区域。PRISMethaNet 模型使用增强数据作为输入进行训练，并从匹配过滤器 （MF） 算法获得羽流掩码。这个新颖的模型成功地检测到了羽流，总体准确率 （OA）、F1 分数 （F1）、精密度和召回率分别为 0.99、0.96、0.93 和 0.99，量化不确定性从 11% 到 58% 不等。使用梯度加权类激活映射 （Grad-CAM） 算法对 ASPP 模块进行拆箱，表明较大的膨胀率 （DRs） 与 CH4 羽流可检测性之间存在很强的关系。重要的是，结果强调，与以前研究中使用的统计方法相比，通过 PRISMethaNet 获得的羽流面罩提供了更准确的 CH4 量化率。特别是，PRISMethaNet 的均方误差 （MSE） 约为 1,102 kg/h，而常用统计方法的 MSE 约为 1,974 kg/h。