Model prediction of depth-specific soil texture distributions with artificial neural network: A case study in Yunfu, a typical area of Udults Zone, South China

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The depth-specific soil texture map with high-resolution (i.e. ≤10 m) is essential for soil management and forest silviculture. The objective of this research was to develop a modelling method to generate high-resolution soil texture maps at five depths (D1: 0–20, D2: 20–40, D3: 40–60, D4: 60–80, and D5: 80–100 cm) in Yunfu, a typical area of Udults Zone, South China. Taking a coarse-resolution soil texture (CST) map with a 1: 2,800,000 scale and nine topo-hydrologic variables derived from a digital elevation model (DEM) with 10 m-resolution as input candidates, a series of artificial neural network (ANN) models for five depths were built and evaluated by a 10-fold cross-validation 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 DEM-generated variables as inputs, respectively, and model accuracies for estimating sand and clay contents varied with root mean squared error (RMSE) of 6.8–9.7%, R2 of 0.56–0.72, and relative overall accuracy (ROA) ± 5% of 54–81%, which were better than most of other researches. An extra independent validation with 64 soil profiles outside of the model-building area also indicated that the optimal models had adequate capabilities for generalization with RMSE of 9.2–12.2%, R2 of 0.33–0.47, and ROA ± 5% of 37–53%. The depth-specific sand and clay content maps with 10 m-resolution generated from the optimal models in Yunfu showed more detailed information than the CST map, and could reflected the influence of the DEM-derived topo-hydrologic 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 (D2-5), 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 k-fold cross-validation, can be used to generate depth-specific soil texture maps in Udults Zone, South China. In addition, the generated high-resolution maps can clearly show the changes of soil texture in three-dimension.