Improved Consistency of Satellite XCO <sub>2</sub> Retrievals Based on Machine Learning
Xiaoting Huang, Zhu Deng, Fei Jiang, Minqiang Zhou, Xiaojuan Lin, Zhu Liu, Muyan Peng
Abstract
Abstract Quantifying atmospheric CO 2 over long periods from space is crucial in understanding the carbon cycle's response to climate change. However, a single satellite offers limited spatiotemporal coverage, making comprehensive monitoring challenging. Moreover, biases among various satellite retrievals hinder their direct integration. This study proposed a machine learning framework for fusing the column‐averaged dry‐air mole fraction of CO 2 (XCO 2 ) retrievals from Greenhouse Gases Observing Satellite (GOSAT) and OCO‐2 satellites. The best model ( R 2 = 0.85) presented improved consistency of GOSAT retrievals by reducing 71.5% of the average monthly bias while using OCO‐2 retrievals as a benchmark, indicating the fusion data set's potential to enhance observation coverage. Incorporating the adjusted GOSAT XCO 2 retrievals into the OCO‐2 data set added an average of 84.7 thousand observations annually, enhancing the yearly temporal coverage by 53.6% (from 14 to 21.5 days per grid). This method can be adapted to other satellites, maximizing satellite resources for a more robust carbon flux inversion.