The roles of sampling depth, lateral profile density and vertical sampling density in 3D digital soil mapping of soil organic carbon

Abstract

Sampling density and depth play crucial roles in three-dimensional (3D) soil modeling and prediction, particularly in digital soil mapping (DSM). However, previous studies have yielded inconsistent and even contradictory results regarding impacts of sampling density and depth on the accuracy of 3D DSM. Hence, this study aimed to evaluate the impacts of sampling depth, vertical sampling density and lateral profile density on 3D soil mapping accuracy based on a case study of soil organic carbon (SOC). To achieve this, a comprehensive analysis was conducted based on 511 soil samples collected from 111 profiles in a local hilly area spanning 5.52 km2 in China. A 3D regression geostatistical approach was employed for the analysis. The results revealed that samples taken from different depth intervals exhibited varying degrees of importance in relation to prediction accuracy. Notably, a reduced number of surface soil samples (0–0.3 m) led to significant fluctuations in the accuracy of predictions for the entire soil profile. Furthermore, a lower number of subsurface soil samples (0.3–0.6 m) also diminished the overall prediction accuracy, while the influence of deeper soil samples (0.6–1.2 m) on the overall accuracy was relatively less pronounced. Reducing the number of profiles for calibration led to significantly worse and more variable prediction accuracy, compared to reducing vertical samples. For 3D mapping, this study recommends prioritizing the collection of additional lateral profiles, specifically focusing on surface and subsurface soils, especially for properties that display vertical variation similar to SOC.