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

Wenqing Yu, Shuo Chen, Weihao Yang, Yingqiang Song () and Miao Lu ()
Additional contact information
Wenqing Yu: School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China
Shuo Chen: School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China
Weihao Yang: School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China
Yingqiang Song: School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China
Miao Lu: National Center of Technology Innovation for Comprehensive Utilization of Saline-Alkali Land, Dongying 257300, China

Agriculture, 2024, vol. 14, issue 9, 1-21

Abstract: 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.

Keywords: remote sensing; soil CO 2 flux; machine learning; farmland (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/9/1453/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/9/1453/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:9:p:1453-:d:1463836

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().