Accurate estimation of carbon removal by terrestrial ecosystems and oceans is crucial to the success of global carbon mitigation initiatives. The emergence of multi-source CO₂ observations offers prospects for an improved assessment of carbon fluxes. However, the utility of these diverse observations has been limited by their heterogeneity, leading to much variation in estimated carbon fluxes. To harvest the diverse data types, this paper develops a multi-observation carbon assimilation system (MCAS), which simultaneously integrates both satellite and ground-based observations. MCAS modifies the ensemble Kalman filter to apply different inflation factors to different types of observation errors, addressing the heterogeneity between satellite and in situ data. In commonly used independent validation datasets, the carbon flux derived from MCAS outperformed those obtained from a single source, demonstrating a 20% reduction in error compared to existing carbon flux products. We use MCAS to conduct ecosystem and ocean carbon flux inversion for the period of 2016–2020, which reveals that the 5-year average global net terrestrial and ocean sink was 1.84 ± 0.60 and 2.74 ± 0.49 petagrams, absorbing approximately 47% of human-caused CO₂ emissions together, which were consistent with the global carbon project estimates of 1.82 and 2.66 petagrams. All these facts suggest MCAS is a better methodology than those for assimilating single-source observation only.

准确估计陆地生态系统和海洋的碳去除量对于全球碳减排计划的成功至关重要。多源 CO₂ 观测的出现为改进碳通量评估提供了前景。然而，这些不同观测的效用受到其异质性的限制，导致估计的碳通量变化很大。为了收集不同的数据类型，本文开发了一种多观测碳同化系统 （MCAS），该系统同时整合了卫星和地面观测。MCAS 修改了集成卡尔曼滤波，将不同的膨胀因子应用于不同类型的观测误差，解决了卫星和原位数据之间的异质性。在常用的独立验证数据集中，从 MCAS 得出的碳通量优于从单一来源获得的碳通量，与现有的碳通量产品相比，误差减少了 20%。我们使用 MCAS 对 2016-2020 年期间的生态系统和海洋碳通量进行反演，结果显示全球陆地和海洋净汇 5 年平均为 1.84 ± 0.60 和 2.74 ± 0.49 拍克，共吸收了约 47% 的人_{为二氧化碳排放，}这与全球碳项目估计的 1.82 和 2.66 拍克一致。所有这些事实都表明，MCAS 是一种比仅吸收单一来源观察更好的方法。