Mapping soil pH levels across Europe: An analysis of LUCAS topsoil data using random forest kriging (RFK) |
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| Authors: | Shancai Xiao Minghao Ou Yajun Geng Tao Zhou |
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| Institution: | 1. College of Urban and Environmental Sciences, Peking University, Beijing, China;2. College of Public Administration, Nanjing Agricultural University, Nanjing, China
National & Local Joint Engineering, Research Center for Rural Land Resources Use and Consolidation, Nanjing, China;3. School of Resources and Environmental Engineering, Ludong University, Yantai, China |
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| Abstract: | Soil pH affects food production, pollution control and ecosystem services. Mapping soil pH levels, therefore, provides policymakers with crucial information for developing sustainable soil use and management policies. In this study, we used the LUCAS 2015 TOPSOIL data to map soil pH at a European level. We used random forest kriging (RFK) to build a predictive model of spatial variability of soil pH, as well as random forest (RF) without co-kriging and boosted regression trees (BRT) modelling techniques. Model accuracy was evaluated using a ten-fold cross-validation procedure. While we found that all methods accurately predicted soil pH, the accuracy of the RFK method was best with regression performance metrics of: R2 = 0.81 for pH (H2O) and pH (CaCl2); RMSE = 0.59 for pH (H2O) and RMSE = 0.61 in pH (CaCl2); MAE = 0.41 for pH (H2O) and MAE = 0.43 in pH (CaCl2). Dominant explanatory variables in the RF and BRT modelling were topography and remote sensing variables, respectively. The generated maps broadly depicted similar spatial patterns of soil pH, with an increasing gradient of soil pH from north to south Europe, with the highest values mainly concentrated along the Mediterranean coast. The mapping could provide spatial reference for soil pH assessment and dynamic monitoring. |
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| Keywords: | boosted regression trees digital soil mapping random forest remote sensing soil pH |
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