Multi-sensor integrated mapping of global XCO2 from 2015 to 2021 with a local random forest model

Carbon dioxide (CO2) is one of the most important greenhouse gases in the atmosphere, and carbon satellites play a vital role in monitoring its concentration. However, a single carbon satellite often has inadequate spatial coverage, resulting in numerous gaps. Utilizing the complementary advantages of multiple satellites in spatial coverage for high-coverage mapping of CO2 may be an effective means. To this end, this study proposes a local random forest (LRF) model to generate a global high-coverage and high-precision column-averaged dry-air mole fraction of CO2 (XCO2) product from 2015 to 2021, which integrates multi-source data from OCO-2, OCO-3, GOSAT, and GOSAT-2. The results indicate that the LRF reconstructions agree well with the ground-based site observations, with RMSE, MAE, and R2 of 1.08 ppm, 0.82 ppm, and 0.96, respectively, outperforming the global random forest and artificial neural network models. Meanwhile, multi-source satellite fusion effectively improves the global XCO2 coverage, with a maximum improvement rate of 122.2% compared to the single satellite of OCO-2. Based on the reconstructed dataset, regional and seasonal differences in the global XCO2 distribution are observed, and an average growth rate of 2.18 ppm/year of global XCO2 is revealed during 2015–2021. This study combines data from multi-source satellites to generate high-coverage global XCO2 products from 2015 to 2021, which can be freely accessed from https://github.com/ch00en/Multi-source-satellite-XCO2-fusion-products.git and would hold great potential for carbon cycle research.