Geological CO2 degassing is a fundamental Earth process but still quite poorly understood, since a thorough quantification with conventional measurement techniques is challenging. Optical remote sensing techniques have the potential to extend conventional measurement capabilities, enabling new insights into processes related to Earth’s CO2 degassing. This article provides an integrated and pragmatic overview of existing and future remote sensing approaches suitable for geological CO2 quantification and connects results from instrument research in optical remote sensing with possible applications in the Earth sciences. The paper aims to provide intercomparison by means of key parameters of very different remote sensing approaches. One of these parameters is the minimum detectable CO2 flux, which is estimated for each remote sensing method herein. This may be used to identify a remote sensing platorm for a specific Earth science problem related to CO2 degassing. Six such prominent Earth science problems are detailed. Remote sensing technology for extraterrestrial CO2 degassing is briefly examined. With respect to established in situ measurements, the main benefits of remote sensing include a safe measurement distance, spatially inclusive probing and swift measurements, while the main shortcomings include a generally lower measurement precision and the lack of commercially available turnkey solutions. While all six Earth science problems examined in this review will benefit to some extent from CO2 remote sensing, remote sensing is unlikely to replace conventional in situ probing entirely in the near future, but can be seen as complementary to conventional measurement approaches. Earth Science problems that could immediately benefit from CO2 remote sensing include a comprehensive survey of the significant but highly uncertain CO2 flux of the East African Rift System, comparing volcanic CO2 concentrations from satellite borne remote sensing with ground-based remote sensing, and integration of CO2 remote sensing data into automated volcanic unrest prediction.