Evaluating an adaptive sampling algorithm to assist soil survey in New South Wales, Australia

Abstract

Knowledge of the spatial variation of soil is important in modern agricultural management. To attain this knowledge, ground-based samples are required in combination with many ground-based, air-borne and space-borne sensors from the Internet of Things. Compared to traditional grid and simple random sampling that are designed for fixed sensors, adaptive sampling is not well studied. In this study, we propose a prior-based adaptive sampling scheme to collect soil samples for estimation of ground-based Gamma-ray potassium across an 80-ha field in a semi-arid landscape, in New South Wales, Australia. We compare the performance of the sampling algorithm via a linear mixed model between various adaptive sampling schemes with prior information of varying quality (e.g. ground apparent electrical conductivity, air-borne Gamma-ray potassium, and a legacy map of clay content). We also compare the model performance of the adaptive sampling scheme with more conventional grid and simple random sampling schemes. Results show that the adaptive sampling scheme was superior to the grid and simple random sampling schemes in terms of the accuracy of the linear mixed model when the sampling size was small (<15 additional samples) due to the use of prior information. The accuracy of the linear mixed models associated with the adaptive sampling schemes deteriorated when the quality (correlation with the target soil variable) of the prior information decreases. We conclude that the algorithm has the potential to be applied generally for automated adaptive sampling design (e.g., on an autonomous vehicle) when sampling cost is large and travelling time of the sensor is relatively small.