Since the Industrial Revolution, both natural and anthropogenic factors have contributed to the increase in greenhouse gas emissions, resulting in a series of environmental issues such as global warming.The global oceans have been confirmed as the primary sink for atmospheric CO₂, capable of absorbing approximately one-fourth of anthropogenic CO₂ emissions. In contrast, inland water bodies, acting as a source of atmospheric CO₂, emit greenhouse gases equivalent to nearly 20% of global fossil fuel CO₂ emissions.The accurate estimation of surface water CO₂ partial pressure （pCO₂） is a prerequisite for studying the carbon flux and source-sink patterns in various water bodies. Since the 1990s, researchers have accumulated a substantial amount of measured pCO₂ values. This solid dataset has laid the foundation for a deeper understanding of the influencing mechanisms of water body pCO₂ and the development of estimation models.Remote sensing, with its capability for large-scale and long-term observations, is currently the mainstream method for estimating water body pCO₂. This involves inferring pCO₂ through the remote sensing retrieval of relevant environmental variables. This paper begins by elucidating the environmental variables and relevant physicochemical processes that influence water body pCO₂, providing the theoretical foundation for parameterizing pCO₂. Subsequently, it summarizes remote sensing retrieval algorithms for pCO₂ in different types of water bodies. While these algorithms have matured in studies over the open oceans and developed a series of empirical or semi-analytical models for nearshore waters, there is relatively less research on remote sensing retrieval algorithms for inland water bodies. This scarcity can be attributed to the complex optical properties and spatiotemporal variability of pCO₂ in inland water bodies. Given the significance of inland water bodies in the global carbon cycle, researchers should pay greater attention to remote sensing estimation studies of pCO₂ in these inland water systems.