ML Model (DNNResNet) Satellite XCO2

India often faces challenges in monitoring atmospheric carbon dioxide (CO₂) through satellite observations due to persistent cloud cover, especially during the monsoon season. This limitation affects the continuous tracking of carbon and hinders accurate assessments of carbon-climate feedback. To address this, we developed a high-resolution (0.25°) monthly column-averaged CO₂ (XCO₂) dataset for 2003-2020 using a Machine Learning (ML)-based Deep Neural Network (DNN) downscaling and integration of three satellite retrievals of XCO₂ (SCanning Imaging Absorption SpectroMeter for Atmospheric ChartographY; SCIAMACHY, Greenhouse gases Observing SATellite; GOSAT and Orbiting Carbon Observatory; OCO-2) across India. The ML-predicted XCO₂ shows strong agreement with OCO-2 data for 2018-2020 (correlation coefficient, CC > 0.9; standard deviation: 0.39 ppm), and latitudinal biases range within ±2 ppm.