Using remote sensors to predict soil properties: Radiometry and peat depth in Dartmoor, UK

Abstract

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.