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Using satellite-derived attributes as proxies for soil carbon cycling to map carbon stocks in alpine grassland soils

文献类型：期刊论文

作者Yang, Ren-Min4; Huang, Lai-Ming2,3; Yan, Zhifeng4; Zhang, Xin1; Yan, Shao-Jun4
刊名GEODERMA
出版日期2025
卷号453页码:117143
关键词Soil organic carbon Soil carbon balance Remotely sensed proxies PLS-SEM QRF
ISSN号0016-7061
DOI10.1016/j.geoderma.2024.117143
产权排序2
文献子类Article
英文摘要Alpine grassland ecosystems play a crucial role in the global carbon (C) balance by contributing to the soil organic carbon (SOC) pool; thus, quantifying SOC stocks in these ecosystems is essential for understanding potential gains or losses in soil C under the threat of climate change and anthropogenic activities. Remote sensing plays a vital role in estimating SOC stocks; however, identifying reliable remote sensing proxies to enhance SOC prediction remains a challenge. Information on soil C cycling proxies can reveal how the balance between C inputs and outputs affects SOC. Therefore, these proxies could be effective indicators of SOC variations. In this study, we explored the potential of satellite-derived attributes related to soil C cycling proxies for predicting SOC stocks. We derived remote sensing indices such as gross primary production, soil respiration, soil moisture, land surface temperature, radiation, and soil disturbance and assessed the relationships between these indices and SOC stocks via partial least squares structural equation modeling (PLS-SEM). We evaluated the effectiveness of these indices in predicting SOC stocks, we compared PLS-SEM and quantile regression forest (QRF) models across different variable combinations, including static, intra-annual, and inter-annual information. The PLS-SEM results demonstrated the suitability of the derived remote sensing indices and their interactions in reflecting processes related to soil C balance. The QRF models, using these indices, achieved promising prediction accuracies, with a coefficient of determination (R2) of 0.54 and a root mean square error (RMSE) of 0.79 kg m-2 at the topmost 10 cm of soil. However, the prediction performance generally decreased with increasing soil depth, up to 30 cm. The results also revealed that adding intra- and inter-annual information to remotely sensed proxies did not increase the prediction accuracy. Our study revealed that gross primary production, soil respiration, soil moisture, land surface temperature, radiation, and soil disturbance are effective proxies for representing factors influencing soil C balance and mapping SOC stocks in alpine grasslands.
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WOS关键词ORGANIC-CARBON ; TEMPERATURE SENSITIVITY ; TERRESTRIAL ECOSYSTEMS ; CLIMATE-CHANGE ; RESPIRATION ; FOREST ; TOPSOIL ; MATTER ; PERMAFROST ; PREDICTION
WOS研究方向Agriculture
语种英语
WOS记录号WOS:001390953900001
出版者ELSEVIER
源URL[http://ir.igsnrr.ac.cn/handle/311030/211263]  
专题黄河三角洲现代农业工程实验室_外文论文
作者单位1.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Yellow River Delta Modern Agr Engn Lab, Beijing 100101, Peoples R China;
3.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China;
4.Tianjin Univ, Sch Earth Syst Sci, Tianjin 300072, Peoples R China;
推荐引用方式
GB/T 7714		 Yang, Ren-Min,Huang, Lai-Ming,Yan, Zhifeng,et al. Using satellite-derived attributes as proxies for soil carbon cycling to map carbon stocks in alpine grassland soils[J]. GEODERMA,2025,453:117143.
APA Yang, Ren-Min,Huang, Lai-Ming,Yan, Zhifeng,Zhang, Xin,&Yan, Shao-Jun.(2025).Using satellite-derived attributes as proxies for soil carbon cycling to map carbon stocks in alpine grassland soils.GEODERMA,453,117143.
MLA Yang, Ren-Min,et al."Using satellite-derived attributes as proxies for soil carbon cycling to map carbon stocks in alpine grassland soils".GEODERMA 453(2025):117143.

入库方式： OAI收割

来源：地理科学与资源研究所

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