Spatial Distribution Characteristics And Predictive Mapping Of Key Soil Properties In The Middle Reaches Of The Heihe River Basin

In this study,the middle reaches of the Heihe River Basin was selected as a research area,due to its large spatial scale and complex landscape conditions,a strong spatial heterogeneity of the soil-forming environment,and the formation of a variety of soil processes.Given these complexisities,it is difficult to accurately simulate the two-dimensional?2D?and three-dimensional?3D?soil properties in the area.In addition,the current study of digital soil mapping?DSM?is generally based on sampling in large quantities and often requires significant time and expense.The 2D and 3D DSM has low accuracy in predicting soil properties at large scales.Therefore,using finite sampling points is of great significance in the research with DSM.However,neither the spatial prediction model nor the depth function model has used the knowledge of soil genesis.Using the knowledge of soil genesis can not only understand the formation of soil factors,occurrence process,type and nature,but also improve the accuracy of spatial prediction of soil properties.In this dissertation,we first assume that the knowledge of soil genesis can be used to improve the spatial prediction of soil properties.To test this hypothesis,we collected 80 soil profile datasets and 43 soil surface saturated hydraulic conductivity?SHC?datasets using the purposeful sampling method in the middle reaches of the Heihe River Basin.In combination with 12 environmental variables,soil knowledge,exponential function,boosted regression tree model and similarity-based model were combined to explore the spatial prediction of SOC,SHC and soil field capacity?SFC?.The main results are as follows:?1?Combining the equal-area spline function and classification exponential function and the knowledge of soil genesis,the depth distribution of SOC and SFC was simulated.The equal-area spline function can well simulate the depth distribution of SOC in the middle reaches of the Heihe River basin.The classification exponential function was established on the basis of the depth distribution pattern of SFC capacity in the region.The depth distribution function can reflect the distribution pattern of three kinds of SFC in the depth of 1m in the middle reaches of the Heihe River basin,including a decrease with depth,an increase with depth,and a decrease initially and an increase afterwards.The results show that the depth functions provided high fitting accuracy,with a mean R² of 0.93 and 0.86,respectively.?2?According to the Gauss mixture model,the complicated landscape environment in the study area is divided into six"climate-vegetation"landscape units under relatively uniform environmental conditions.Each landscape unit is expressed as a set of features of the type and distribution of SFC in the region.Compared with other DSM methods,this method can provide the flexibility and authenticity of model predictions.?3?The depth distribution function was used to define the 3D distribution of SOC and SFC.The 3D spatial prediction maps were constructed based on the boosted regression tree model.These two methods can describe the characteristics of 3D spatial distribution of SOC and SFC in the middle reaches of the Heihe River basin.The ranges of MAE?mean absolute error?,RMSE?root mean of squared error?,R²?Coefficient of Determination?,and LCCC?lin's concordance correlation coefficient?of SOC are 0.40-0.85 kg m^-3,0.55-1.21 kgm^-3,0.39-0.65?0.56-0.80,respectively.The MAE,RMSE,R²,LUCC of SFC is 1.57%,7.89%,0.63 and 0.81,respectively.Under the conditions of large-scale and complex landscapes,the DSM method based on the knowledge of soil genesis can accurately describe the spatial distribution of soil properties,which satisfies the relevant administrative and research departmental need,such as estimating SOC pool and farmland irrigation level.