Preliminary Top-Down Remote Sensing-Based Modeling of Facility-Level Methane Emission Attribution in the Oil and Gas Sector

Industrial parks are major GHG sources and key actors in mitigation. Although satellite remote sensing has advanced since 2020—driven by initiatives like the Global Methane Pledge—it still excels mainly at isolated, strong methane point sources and struggles with dense source clusters, making facility-level attribution difficult. Two issues dominate: (1) the spectral–spatial trade-off—together with limited spectral resolution and SNR of current hyperspectral sensors—constrains XCH₄ precision, pushing weak-source enhancements below retrieval noise; and (2) spatial overlap in large parks masks weak signals with nearby strong emitters. Even so, long-term matched-filter time series retain valuable, if hard-to-quantify, information.

We introduce an adaptive framework to apportion sub-source contributions within complex parks. The approach fuses sensors across scales: Sentinel-5P/TROPOMI constrains park-level totals, then time-series AHSI observations attribute emissions to individual facilities. This satellite-based method enables transparent, accurate facility-scale GHG reporting for industrial parks, supporting mitigation planning and the energy transition.