Use of EMI, gamma-ray emission and GPS height as multi-sensor data for soil characterisation

Geo-electrical sensors are often used as auxiliary variables with sparse direct measurements to estimate soil properties. Using a single sensor is not ideal in some circumstances. For example, sandy, sandy gravelly, sandy salt-affected and clayey soils are poorly identified using an EMI or gamma-ray sensor singularly. The complementary use of these sensors should improve interpretation in landscapes containing these soils. Analysis of multi-sensor data is however problematic. Several methods have been developed to integrate multi-sensor data but there is currently no unequivocally accepted methodology. The objectives of this work were: 1) to define a combined approach of geostatistics and sensor data fusion to integrate field data from electromagnetic induction (EMI) measured with EM38 and EM31, gamma (γ)-ray and RTK GPS sensors for delineating areas of homogeneous soil; 2) to show the potential of gamma radiometric sensor by estimating a relationship for crop available soil potassium (K) from the γ-ray signal. The geophysical survey was carried out on an 80-ha cropping field in Corrigin, Western Australia. Seventy-seven soil samples were collected at the nodes of a 100 × 100m-mesh grid and analysed for different properties. The EM38 and EM31 data were strongly correlated with each other and so were γ-radiometric counts from thorium (Th), uranium (U) and all elements (TC). The multi-sensor data were split into 4 subgroups, based on their similarities: 1) EMI data; 2) γ-radiometric counts from potassium (emitted from all forms of K including readily plant available, non-exchangeable and structural K); 3) γ-radiometric counts from Th, U and TC and 4) RTK GPS height. Each group of data was separately analysed using geostatistical techniques. The soil samples and geophysical data were jointly interpolated using multi collocated cokriging. The EMI data showed anisotropy and an anisotropic Linear Model of Coregionalization was fitted before cokriging. The EM31 and EM38 maps looked quite similar. The maps of γ-U, Th and TC were also similar, suggesting that they reflected the same soil properties, but were somewhat different from the γ-K maps. High values of EMI coincided with both low γ-radiometric values at the valley bottom, due to moist sandy salinity-prone soil of varying depth to texture contrast, and high γ-radiometric values at the elevated areas of the field due to emission from finer textured soil. High γ-radiometric values coincided also with low values of EMI over gravelly sands. Only the use of a multi-sensor platform could discriminate soils that gave similar outputs to one sensor. The first two principal components of the geophysical data were used to partition the field into homogeneous areas.In order to test the utility of geophysical survey for K recommendations, the spatial association between the maps of the estimates of plant available soil K content and γ-K counts was demonstrated by using different agreement coefficients and a regression model with correlated errors was estimated between the two variables.

► EMI, γ-ray, DGPS data integrated for delineating homogeneous soil zones.
► Crop available soil potassium estimated from gamma-ray signal.
► Soil and geophysical data integrated using geostatistics and PCA