Satellite remote sensing platforms can collect measurements on a global scale within a few days, which provides an unprecedented opportunity to characterize and understand the spatio-temporal variability of environmental variables. Because of the additional challenges of making precise and accurate measurements from space, it is essential to validate satellite remote sensing datasets with highly precise and accurate ground-based measurements. The focus of this article is on two sets of measurements: Atmospheric column-averaged carbon dioxide (CO2) collected by the Orbiting Carbon Observatory-2 (OCO-2) mission in its target mode of operation; and ground-based data used for validation from the Total Carbon Column Observing Network (TCCON). The current statistical modeling of the relationship between the less-precise OCO-2 satellite data (Y) and the more-precise TCCON ground-based data (X) assumes a linear regression and heteroscedastic measurement errors that reside in both the OCO-2 data and the TCCON data. To obtain consistent estimates of the regression coefficients, it is critical to determine the error variance of each datum in the regression. In this article, a rigorous statistical procedure is presented for obtaining these error variances through modeling the spatial and/or temporal dependence structure in the OCO-2 and TCCON datasets. Numerical results for analyzing data at the Lamont TCCON station and the corresponding OCO-2 target-mode data (orbit number 3590) illustrate our procedure.