Satellite retrievals of atmospheric greenhouse gas columns are used to obtain information about greenhouse gas sources and sinks by inverse modeling. Such an application requires high accuracy, as even small biases of the retrieved concentrations may result in large errors of the inferred rates of surface emissions (source) and deposition, surface uptake or removal in the atmosphere (sinks). For example, for the upcoming satellite mission dedicated to carbon dioxide monitoring (CO2M), co-funded by ESA and the European Commission for the Copernicus Programme, the accuracy of the dry-air column-averaged CO₂ mole fraction (XCO₂) is required to be better than 0.5 ppm. Here we investigate a potentially important systematic error source, namely XCO₂ biases due to correlated sub-pixel variability of surface reflectance (albedo) and altitude. To minimize this error source we propose the use of an albedo-weighted surface altitude which better represents the satellite’s spatial sample than the unweighted average by using a linearized theoretical analysis. We use Copernicus Sentinel-2 data combined with Copernicus Digital Elevation Model (DEM) data and the Fast atmOspheric traCe gAs retrievaL (FOCAL) algorithm and create a variety of self-consistent experiments to test this theory. First, we conduct experiments with defined conditions and second, we apply the methodology to some real-world examples: the Bełchatów power plant in Poland, the Black Forest in Germany, the region around Mont Blanc in the European Alps and the whole country of Germany. In all these examples, we find that using the albedo-weighted average of the surface altitude reduces biases at locations with heterogeneous surface structure to values below the requirements for future satellite missions. In addition, we developed a possible post-processing equation to account for this process, because high-resolution albedo currently is not measured simultaneously with satellite instruments. We also find that filter parameters connected to the surface roughness might be relaxed when using the albedo-weighted surface altitude in the retrievals. Furthermore, we examine the dependence of the XCO₂ errors on the size of the spatial samples and find that the errors become larger with larger spatial samples, but are generally smaller by more than a factor of four when using the albedo-weighted instead of the unweighted average of the surface altitude. In conclusion, we show that the use of the albedo-weighted surface altitude in the retrieval process results in significant reduction of the XCO₂ bias compared to the use of the unweighted mean altitude, as currently used in most retrieval schemes.