XCO2 Fusion Algorithm Based on Multi-Source Greenhouse Gas Satellites and CarbonTracker

Abstract

In view of the urgent need for high coverage and high-resolution atmospheric CO2 data in the study of carbon neutralization and global CO2 change research, this study combines the Kriging interpolation and the Triple Collision (TC) algorithm to fuse three XCO2 datasets, OCO-2, GOSAT, and CarbonTracker, to obtain a 1° × 1° half-monthly average XCO2 dataset. Through a sub division of the Kriging interpolation, the average coverages of the OCO-2 and GOSAT XCO2 interpolating datasets are increased by 53.65% and 48.5%, respectively. In order to evaluate the accuracy of the TC fusion dataset, this study used a reliable reference dataset, TCCON data, as the verification data. Through comparative analysis, the MAE of the fusion dataset is 0.6273 ppm, RMSE is 0.7683 ppm, and R2 is 0.8279. It can be seen that the combination of Kriging interpolation and TC algorithm can effectively improve the coverage and accuracy of the XCO2 dataset.

XCO2 Super-Resolution Reconstruction Based on Spatial Extreme Random Trees

“…Additionally, geostatistical cluster analysis divides the study area into spatial units with similar CO 2 concentration characteristics, revealing regional differences in the concentration distribution and providing a reference for regional CO 2 management. Despite the significant progress made as a result of these methods in CO 2 concentration research, they still present some challenges [42]. Geostatistical methods, represented by Kriging interpolation, are sensitive to small-scale spatial changes and cannot effectively handle unstable datasets.…”