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Cadmium (Cd) is a toxic metal and found in various soils, including forest soils. The great spatial heterogeneity in soil Cd makes it difficult to determine its distribution. Both traditional soil surveys and spatial modeling have been used to study the natural distribution of Cd. However, traditional methods are highly labor-intensive and expensive, while modeling is often encumbered by the need to select the proper predictors. In this study, based on intensive soil sampling (385 soil pits plus 64 verification soil pits) in subtropical forests in Yunfu, Guangdong, China, we examined the impacting factors and the possibility of combining existing soil information with digital elevation model (DEM)-derived variables to predict the Cd concentration at different soil depths along the landscape. A well-developed artificial neural network model (ANN), multi-variate analysis, and principal component analysis were used and compared using the same dataset. The results show that soil Cd concentration varied with soil depth and was affected by the top 0–20 cm soil properties, such as soil sand or clay content, and some DEM-related variables (e.g., slope and vertical slope position, varying with depth). The vertical variability in Cd content was found to be correlated with metal contents (e.g., Cu, Zn, Pb, Ni) and Cd contents in the layer immediately above. The selection of candidate predictors differed among different prediction models. The ANN models showed acceptable accuracy (around 30% of predictions have a relative error of less than 10%) and could be used to assess the large-scale Cd impact on environmental quality in the context of intensifying industrialization and climate change, particularly for ecosystem management in this region or other regions with similar conditions.

Comparison of Models for Spatial Distribution and Prediction of Cadmium in Subtropical Forest Soils, Guangdong, China