Machine learning based estimation of column averaged co2 from oco-2 satellite data

Excessive levels of carbon dioxide (CO2) in the atmosphere contributes to global temperature rise, and efforts are being made to limit this increase to ensure the safety of Earth's inhabitants. Satellites like GOSAT-2 and OCO-2 provide global-scale monitoring of atmospheric CO2 levels. However, cloud and aerosol occlusion result in missing data, and the spatial and temporal resolutions of these measurements are coarse. Addressing these limitations is crucial for leveraging satellite-based global CO2 monitoring to identify CO2 sources and sinks and understand their spatio-temporal evolution. In this study, we employ machine learning techniques to estimate column-averaged CO2 (XCO2) from level 2 (L2) XCO2 estimates obtained from the OCO-2 satellite, and daily XCO2 data generated using Fixed Rank Krigging (FRK) at a spatial resolution of 1(0)x1(0) is used as the target variable. Meteorological variables, known to strongly influence XCO2 distribution, are considered as covariates in the machine learning framework. To validate our estimates, we compare them with measurements from the Total Carbon Column Observing Network (TCCON) sensors. Additionally, we compare our estimates with those obtained from FRK and GEOS-L3 data. The validation against TCCON measurements and the comparison with existing data sources contribute to the evaluation and reliability of our approach.