Spatial Mapping of Soil CO 2 Flux in the Yellow River Delta Farmland of China Using Multi-Source Optical Remote Sensing Data

The spatial prediction of soil CO 2 flux is of great significance for assessing regional climate change and high-quality agricultural development. Using a single satellite to predict soil CO 2 flux is limited by climatic conditions and land cover, resulting in low prediction accuracy. To this end, this study proposed a strategy of multi-source spectral satellite coordination and selected seven optical satellite remote sensing data sources (i.e., GF1-WFV, GF6-WFV, GF4-PMI, CB04-MUX, HJ2A-CCD, Sentinel 2-L2A, and Landsat 8-OLI) to extract auxiliary variables (i.e., vegetation indices and soil texture features). We developed a tree-structured Parzen estimator (TPE)-optimized extreme gradient boosting (XGBoost) model for the prediction and spatial mapping of soil CO 2 flux. SHapley additive explanation (SHAP) was used to analyze the driving effects of auxiliary variables on soil CO 2 flux. A scatter matrix correlation analysis showed that the distributions of auxiliary variables and soil CO 2 flux were skewed, and the linear correlations between them (r < 0.2) were generally weak. Compared with single-satellite variables, the TPE-XGBoost model based on multiple-satellite variables significantly improved the prediction accuracy (RMSE = 3.23 kg C ha −1 d −1 , R 2 = 0.73), showing a stronger fitting ability for the spatial variability of soil CO 2 flux. The spatial mapping results of soil CO 2 flux based on the TPE-XGBoost model revealed that the high-flux areas were mainly concentrated in eastern and northern farmlands. The SHAP analysis revealed that PC2 and the TCARI of Sentinel 2-L2A and the TVI of HJ2A-CCD had significant positive driving effects on the prediction accuracy of soil CO 2 flux. The above results indicate that the integration of multiple-satellite data can enhance the reliability and accuracy of spatial predictions of soil CO 2 flux, thereby supporting regional agricultural sustainable development and climate change response strategies.