High-Spatial-Resolution Estimation of XCO2 Using a Stacked Ensemble Model

One of the leading causes of climate change and global warming is the rise in carbon dioxide (CO2) levels. For a precise assessment of CO2’s impact on the climate and the creation of successful mitigation methods, it is essential to comprehend its distribution by analyzing CO2 sources and sinks, which is a challenging task using sparsely available ground monitoring stations and airborne platforms. Therefore, the data retrieved by the Orbiting Carbon Observatory-2 (OCO-2) satellite can be useful due to its extensive spatial and temporal coverage. Sparse and missed retrievals in the satellite make it challenging to perform a thorough analysis. This work trains machine learning models using the Orbiting Carbon Observatory-2 (OCO-2) XCO2 retrievals and auxiliary features to obtain a monthly, high-spatial-resolution, gap-filled CO2 concentration distribution. It uses a multi-source aggregated (MSD) dataset and the generalized stacked ensemble model to predict country-level high-resolution (1 km2) XCO2. When evaluated with TCCON, this country-level model can achieve an RMSE of 1.42 ppm, a MAE of 0.84 ppm, and R2 of 0.90.