Assessing the detection potential of targeting satellites for global greenhouse gas monitoring: insights from TANGO orbit simulations

Abstract. Targeting satellite observations offer a promising avenue for detecting and quantifying anthropogenic greenhouse gas (GHG) emissions from localized point sources at high spatial resolution. In this study, we assess the detection potential of the Twin ANthropogenic Greenhouse gas Observers (TANGO) satellite mission, scheduled for 2028, using orbit simulations and the TNO global point source (GPS) inventory. We examine its target selection approach across three observational scenarios, clear sky, cloud filtered, and cloud forecast, by applying two prioritization schemes (one favouring CH4 point sources over CO2 and the other vice versa). Results show that, under current detection limits (TDLs), TANGO can detect a large fraction of major point sources, identifying ∼500 targets per repeat cycle, depending on the prioritization scheme employed. However, cloud cover significantly reduces observational yield (∼ 64 %–68 % fewer detections). Integrating a cloud-forecast-informed target selection improves the total number of detected targets by 34.6 % under CO2 prioritization and 22.1 % under CH4 prioritization compared to the cloud-filtered scenario, demonstrating the benefits of adaptive observation strategies. We also explore a hypothetical enhanced detection limit (EDL) scenario, representing the potential for future satellites with improved sensitivity. While EDL extends the range of observable sources, many of these smaller emitters are associated with greater uncertainties, highlighting the importance of well-characterized retrieval precision. Finally, we discuss the potential benefits of a satellite constellation, which could enhance revisit times and observational frequency for sources of key interest. Our results demonstrate TANGO as a case study for the capabilities and challenges of next-generation targeting satellite missions, highlighting the importance of high-resolution GHG monitoring and cloud-aware adaptation for improving global emission quantification.