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

Author

Listed:
  • Wenqing Yu

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China
    These authors contributed equally to this work.)

  • Shuo Chen

    (School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255000, China
    These authors contributed equally to this work.)

  • 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
    National Center of Technology Innovation for Comprehensive Utilization of Saline-Alkali Land, Dongying 257300, China)

  • Miao Lu

    (National Center of Technology Innovation for Comprehensive Utilization of Saline-Alkali Land, Dongying 257300, China
    State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China/Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China)

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.

Suggested Citation

  • Wenqing Yu & Shuo Chen & Weihao Yang & Yingqiang Song & Miao Lu, 2024. "Spatial Mapping of Soil CO 2 Flux in the Yellow River Delta Farmland of China Using Multi-Source Optical Remote Sensing Data," Agriculture, MDPI, vol. 14(9), pages 1-21, August.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:9:p:1453-:d:1463836
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    References listed on IDEAS

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    1. Gao, Yanni & Yu, Guirui & Li, Shenggong & Yan, Huimin & Zhu, Xianjin & Wang, Qiufeng & Shi, Peili & Zhao, Liang & Li, Yingnian & Zhang, Fawei & Wang, Yanfen & Zhang, Junhui, 2015. "A remote sensing model to estimate ecosystem respiration in Northern China and the Tibetan Plateau," Ecological Modelling, Elsevier, vol. 304(C), pages 34-43.
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