Pais, S.M.Bhattacharjee, S.Anand Kumar, M.Chen, J.2026-02-042024IEEE Geoscience and Remote Sensing Letters, 2024, 21, , pp. 1-51545598Xhttps://doi.org/10.1109/LGRS.2024.3379204https://idr.nitk.ac.in/handle/123456789/21433One of the well-known greenhouse gases (GHGs) produced by anthropogenic human activity is carbon dioxide (CO<inf>2</inf>). Understanding the carbon cycle and how negatively it affects the ecosystem requires analysis of the rise in CO<inf>2</inf> concentration. This work aims to map CO<inf>2</inf> concentration for the entire surface, making it useful for regional carbon cycle analysis. Here, column-averaged CO<inf>2</inf> dry mole fraction, called XCO<inf>2</inf>, measured by the orbiting carbon observatory-2 (OCO-2) satellite, is used. Because of spectral interference by the clouds and aerosols, there are many missing footprints in the Level-2 swath of OCO-2, making it disruptive to understand any assessment related to the carbon cycle. The objective of this work is to predict 1 km2 XCO<inf>2</inf> using data resampling and machine learning models. This work achieves a minimum mean absolute error (MAE) and root mean square error (RMSE) of 0.3990 and 0.8090 ppm, using the monthly models. © 2004-2012 IEEE.Analytical modelsData fusionGreenhouse gasesInterpolationLearning systemsOrbitsRemote sensingSatellites>Computational modellingData resamplingDown-scalingGap fillingKrigingLand coverMachine-learningODIACOrbiting carbon observatory-2Predictive modelsRegressorResamplingSIF<sub xmlns:ali="Spatial resolutionXCO<sub xmlns:ali="Xmlns:mml="Xmlns:xlink="Xmlns:xsi="Carbon dioxideartificial intelligencecarbon cycledownscalingestimation methodhuman activityinterpolationkrigingmachine learningsatellite imageryspatiotemporal analysis