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

作者：Lei Zhang, Lin Yang, Yuxin Ma, A‐Xing Zhu, Ren Wei, Jie Liu, Mogens Humlekrog Greve, Chenghu Zhou · 发表于：Geoderma · 年份：2025 · DOI：10.1016/j.geoderma.2025.117466 · 被引用次数：6 · 研究领域：Soil Carbon and Nitrogen Dynamics、Soil Geostatistics and Mapping、Peatlands and Wetlands Ecology

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% ...