Background Soil organic carbon(SOC)is a critical component of the global carbon cycle,and an accurate estimate of regional SOC stock(SOCS)would significantly improve our understanding of SOC sequestration and cycles.Z...Background Soil organic carbon(SOC)is a critical component of the global carbon cycle,and an accurate estimate of regional SOC stock(SOCS)would significantly improve our understanding of SOC sequestration and cycles.Zoige Plateau,locating in the northeastern Qinghai-Tibet Plateau,has the largest alpine marsh wetland worldwide and exhibits a high sensitivity to climate fluctuations.Despite an increasing use of optical remote sensing in predicting regional SOCS,optical remote sensing has obvious limitations in the Zoige Plateau due to highly cloudy weather,and knowledge of on the spatial patterns of SOCS is limited.Therefore,in the current study,the spatial distributions of SOCS within 100 cm were predicted using an XGBoost model—a machine learning approach,by integrating Sentinel-1,Sentinel-2 and field observations in the Zoige Plateau.Results The results showed that SOC content exhibited vertical distribution patterns within 100 cm,with the highest SOC content in topsoil.The tenfold cross-validation approach showed that XGBoost model satisfactorily predicted the spatial patterns of SOCS with a model efficiency of 0.59 and a root mean standard error of 95.2 Mg ha^(-1).Predicted SOCS showed a distinct spatial heterogeneity in the Zoige Plateau,with an average of 355.7±123.1 Mg ha^(-1)within 100 cm and totaled 0.27×10^(9)Mg carbon.Conclusions High SOC content in topsoil highlights the high risks of significant carbon loss from topsoil due to human activities in the Zoige Plateau.Combining Sentinel-1 and Sentinel-2 satisfactorily predicted SOCS using the XGBoost model,which demonstrates the importance of selecting modeling approaches and satellite images to improve efficiency in predicting SOCS distribution at a fine spatial resolution of 10 m.Furthermore,the study emphasizes the potential of radar(Sentinel-1)in developing SOCS mapping,with the newly developed fine-resolution mapping having important applications in land management,ecological restoration,and protection efforts in the Zoige Plateau.展开更多
基金supported by the Demonstration Project of Carbon Sink Measurement and Value Realization Path after Ecological Restoration of Mountain and Water in Zoige(51000024Y000010980721)the Everest Scientific Research Program of Chengdu University of Technology(80000-2023ZF11410)
文摘Background Soil organic carbon(SOC)is a critical component of the global carbon cycle,and an accurate estimate of regional SOC stock(SOCS)would significantly improve our understanding of SOC sequestration and cycles.Zoige Plateau,locating in the northeastern Qinghai-Tibet Plateau,has the largest alpine marsh wetland worldwide and exhibits a high sensitivity to climate fluctuations.Despite an increasing use of optical remote sensing in predicting regional SOCS,optical remote sensing has obvious limitations in the Zoige Plateau due to highly cloudy weather,and knowledge of on the spatial patterns of SOCS is limited.Therefore,in the current study,the spatial distributions of SOCS within 100 cm were predicted using an XGBoost model—a machine learning approach,by integrating Sentinel-1,Sentinel-2 and field observations in the Zoige Plateau.Results The results showed that SOC content exhibited vertical distribution patterns within 100 cm,with the highest SOC content in topsoil.The tenfold cross-validation approach showed that XGBoost model satisfactorily predicted the spatial patterns of SOCS with a model efficiency of 0.59 and a root mean standard error of 95.2 Mg ha^(-1).Predicted SOCS showed a distinct spatial heterogeneity in the Zoige Plateau,with an average of 355.7±123.1 Mg ha^(-1)within 100 cm and totaled 0.27×10^(9)Mg carbon.Conclusions High SOC content in topsoil highlights the high risks of significant carbon loss from topsoil due to human activities in the Zoige Plateau.Combining Sentinel-1 and Sentinel-2 satisfactorily predicted SOCS using the XGBoost model,which demonstrates the importance of selecting modeling approaches and satellite images to improve efficiency in predicting SOCS distribution at a fine spatial resolution of 10 m.Furthermore,the study emphasizes the potential of radar(Sentinel-1)in developing SOCS mapping,with the newly developed fine-resolution mapping having important applications in land management,ecological restoration,and protection efforts in the Zoige Plateau.
