 Research on High Spatiotemporal Resolution of XCO 2 in Sichuan Province Based on Stacking Ensemble LearningZhaofei Li (),
Na Zhao,
Han Zhang,
Yang Wei,
Yumin Chen and
Run Ma
Sustainability, 2025, vol. 17, issue 8, 1-22 Abstract: Global warming caused by the increase in the atmospheric CO 2 content has become a focal environmental issue of common concern to the international community. As a key resource support for achieving the “dual carbon” goals in Western China, Sichuan Province requires a deep analysis of its carbon sources, carbon sinks, and its characteristics in terms of atmospheric environmental capacity, which is of great significance for formulating effective regional sustainable development strategies and responding to global climate change. In view of the unique geographical and climatic conditions in Sichuan Province and the current situation of a low and uneven distribution of atmospheric environmental capacity, this paper uses three forms of multi-source satellite data, OCO-2, OCO-3, and GOSAT, combined with other auxiliary data, to generate a daily XCO 2 concentration dataset with a spatial resolution of a 1km grid in Sichuan Province from 2015 to 2022. Based on the Optuna optimization method with 10-fold cross-validation, the optimal hyperparameter configuration of the four base learners of Stacking, random forest, gradient boosting decision tree, extreme gradient boosting, and the K nearest neighbor algorithm is searched for; finally, the logistic regression algorithm is used as the second-layer meta-learner to effectively improve the prediction accuracy and generalization ability of the Stacking ensemble learning model. According to the comparison of the performance of each model by cross-validation and TCCON site verification, the Stacking model significantly improved in accuracy, with an R 2 , RMSE, and MAE of 0.983, 0.87 ppm and 0.19 ppm, respectively, which is better than those of traditional models such as RF, KNN, XGBoost, and GBRT. The accuracy verification of the atmospheric XCO 2 data estimated by the model based on the observation data of the two TCCON stations in Xianghe and Hefei showed that the correlation coefficients were 0.96 and 0.98, and the MAEs were 0.657 ppm and 0.639 ppm, respectively, further verifying the high accuracy and reliability of the model. At the same time, the fusion of multi-source satellite data significantly improved the spatial coverage of XCO 2 concentration data in Sichuan Province, effectively filling the gap in single satellite observation data. Based on the reconstructed XCO 2 dataset of Sichuan Province, the study revealed that there are significant regional and seasonal differences in the XCO 2 concentrations in the region, showing seasonal variation characteristics of being higher in spring and winter and lower in summer and autumn; in terms of the spatial distribution, the overall spatial distribution characteristics are high in the east and low in the west. This study helps to deepen our understanding of the carbon cycle and climate change, and can provide a scientific basis and risk assessment methods for policy formulation, effect evaluation, and international cooperation. Keywords: stacking ensemble learning; multi-source satellite; XCO 2; high resolution (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:8:p:3433-:d:1633213 Access Statistics for this article Sustainability is currently edited by Ms. Alexandra Wu More articles in Sustainability from MDPI |
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