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Regional-scale soil carbon predictions can be enhanced by transferring global-scale soil-environment relationships

文献类型：期刊论文

作者Zhang, Lei3,4; Yang, Lin3; Ma, Yuxin5; Zhu, A. -Xing1; Wei, Ren3; Liu, Jie3; Greve, Mogens H.6; Zhou, Chenghu2,3
刊名GEODERMA
出版日期2025-09-01
卷号461页码:117466
关键词Soil organic carbon Soil-environment relationships Soil mapping Pre-trained model Model transferability Deep learning Global and regional scales
ISSN号0016-7061
DOI10.1016/j.geoderma.2025.117466
产权排序6
文献子类Article
英文摘要Accurate modelling and mapping soil organic carbon are crucial for supporting soil health restoration and climate change mitigation at both regional and global scales. However, regional soil predictions often suffer from data scarcity and high prediction uncertainty. Utilizing a pre-trained global-to-regional soil carbon predictive model can be a potential solution to address this challenge. Despite its promise, how to construct and apply the global-scale model to enhance regional-scale soil carbon mapping remains largely unexplored. Here, we propose the Global Soil Carbon Pre-trained Model (GSoilCPM), a deep-learning-based domain adaptative model, to enhance regional-scale soil carbon predictions. Based on large amount of environmental covariate data and 106,167 soil samples across the globe, we verify our hypothesis of the effectiveness of this 'global-to-regional' modelling strategy. The pre-trained model can be then transferred and fine-tuned to bridge the regional-and global-scale soil-environment relationships. We applied and validated this modelling strategy in four regional-scale study areas, three in the Northern Hemisphere and one in the Southern Hemisphere, each with distinct environmental background. Compared to traditional modelling approaches as a baseline, four case studies all demonstrated significant improvement in prediction accuracy across diverse environments and varying data availabilities. The average percentage improvement across all regions is 10.93% (absolute values decreased by 1.20 g kg(-1) averagely) in MAE and 29.04% (absolute values increased by 0.10 averagely) in CCC. The applicability and future horizons of using GSoilCPM were further discussed. We further reveal that regions with fewer soil samples or lower baseline accuracy benefit more from the pre-trained global model. Our findings highlight the advantages of leveraging the generalized knowledge from global models to enhance specifically localized soil modelling, positioning a potential paradigm shift in digital soil mapping, and far-reaching implications for soil monitoring and land management.
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WOS关键词SPATIAL PREDICTION ; DEPTH FUNCTIONS ; ORGANIC-MATTER ; MODELS ; MAP
WOS研究方向Agriculture
语种英语
WOS记录号WOS:001608111000001
出版者ELSEVIER
源URL[http://ir.igsnrr.ac.cn/handle/311030/217709]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Yang, Lin
作者单位1.Univ Wisconsin Madison, Dept Geog, Madison, WI USA;
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
3.Nanjing Univ, Sch Geog & Ocean Sci, Nanjing, Peoples R China;
4.Lawrence Berkeley Natl Lab, Climate & Ecosyst Sci Div, Berkeley, CA USA;
5.New South Wales Dept Climate Change, Energy Environm & Water, Parramatta, NSW, Australia;
6.Aarhus Univ, Dept Agroecol, Tjele, Denmark;
推荐引用方式
GB/T 7714		 Zhang, Lei,Yang, Lin,Ma, Yuxin,et al. Regional-scale soil carbon predictions can be enhanced by transferring global-scale soil-environment relationships[J]. GEODERMA,2025,461:117466.
APA Zhang, Lei.,Yang, Lin.,Ma, Yuxin.,Zhu, A. -Xing.,Wei, Ren.,...&Zhou, Chenghu.(2025).Regional-scale soil carbon predictions can be enhanced by transferring global-scale soil-environment relationships.GEODERMA,461,117466.
MLA Zhang, Lei,et al."Regional-scale soil carbon predictions can be enhanced by transferring global-scale soil-environment relationships".GEODERMA 461(2025):117466.

入库方式： OAI收割

来源：地理科学与资源研究所

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