Methane (CH4) plays a critical role in accelerating global climate change, and recent advancements using Sentinel-2 satellite imagery have demonstrated potential in detecting and quantifying significant methane emissions. However, existing approaches often rely on temporal analysis of shortwave-infrared spectra, assuming consistent ground conditions and prior knowledge of methane-free periods, which can lead to errors and limit scalability. To overcome these challenges, we present Methane SatMapper, an innovative end-to-end spectral transformer model specifically designed to accurately identify and quantify methane plumes. Our model introduces two novel modules: one that identifies potential methane emission sites by analyzing solar radiation absorption in the spectral domain and another that localizes and quantifies methane plumes without the need for temporal data. By utilizing all 12 spectral channels of Sentinel-2 imagery, our architecture effectively estimates ground terrain and detects methane emissions, providing enhanced robustness to variable ground conditions and increased computational efficiency by eliminating the need for historical time-series data. Primary evaluations confirm that Methane SatMapper delivers precise and reliable methane detection, addressing key limitations in scalability and temporal dependence.