Remote sensors provide high resolution data over large spatial extents that can potentially be used to map soil properties such as the concentration of organic carbon or its moisture content. The sensors rarely measure the property of interest directly but instead measure a related property. There is a need to make ground measurements of the property of interest to calibrate a model or relationship between the soil property and the sensor data.

We develop a framework for optimizing the locations and number of ground measurements of a soil property for surveys incorporating sensor data. The data are used to estimate a linear mixed model of the property where the fixed effects are a flexible spline-based function of the sensor measurements.

The framework is used to map peat depth across a portion of Dartmoor National Park using radiometric potassium data measurements from an airborne survey. The most accurate maps result from using a geostatistical predictor to combine the relationship with the sensor data and the spatial correlation amongst the peat depth measurements. The optimal sampling designs suggest that ground measurements should be focussed where peat depths are largest and most uncertain. When measurements are made at 25 optimally selected sites, predictions that do not utilise the sensor data have 20% larger root mean square errors than those that do. For 200 ground measurements this benefit is 14%. The maps produced using the sensor data and 25 ground measurements have smaller root mean square errors than those based only upon 200 ground measurements.

遥感器提供大空间范围内的高分辨率数据，可用于绘制土壤特性图，例如有机碳浓度或其水分含量。传感器很少直接测量感兴趣的属性，而是测量相关属性。需要对感兴趣的特性进行地面测量，以校准土壤特性和传感器数据之间的模型或关系。

我们开发了一个框架，用于优化结合传感器数据的调查土壤特性的位置和地面测量数量。数据用于估计属性的线性混合模型，其中固定效应是传感器测量的基于样条的灵活函数。

该框架用于使用来自空中调查的放射性钾数据测量来绘制达特穆尔国家公园部分地区的泥炭深度。最准确的地图来自使用地质统计预测器将传感器数据的关系以及泥炭深度测量值之间的空间相关性结合起来。最佳采样设计表明地面测量应集中在泥炭深度最大和最不确定的地方。当在 25 个最佳选择的地点进行测量时，不使用传感器数据的预测比使用传感器数据的预测均方根误差大 20%。对于 200 次地面测量，此优势为 14%。使用传感器数据和 25 次地面测量生成的地图比仅基于 200 次地面测量的地图具有更小的均方根误差。