Carbon　dioxide　is　one　of　the　most　influential　greenhouse　gases　affecting　human　life.　CO2　data　can　be　obtained　through　three　methods:　ground-based,　airborne,　and　satellite-based　observations.　However,　ground-based　monitoring　is　typically　composed　of　sparsely　distributed　stations,　while　airborne　monitoring　has　limited　coverage　and　spatial　resolution;　they　cannot　fully　reflect　the　spatiotemporal　distribution　of　CO2.　Satellite　remote　sensing　plays　a　crucial　role　in　monitoring　the　global　distribution　of　atmospheric　CO2,　offering　high　observation　accuracy　and　wide　coverage.　However,　satellite　remote　sensing　still　faces　spatiotemporal　constraints,　such　as　interference　from　clouds　(or　aerosols)　and　limitations　from　satellite　orbits,　which　can　lead　to　significant　data　loss.　Therefore,　the　reconstruction　of　satellite-based　CO2　data　becomes　particularly　important.　This　article　summarizes　methods　for　the　reconstruction　of　satellite-based　CO2　data,　including　interpolation,　data　fusion,　and　super-resolution　reconstruction　techniques,　and　their　advantages　and　disadvantages,　it　also　provides　a　comprehensive　overview　of　the　classification　and　applications　of　super-resolution　reconstruction　techniques.　Finally,　the　article　offers　future　perspectives,　suggesting　that　ideas　like　image　super-resolution　reconstruction　represent　the　future　trend　in　the　field　of　satellite-based　CO2　data　reconstruction.