Large-area soil mapping based on environmental similarity with adaptive consideration of spatial distance to samples

Abstract

Digital soil mapping (DSM) based on environmental similarity may be used to predict the soil properties at unvisited locations based on the soil–environment relationship at each sample locations, which is more suitable for large-area (or regional scale) predictive mapping with comparatively limited soil samples than (geo-)statistical DSM methods with high requirements for the quantity and spatial distribution of samples. Recently, the DSM method based on environmental similarity but with inadequate consideration of the First Law of Geography was improved by considering the spatial distance to samples by means of the inverse distance weight (IDW), following the assumption that the more similar the environmental conditions and the shorter the spatial distance between the locations of interest and a sample are, the more similar the soil properties of the two locations. However, the current consideration of spatial distance by a constant distance-decay parameter should be improper across a whole large area under mapping due to the spatial variation of both sample distribution and the environmental conditions over a large area. In this paper, we propose a new large-area DSM method based on environmental similarity with adaptive consideration of spatial distance to samples by using adaptive distance-decay parameter values across a large area. An evaluation experiment to predict the soil organic matter at a depth of 0–20 cm at a 90-m resolution with a total of 659 soil samples in Anhui Province (approximately 134000 km2), China, was carried out. Evaluation results show that the proposed method (named iPSM-AdaIDW) achieved higher accuracy (with a root mean square error, or RMSE, of 8.273 g/kg) than two representative environmental-similarity-based DSM methods, i.e., individual Predictive Soil Mapping (iPSM) (without considering the spatial distance to samples) (RMSE = 8.878 g/kg) and iPSM considering spatial distance with a constant distance-decay parameter across the study area (RMSE = 8.310 g/kg), and a representative of large-area DSM not based on environmental similarity, i.e., the random forest kriging method (RMSE = 9.647 g/kg). For large areas with quantitively limited and unevenly distributed samples, the proposed method can achieve more applicable and accurate predictions.