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.

了解土壤的空间变化在现代农业管理中非常重要。为了获得此知识，需要将地面样本与来自物联网的许多地面，空中和空间传感器结合使用。与为固定传感器设计的传统网格和简单随机采样相比，自适应采样没有得到很好的研究。在这项研究中，我们提出了一种基于先验的自适应采样方案，以收集土壤样本，以估计澳大利亚新南威尔士州半干旱景观中80公顷田地的地面伽玛射线钾。我们通过各种自适应采样方案之间的线性混合模型，通过具有不同质量的先验信息（例如，地面视在电导率，空气传播的伽玛射线钾，以及粘土含量的旧版地图）。我们还将比较自适应采样方案与更常规的网格和简单随机采样方案的模型性能。结果表明，当由于使用先验信息而导致样本量较小（<15个额外样本）时，就线性混合模型的准确性而言，自适应抽样方案优于网格和简单随机抽样方案。当先验信息的质量（与目标土壤变量的相关性）降低时，与自适应采样方案相关的线性混合模型的准确性就会降低。我们得出结论，当采样成本大且传感器的行进时间相对较小时，该算法具有普遍应用于自动自适应采样设计的潜力（例如，在自动驾驶汽车上）。