On the interpolation of volumetric water content in research catchments

Abstract

Digital Soil Mapping (DSM) is widely used in the environmental sciences because of its accuracy and efficiency in producing soil maps compared to the traditional soil mapping. Numerous studies have investigated how the sampling density and the interpolation process of data points affect the prediction quality. While, the interpolation process is straight forward for primary attributes such as soil gravimetric water content (θg) and soil bulk density (ρb), the DSM of volumetric water content (θv), the product of θg by ρb, may either involve direct interpolations of θv (approach 1) or independent interpolation of ρb and θg data points and subsequent multiplication of ρb and θg maps (approach 2). The main objective of this study was to compare the accuracy of these two mapping approaches for θv. A 23ha grassland catchment in KwaZulu-Natal, South Africa was selected for this study. A total of 317 data points were randomly selected and sampled during the dry season in the topsoil (0–0.05m) for θg by ρb estimation. Data points were interpolated following approaches 1 and 2, and using inverse distance weighting with 3 or 12 neighboring points (IDW3; IDW12), regular spline with tension (RST) and ordinary kriging (OK). Based on an independent validation set of 70 data points, OK was the best interpolator for ρb (mean absolute error, MAE of 0.081gcm−3), while θg was best estimated using IDW12 (MAE=1.697%) and θv by IDW3 (MAE=1.814%). It was found that approach 1 underestimated θv. Approach 2 tended to overestimate θv, but reduced the prediction bias by an average of 37% and only improved the prediction accuracy by 1.3% compared to approach 1. Such a great benefit of approach 2 (i.e., the subsequent multiplication of interpolated maps of primary variables) was unexpected considering that a higher sampling density (∼14 data point ha−1 in the present study) tends to minimize the differences between interpolations techniques and approaches. In the context of much lower sampling densities, as generally encountered in environmental studies, one can thus expect approach 2 to yield significantly greater accuracy than approach 1. This approach 2 seems promising and can be further tested for DSM of other secondary variables.