您好，欢迎访问浙江省农业科学院 机构知识库！
浙江省农业科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Using process-oriented model output to enhance machine learning-based soil organic carbon prediction in space and time

文献类型： 外文期刊

作者： Zhang, Lei 1 ; Heuvelink, Gerard B. M. 2 ; Mulder, Vera L. 2 ; Chen, Songchao 4 ; Deng, Xunfei 5 ; Yang, Lin 1 ;

作者机构： 1.Nanjing Univ, Sch Geog & Ocean Sci, Nanjing, Peoples R China

2.Wageningen Univ, Soil Geog & Landscape Grp, Wageningen, Netherlands

3.ISRIC World Soil Informat, Wageningen, Netherlands

4.Zhejiang Univ, ZJU Hangzhou Global Sci & Technol Innovat Ctr, Hangzhou, Peoples R China

5.Zhejiang Acad Agr Sci, Inst Digital Agr, Hangzhou, Zhejiang, Peoples R China

6.Nanjing Univ, Frontiers Sci Ctr Crit Earth Mat Cycling, Nanjing, Peoples R China

关键词： Hybrid modelling; Mechanistic knowledge-guided machine; learning; RothC; Random forest; Digital soil mapping; Soil carbon dynamics

期刊名称：SCIENCE OF THE TOTAL ENVIRONMENT （ 影响因子：9.8； 五年影响因子：9.6 ）

ISSN： 0048-9697

年卷期： 2024 年 922 卷

页码：

收录情况： SCI

摘要： Monitoring and modelling soil organic carbon (SOC) in space and time can help us to better understand soil carbon dynamics and is of key importance to support climate change research and policy. Although machine learning (ML) has attracted a lot of attention in the digital soil mapping (DSM) community for its powerful ability to learn from data and predict soil properties, such as SOC, it is better at capturing soil spatial variation than soil temporal dynamics. By contrast, process -oriented (PO) models benefit from mechanistic knowledge to express physiochemical and biological processes that govern SOC temporal changes. Therefore, integrating PO and ML models seems a promising means to represent physically plausible SOC dynamics while retaining the spatial prediction accuracy of ML models. In this study, a hybrid modelling framework was developed and tested for predicting topsoil SOC stock in space and time for a regional cropland area located in eastern China. In essence, the hybrid model uses predictions of the PO model in unsampled years as additional training data of the ML model, with a weighting parameter assigned to balance the importance of SOC values from the PO model and real measurements. The results indicated that temporal trends of SOC stock modelled by PO and ML models were largely different, while they were notably similar between the PO and hybrid models. Cross -validation showed that the hybrid model had the best performance (RMSE = 0.29 kg m -2 ), with a 19 % improvement compared with the ML model. We conclude that the proposed hybrid framework not only enhances space-time soil carbon mapping in terms of prediction accuracy and physical plausibility, it also provides insights for soil management and policy decisions in the face of future climate change and intensified human activities.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：浙江省农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台