甲烷分析与EMIT羽流定位模型（Methane Analysis and Plume Localization with EMIT model）是一种深度学习框架，可在全球范围内自动实现甲烷羽流的检测、增强、量化及源识别，将原始卫星数据转化为可扩展的气候行动依据。甲烷是一种强效温室气体；在100年时间尺度上，其全球增温潜势是二氧化碳的30倍。事实上，自工业革命以来，甲烷已导致约25%的人为引起的全球变暖。

The Methane Analysis and Plume Localization with EMIT model is a deep-learning framework that automates the detection, enhancement quantification, and source estimation of methane plumes globally, turning raw satellite data into scalable climate action. Methane is a potent greenhouse gas; over a 100-year timeframe, its warming potential is 30 times greater than that of carbon dioxide. In fact, it has driven approximately 25% of human-induced warming since the start of the industrial era.

甲烷分析与EMIT羽流定位模型（MAPL-EMIT）是一种深度学习框架，可在全球范围内自动完成甲烷羽流的检测、增强量化及源定位，将原始卫星数据转化为可扩展的气候行动依据。甲烷是一种强效温室气体；在100年时间尺度上，其全球增温潜势（GWP）是二氧化碳的30倍。事实上，自工业革命以来，甲烷已导致约25%的人为全球变暖。由于甲烷在大气中的存留时间相对较短，及时削减其排放，便成为减缓全球升温的一条关键“快速行动”路径。这一紧迫性体现在《全球甲烷承诺》中——目前已有逾125个国家承诺到2030年将甲烷排放量减少30%。为实现上述目标，我们必须赋能各利益相关方，持续追踪废弃物、农业和能源三大领域中局地点源（空间尺度仅为数十米量级）的排放情况。最具成本效益的减排策略，集中于油气基础设施、农业设施及垃圾填埋场。 来自太空的全球甲烷探测视图。当放大观察时，MAPL-EMIT模型可精准标定特定甲烷羽流，并揭示关键信息，例如排放源位置。 从太空测量甲烷需兼顾三大要素：（1）视场角（空间覆盖范围/重访周期），（2）空间分辨率，以及（3）光谱分辨率。以TROPOMI为代表的全球测绘载荷，旨在通过高覆盖（约2600 km幅宽）、粗空间分辨率（约5.5 km × 3.5 km）与精细光谱采样（0.1 nm）相结合的方式，探测背景甲烷浓度的微小变化。相比之下，以EMIT为代表的点源测绘载荷则擅长在设施尺度上测量甲烷排放：其采用中等覆盖（80 km幅宽）、极高空间分辨率（60米）与中等光谱分辨率（7.4 nm光谱采样），足以在高信噪比条件下捕捉甲烷的化学特征信号。 然而，要全面释放此类丰富数据在全球尺度上的潜力，仍面临额外挑战。地球多样的地表景观构成复杂背景，部分地表物质甚至可能模拟出类似甲烷的光谱响应，致使较小或更弥散的排放源识别尤为困难。为延续EMIT团队的基础性工作，并推动高通量全球制图能力，我们与该团队合作，应用深度学习模型以理解场景更广阔的视觉上下文。此项工作契合谷歌地球人工智能（Google Earth AI）的整体战略——即依托一系列地理空间模型与数据集，将行星尺度数据转化为可操作的智能洞察。通过在大规模卫星影像上部署深度学习技术，我们旨在以面向特定环境监测的专业化工具，补充并强化更广泛的行星人工智能倡议。 发现不可见之物。本对比直观呈现了甲烷探测的挑战：标准可见光影像无法显示气体；NASA L2B增强结果（经匹配滤波处理）仅呈现噪声信号；而MAPL-EMIT模型则能清晰分离甲烷羽流与背景地表。尤为关键的是，这种空间感知能力使模型得以解析高度复杂的场景。在密集工业区，邻近多个设施的排放常融合为单一云团。为厘清此类情形，MAPL-EMIT同步执行三项独立任务： 解耦复杂排放。通过分析空间上下文，MAPL-EMIT可在密集工业区域中，同时精确勾勒多个重叠甲烷羽流的轮廓，并准确定位各自排放源（以“X”标记）。 基于合成现实开展训练。由于现实中缺乏数以百万计的标注甲烷羽流样本，我们通过将基于物理模型生成的模拟羽流（涵盖多样化的排放速率与湍流大气条件）直接注入真实EMIT高光谱场景，完成模型训练。该策略不仅提升了模型灵敏度，使其能够稳定捕获更微弱的排放信号，从而突破当前探测极限；亦验证了其在复杂环境下的鲁棒性——成功对全球25个最高排放垃圾填埋场中的24个完成羽流测绘。 长期追踪持续排放。MAPL-EMIT对约旦安曼一处大型垃圾填埋场开展的时间序列甲烷探测，成功捕捉其持续释放的羽流，进一步印证了该模型在复杂环境下的高灵敏度与强鲁棒性。 MAPL-EMIT彰显了一次卓有成效的协作：谷歌的机器学习专长与美国国家航空航天局喷气推进实验室（NASA JPL）合作伙伴的领域知识深度融合。双方携手推进空间甲烷观测在设施尺度上的全部潜能，为全球社会提供切实可行的工具，助力实质性减少温室气体排放。

The Methane Analysis and Plume Localization with EMIT model is a deep-learning framework that automates the detection, enhancement quantification, and source estimation of methane plumes globally, turning raw satellite data into scalable climate action. Methane is a potent greenhouse gas; over a 100-year timeframe, its warming potential is 30 times greater than that of carbon dioxide. In fact, it has driven approximately 25% of human-induced warming since the start of the industrial era. Because methane has a relatively short atmospheric lifespan, promptly reducing these emissions offers a critical "fast-action" pathway to mitigating global temperature rise. This urgency is reflected in the Global Methane Pledge, where over 125 countries have committed to a 30% emissions reduction by 2030. To hit these targets, we must empower stakeholders to track localized point sources (emissions occurring from a small spatial footprint on the order of a few tens of meters) across the waste, agriculture, and energy sectors. The most cost-effective strategies are to mitigate emissions from oil and gas infrastructure, agricultural facilities, and landfills. A global view of methane detections from space. As we zoom in, the MAPL-EMIT model highlights specific methane plumes, revealing critical details such as source location. Measuring methane from space requires balancing three key factors: (1) field of view (spatial coverage/revisit), (2) spatial resolution, and (3) spectral resolution. Global mappers like TROPOMI were designed to detect small changes in background methane concentrations by integrating high coverage (approximately 2,600 km swath width), coarse spatial resolution (around 5.5 km x 3.5 km), and fine spectral sampling (0.1 nm). In contrast, point source mappers like EMIT excel at measuring methane emissions at the facility scale. They achieve this by combining moderate coverage (an 80 km wide field of view) with very high spatial resolution (60 meters) and a moderate spectral resolution (7.4 nm spectral sampling), sufficient to capture the chemical signature of methane at a high signal to noise ratio. However, fully unlocking the potential of this rich data at a global scale presents additional challenges. The Earth's varied landscapes provide a complex backdrop, and some surface materials can masquerade as methane, making the identification of smaller or more diffuse sources particularly challenging. To build on the EMIT team's foundational work and enable high-throughput global mapping, we collaborate with them to apply deep-learning models that can understand the broader visual context of the scene. This work aligns with Google’s broader effort behind Google Earth AI, our collection of geospatial models and datasets to turn planetary data into actionable intelligence. By applying deep learning to satellite imagery at scale, we aim to complement broader planetary AI initiatives with specialized tools for targeted environmental monitoring. Spotting the invisible. This comparison demonstrates the challenge of methane detection: standard visible imagery shows no gas, NASA L2B enhancements (from matched filter) reveal a noisy signal, but the MAPL-EMIT model clearly isolates the methane plume from the background landscape. Crucially, this spatial awareness empowers the model to untangle highly complex scenes. In dense industrial regions, emissions from multiple neighboring facilities often merge into a single cloud. To make sense of these scenarios, MAPL-EMIT simultaneously solves three distinct tasks: Disentangling complex emissions. By analyzing spatial context, MAPL-EMIT simultaneously delineates the exact shape of multiple, overlapping methane plumes and pinpoints their respective source origins (marked with an X), even in dense industrial regions. Training with synthetic reality. Because millions of labeled, real-world methane plumes do not exist, we trained the model by injecting physics-based simulated plumes, representing diverse emission rates and turbulent atmospheric conditions, directly into real EMIT hyperspectral scenes. This increased sensitivity also allows MAPL-EMIT to reliably capture weaker emissions, improving on current detection limits. The model also proved robust in complex environments, successfully mapping plumes at 24 of the world's 25 top-emitting landfills. Tracking persistent emissions over time. A time-series of MAPL-EMIT methane detections successfully capturing plumes originating from a major landfill in Amman, Jordan, demonstrating the model's high sensitivity and robustness in complex environments. MAPL-EMIT showcases a powerful collaboration, bringing together Google's machine learning expertise with the domain knowledge of our collaborators at NASA JPL. Together, we are advancing the full potential of space-based methane observations at the facility scale, providing the global community with the tools necessary to enable meaningful action on reducing greenhouse gas emissions.