Model prediction of depthspecific soil texture distributions with artificial neural network: A case study in Yunfu, a typical area of Udults Zone, South China Abstract   The depthspecific soil texture map with highresolution (i.e. xe2x89xa410xc2xa0m) is essential for soil management and forest silviculture. The objective of this research was to develop a modelling method to generate highresolution soil texture maps at five depths (D1: 0xe2x80x9320, D2: 20xe2x80x9340, D3: 40xe2x80x9360, D4: 60xe2x80x9380, and D5: 80xe2x80x93100xc2xa0cm) in Yunfu, a typical area of Udults Zone, South China. Taking a coarseresolution soil texture (CST) map with a 1: 2,800,000 scale and nine topohydrologic variables derived from a digital elevation model (DEM) with 10xc2xa0mresolution as input candidates, a series of artificial neural network (ANN) models for five depths were built and evaluated by a 10fold crossvalidation with 385 soil profiles from the Yunfu forest. The results indicated that the optimal model for five depths engaged five, five, five, four, and four DEMgenerated variables as inputs, respectively, and model accuracies for estimating sand and clay contents varied with root mean squared error (RMSE) of 6.8xe2x80x939.7%, R2 of 0.56xe2x80x930.72, and relative overall accuracy (ROA)xc2xa0xc2xb1xc2xa05% of 54xe2x80x9381%, which were better than most of other researches. An extra independent validation with 64 soil profiles outside of the modelbuilding area also indicated that the optimal models had adequate capabilities for generalization with RMSE of 9.2xe2x80x9312.2%, R2 of 0.33xe2x80x930.47, and ROAxc2xa0xc2xb1xc2xa05% of 37xe2x80x9353%. The depthspecific sand and clay content maps with 10xc2xa0mresolution generated from the optimal models in Yunfu showed more detailed information than the CST map, and could reflected the influence of the DEMderived topohydrologic variables. Based on the generated maps, horizontal characteristics of soil texture in the study area exhibited an obvious process of clay translocation from the topsoil (D1) to subsoil (D25), a maximum accumulation of clay in D4, and a dominant sandy soil in the topsoil (D1). Thus, the modelling method, i.e. developing ANNs with kfold crossvalidation, can be used to generate depthspecific soil texture maps in Udults Zone, South China. In addition, the generated highresolution maps can clearly show the changes of soil texture in threedimension.

Digital Soil Mapping over Large Areas with Invalid Environmental Covariate Data Environmental covariates are fundamental inputs of digital soil mapping (DSM) based on the soilxe2x80x93environment relationship. It is normal to have invalid values (or recorded as NoData value) in individual environmental covariates in some regions over an area, especially over a large area. Among the two main existing ways to deal with locations with invalid environmental covariate data in DSM, the locationskipping scheme does not predict these locations and, thus, completely ignores the potentially useful information provided by valid covariate values. The voidfilling scheme may introduce errors when applying an interpolation algorithm to removing NoData environmental covariate values. In this study, we propose a new scheme called FilterNA that conducts DSM for each individual location with NoData value of a covariate by using the valid values of other covariates at the location. We design a new method (SoLIMFilterNA) combining the FilterNA scheme with a DSM method, Soil Land Inference Model (SoLIM). Experiments to predict soil organic matter content in the topsoil layer in Anhui Province, China, under different test scenarios of NoData for environmental covariates were conducted to compare SoLIMFilterNA with the SoLIM combined with the voidfilling scheme, the original SoLIM with the locationskipping scheme, and random forest. The experimental results based on the independent evaluation samples show that, in general, SoLIMFilterNA can produce the lowest errors with a more complete spatial coverage of the DSM result. Meanwhile, SoLIMFilterNA can reasonably predict uncertainty by considering the uncertainty introduced by applying the FilterNA scheme.