As the most abundant greenhouse gas in the atmosphere, CO₂ has a significant impact on climate change. Therefore, the determination of the temporal and spatial distribution of CO₂ is of great significance in climate research. However, existing CO₂ monitoring methods have great limitations, and it is difficult to obtain large-scale monitoring data with high spatial resolution, thus limiting the effective monitoring of carbon sources and sinks. To obtain complete Chinese daily-scale CO₂ information, we used OCO-2 XCO₂ data, Carbon Tracker XCO₂ data, and multivariate geographic data to build a model training data set, which was then combined with various machine learning models including Random Forest, Extreme Random Forest, XGBoost, LightGBM, and CatBoost. The results indicated that the Random Forest model presented the best performance, with a cross-validation R² of 0.878 and RMSE of 1.123 ppm. According to the final estimation results, in terms of spatial distribution, the highest multi-year average RF XCO₂ value was in East China (406.94 ± 0.65 ppm), while the lowest was in Northwest China (405.56 ± 1.43 ppm). In terms of time, from 2016 to 2018, the annual XCO₂ in China continued to increase, but the growth rate showed a downward trend. In terms of seasonal effects, the multi-year average XCO₂ was highest in spring (407.76 ± 1.72 ppm) and lowest in summer (403.15 ± 3.36ppm). Compared with the Carbon-Tracker data, the XCO₂ data set constructed in this study showed more detailed spatial changes, thus, can be effectively used to identify potentially important carbon sources and sinks.

In the context of global warming, the Wuhan Urban Agglomeration is actively responding to China’s carbon peak and carbon neutrality goals and striving to achieve a reduction in carbon sources and an increase in carbon sinks. Therefore, it is critical to investigate carbon emissions from land use. This study uses the carbon emission coefficient method to calculate carbon emissions from land use in the Wuhan Urban Agglomeration, analyzes its temporal and spatial changes and differences in urban structure, and couples with the Markov–PLUS model to simulate and predict the carbon emissions of four scenarios of land use in 2035. The research found the following: (1) during the Wuhan “1+8” City Circle stage, carbon sources and emissions increased steadily, with average annual growth rates of 1.92% and 1.99%, respectively. Carbon sinks remained stable and then decreased, with an average annual growth rate of −0.46%. (2) During the Wuhan Metropolitan Area stage—except for 2020 and 2021, which were affected by COVID-19—carbon sources, sinks, and emissions continued to grow in general, and the average annual growth rates increased to 4.46%, 1.58%, and 4.51%, respectively. (3) In terms of urban structure differences, Wuhan is a high-carbon optimization zone; Xianning, Huangshi, and Huanggang are ecological protection zones; other cities, such as Ezhou, Xiaogan, and Xiantao are comprehensive optimization zones; and there is no low-carbon development zone. (4) The multi-scenario simulation results show that carbon sources and emissions are the highest under the economic development scenario, with values of 100.2952 and 9858.83 million tons, respectively, followed by cropland protection, natural development, and low-carbon development scenarios. Under low-carbon development, carbon sinks were the highest, with values of 1.9709 million tons, followed by natural development, economic development, and cropland protection scenarios. The research results are conducive to the formulation of carbon peak and neutrality goals as well as low-carbon development plans for the Wuhan Urban Agglomeration.

Spatiotemporally refined solar-induced chlorophyll fluorescence (SIF) information over large regions has become a crucial data source for terrestrial carbon cycle studies and agricultural drought stress monitoring. Various machine learning (ML) methods have been employed to reconstruct satellite-based SIF retrievals, addressing issues such as spatial gaps and coarse resolution. However, the spatiotemporal heterogeneity in the statistical relationships between explanatory variables and SIF introduces potential errors in these empirical models, leading to spatiotemporal error aggregation. This study proposes a novel spatiotemporal zoning strategy to reconstruct GOME-2 SIF. The Spatiotemporal Ensemble (STE) model integrates multi-source geospatial data and diverse ML models, demonstrating superior SIF estimation performance. The STE model achieved an R² of 0.925 with 5-fold cross-validation and an RMSE of 0.058 mW m⁻² sr⁻¹ nm⁻¹, outperforming six classical ML models by 14%. The STE model significantly mitigates spatiotemporal error aggregation. Temporally, the seasonal variability in model performance was reduced, with the RMSE gap between months with peak and trough errors decreasing to merely 0.04 mW m⁻² sr⁻¹ nm⁻¹. Spatially, error clustering trends were weakened. We successfully generated a reconstructed SIF product for China at 0.01°/8-day spatiotemporal resolution spanning 2008 to 2018. Reducing spatiotemporal error aggregation in SIF estimation will enhance its application potential.