ALBERT

Description: Finding an ideal sampling design is a crucial stage in detailed soil mapping to assure reasonable accuracy of resulting soil property maps. This study aimed to evaluate the influence of sampling designs and sample sizes on the quality of soil apparent electrical conductivity (ECa) maps from an electromagnetic sensor survey. Twenty-six (26) parallel transects were gathered in a 72-ha plot in Southeastern Brazil. Soil ECa measurements using an on-the-go electromagnetic induction sensor were taken every second using sensor vertical orientation. Two approaches were used to reduce the sample size and simulate kriging interpolations of soil ECa. Firstly, the number of transect lines was reduced by increasing the distance between them; thus, 26 transects with 40 m spacing; 13 with 80 m; 7 with 150 m; and 4 with 300 m. Secondly, random point selection and Douglas-Peucker algorithms were used to derive four reduced datasets by removing 25, 50, 75, and 95% of the points from the ECa survey dataset. Soil ECa was interpolated at 5 m output spatial resolution using ordinary kriging and the four datasets from each simulation (a total of twelve datasets). Map uncertainty was assessed by root mean square error and mean error metrics from 400 random samples previously selected for external map validation. Maps were evaluated on their uncertainty and spatial structure of variation. The transect elimination approach showed that maps produced with transect spacing up to 150 m could preserve the spatial structure of ECa variations. Douglas-Peucker results showed lower nugget values than random point simulations for all selected sample densities, except for a 95% point reduction. The soil ECa maps derived from the 75% reduced dataset (by random sampling or Douglas-Peucker) or from 13 transect lines (80 m spacing) showed reasonable accuracy (RMSE of validation circa 0.7) relative to the map interpolated from all survey points (RMSE of 0.5), suggesting that transect spacing of 80 m and reading intervals greater than one second can be used for improving the efficiency of on-the-go soil ECa surveys.

Description: Mapping soil properties, using geostatistical methods in support of precision agriculture and related activities, requires a large number of samples. To reduce soil sampling and measurement time and cost, a combination of field proximal soil sensors was used to predict and map laboratory-measured soil properties in a 3.4-ha pasture field in southeastern Brazil. Sensor soil properties were measured in situ on a 10 × 10-m dense grid (377 samples) using apparent electrical conductivity meters, apparent magnetic susceptibility meter, gamma-ray spectrometer, water content reflectometer, cone penetrometer, and portable X-ray fluorescence spectrometer (pXRF). Soil samples were collected on a 20 × 20-m thin grid (105 samples) and analyzed in the laboratory for organic C, sum of bases, cation exchange capacity, clay content, soil volumetric moisture, and bulk density. Another 25 samples collected throughout the area were also analyzed for the same soil properties and used for independent validation of models and maps. To test whether the combination of sensors enhances soil property predictions, stepwise multiple linear regression (MLR) models of the laboratory soil properties were derived using individual sensor covariate data versus combined sensor data—except for the pXRF data, which were evaluated separately. Then, to test whether a denser grid sample boosted by sensor-based soil property predictions enhances soil property maps, ordinary kriging of the laboratory-measured soil properties from the thin grid was compared to ordinary kriging of the sensor-based predictions from the dense grid, and ordinary cokriging of the laboratory properties aided by sensor covariate data. The combination of multiple soil sensors improved the MLR predictions for all soil properties relative to single sensors. The pXRF data produced the best MLR predictions for organic C content, clay content, and bulk density, standing out as the best single sensor for soil property prediction, whereas the other sensors combined outperformed the pXRF sensor for the sum of bases, cation exchange capacity, and soil volumetric moisture, based on independent validation. Ordinary kriging of sensor-based predictions outperformed the other interpolation approaches for all soil properties, except organic C content, based on validation results. Thus, combining soil sensors, and using sensor-based soil property predictions to increase the sample size and spatial coverage, leads to more detailed and accurate soil property maps.