Mapping plant-available phosphorus at the field scale using targeted sampling, sensors, and geostatistics

Accurate spatial soil information at the field scale is critical for sustainable land management and environmental modeling. This research investigates methods for mapping plant-available phosphorus by integrating sensor technologies, targeted sampling strategies, and geostatistical approaches.

Field-scale soil mapping in Denmark typically employs a uniform 100-meter sampling grid with interpolation to estimate soil properties. This study explores the potential of targeted sampling informed by proximal and remote sensing technologies, terrain variables, and existing national-scale soil maps. The sensor technologies evaluated include electromagnetic induction (EMI), gamma-ray sensors, and aerial imagery. Although these sensors are widely applied to assess soil texture and organic carbon content, their application in phosphorus mapping is relatively novel. The study relies on data from seven fields located in Weichselian morainic landscapes in Denmark. The fields covered 4 – 37 ha each and mainly comprised loam and sandy loam soils.

Targeted sampling strategies were designed using k-means clustering. We used measurements of Olsen P as a proxy for plant-available phosphorus, which was then mapped using Gaussian Process Regression. The performance of sensor-informed approaches was compared to methods based on spatial coverage sampling and interpolation. Each method was tested with different numbers of soil samples used for calibration.

At the field level, Olsen P was found to have a moderate to strong positive correlation with organic matter. The values were generally higher in topographic depressions and areas with darker soils in the aerial images. Variogram analyses indicated that phosphorus measurements exhibit spatial autocorrelation with effective ranges of 23 to 167 meters in different fields, highlighting opportunities to optimize sampling strategies based on site-specific spatial variability.

Mapping accuracy improved with increased sampling density in both sensor-based and conventional approaches; however, sensor-derived covariates provided significant accuracy gains. The sensor-based methods generally achieved accuracies that were unattainable by conventional approaches, irrespective of the sampling density. The sensor-based methods also stayed effective with low sampling densities (less than 0.5 samples ha^-1), which was not the case without sensors.

This study highlights the potential of combining spatial geostatistics with sensor-based approaches to improve phosphorus mapping. The results demonstrate that such integration can reduce sampling density requirements while enhancing phosphorus mapping precision, offering a cost-effective and scalable solution for field-scale nutrient management.