Although atmospheric CO₂ concentrations collected by satellites play a crucial role in understanding global greenhouse gases, the sparse geographic distribution greatly affects their widespread application. In this paper, a hybrid CNN and spatiotemporal Kriging (CNN-STK) model is proposed to generate a monthly spatiotemporal continuous XCO2 dataset over China at 0.25° grid-scale from 2015 to 2020, utilizing OCO-2 XCO2 and geographic covariates. The validations against observation samples, CAMS XCO2 and TCCON measurements indicate the CNN-STK model is effective, robust, and reliable with high accuracy (validation set metrics: R² = 0.936, RMSE = 1.3 ppm, MAE = 0.946 ppm; compared with TCCON: R² = 0.954, RMSE = 0.898 ppm and MAE = 0.741 ppm). The accuracy of CNN-STK XCO2 exhibits spatial inhomogeneity, with higher accuracy in northern China during spring, autumn, and winter and lower accuracy in northeast China during summer. XCO2 in low-value-clustering areas is notably influenced by biological activities. Moreover, relatively high uncertainties are observed in the Qinghai-Tibet Plateau and Sichuan Basin. This study innovatively integrates deep learning with the geostatistical method, providing a stable and cost-effective approach for other countries and regions to obtain regional scales of atmospheric CO₂ concentrations, thereby supporting policy formulation and actions to address climate change.

In remote sensing, red edge bands are important indicators for monitoring vegetation growth. To examine the application potential of red edge bands in forest canopy closure estimation, three types of commonly used models—empirical statistical models (multiple stepwise regression (MSR)), machine learning models (back propagation neural network (BPNN)) and physical models (Li–Strahler geometric-optical (Li–Strahler GO) models)—were constructed and verified based on Sentinel-2 data, DEM data and measured data. In addition, we set up a comparative experiment without red edge bands. The relative error (E_R) values of the BPNN model, MSR model, and Li–Strahler GO model with red edge bands were 16.97%, 20.76% and 24.83%, respectively. The validation accuracy measures of these models were higher than those of comparison models. For comparative experiments, the E_R values of the MSR, Li–Strahler GO and BPNN models were increased by 13.07%, 4% and 1.22%, respectively. The experimental results demonstrate that red edge bands can effectively improve the accuracy of forest canopy closure estimation models to varying degrees. These findings provide a reference for modeling and estimating forest canopy closure using red edge bands based on Sentinel-2 images.

To provide new insights into the integrated management of carbon and heat for sustainable urban development, this study systematically investigates the complex relationship between atmospheric CO₂ concentrations and land surface temperature (LST). Utilizing OCO-2 and OCO-3 satellite observations, combined with meteorological conditions, air pollutants, and spatial characteristics, a high-resolution (0.1° × 0.1°) monthly CO₂ column concentration (XCO₂) dataset for China spanning 2015 to 2022 was generated using the Random Forest algorithm. The study focuses on urban agglomerations, conducting centroid migration and coupling analyses of XCO₂ and LST to elucidate their spatiotemporal distribution patterns and evolution. Results reveal significant seasonal variations in XCO₂, which has exhibited a gradual increase over the years. The spatiotemporal distributions of XCO₂ and LST in urban agglomerations show a high degree of consistency, with centroids either converging or following similar movement trajectories. Additionally, the degree of coupling and coordination between XCO₂ and LST has improved annually, indicating a closer interrelationship. These findings enhance our understanding of climate system dynamics and provide essential scientific evidence and decision-making support for addressing climate change. By clarifying the connection between atmospheric CO₂ and LST, this study contributes to the development of more effective strategies for carbon reduction and urban heat island mitigation, thereby advancing cities towards greener, lower-carbon, and more sustainable development pathways.