Ensemble modelling-based pedotransfer functions for predicting soil bulk density in China
Zhongxing Chen,
Jie Xue,
Zheng Wang,
Yin Zhou,
Xunfei Deng,
Feng Liu,
Xiaodong Song,
Ganlin Zhang,
Yang Su,
Peng Zhu,
Zhou Shi,
Songchao Chen
Affiliations
Zhongxing Chen
ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, China; College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Jie Xue
Department of Land Management, Zhejiang University, Hangzhou 310058, China
Zheng Wang
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Yin Zhou
School of Public Administration, Zhejiang University of Finance and Economics, Hangzhou 310018, China
Xunfei Deng
Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
Feng Liu
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
Xiaodong Song
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
Ganlin Zhang
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China
Yang Su
Département d’informatique, École Normale Supérieure-PSL, Paris 75005, France
Peng Zhu
Department of Geography and Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong 999077, China
Zhou Shi
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Songchao Chen
ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, China; College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; Corresponding author at: ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, China.
Understanding and managing soil organic carbon stocks (SOCS) are integral to ensuring environmental sustainability and the health of terrestrial ecosystems. The information of soil bulk density (BD) is important in accurately determining SOCS while it is often missing in the soil database. Using 3,504 soil profiles (14,170 soil samples) that represented diverse regions across China, we investigated the effectiveness of various pedotransfer functions (PTFs), including traditional PTFs, machine learning (ML), and ensemble model (EM), in predicting BD. The results showed that refitting the parameter(s) in traditional PTFs was essential for BD prediction (coefficient of determination (R2) of 0.299–0.432, root mean squared error (RMSE) of 0.156–0.162 g cm−3, Lin’s concordance coefficient (LCCC) of 0.428–0.605). Compared to traditional PTFs, ML can greatly improve the model performance for BD prediction with R2 of 0.425–0.616, RMSE of 0.129–0.158 g cm−3 and LCCC of 0.622–0.765. Our results also showed that EM can further improve BD prediction by ensembling four ML models (R2 = 0.630, RMSE = 0.126 g cm−3, LCCC = 0.775). Using the EM model, we filled the missing BD (1207 soil profiles with 3,112 soil samples) in our database and built the SOC stock database (4,275 soil profiles with 17,282 soil samples). This study can be a good reference for gap-filling the missing BD depending on the data availability, thus contribute to a deeper understanding in soil C related climate change mitigation, ecological balance preservation and environmental sustainability promotion.
