Quantifying atmospheric CO₂ 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 CO₂ (XCO₂) retrievals from Greenhouse Gases Observing Satellite (GOSAT) and OCO-2 satellites. The best model (R² = 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 XCO₂ 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.

中文翻译：

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基于机器学习提高卫星 XCO2 检索的一致性

从太空长期量化大气中的CO ₂对于理解碳循环对气候变化的响应至关重要。然而，单颗卫星提供的时空覆盖范围有限，使得全面监测具有挑战性。此外，各种卫星检索之间的偏差阻碍了它们的直接整合。本研究提出了一种机器学习框架，用于融合从温室气体观测卫星 (GOSAT) 和 OCO-2 卫星检索的柱平均干空气 CO ₂ (XCO ₂ ) 摩尔分数。最佳模型（R ²  = 0.85）在使用 OCO-2 检索作为基准时，通过减少 71.5% 的平均月偏差，提高了 GOSAT 检索的一致性，表明融合数据集有增强观测覆盖范围的潜力。将调整后的 GOSAT XCO ₂检索纳入 OCO-2 数据集，每年平均增加 84,700 个观测值，将年度时间覆盖范围提高了 53.6%（从每个网格 14 天增加到 21.5 天）。该方法可以适用于其他卫星，最大限度地利用卫星资源，实现更稳健的碳通量反演。