<p>Methane (CH₄) is a dominant driver of near-term warming, yet global emission monitoring remains constrained by slow processing and large uncertainties. Hyperspectral spectrometers enable sensitive detection of CH₄ plumes, but the relative advantages of enhancement-based (ENH) and radiance-based (RAD) approaches have not been systematically evaluated. Here we introduce a dual-path deep-learning framework that systematically compares both approaches using globally distributed, expert-validated CH₄ plume datasets from EMIT and Tanager-1. The ENH models exhibit higher segmentation accuracy across plume scales, whereas the RAD models, operating directly on 49 shortwave-infrared channels, avoid computationally expensive preprocessing (e.g., matched filtering) and enable rapid screening. Both pathways markedly reduce labor-intensive workflows and latency relative to traditional processing while maintaining competitive performance by utilizing deep learning. Explainable AI analyses demonstrate that the models learn spatial-spectral features consistent with CH₄ absorption structure and plume morphology, providing evidence of scientific validity. Cross-sensor evaluation demonstrates architectural robustness across EMIT and Tanager-1, establishing a physics-grounded framework adaptable across hyperspectral sensors.</p>