An error budget for digital soil mapping of cation exchange capacity using proximally sensed electromagnetic induction and remotely sensed γ-ray spectrometer data

The cation exchange capacity (CEC) of soil is widely used for agricultural assessment as a measure of fertility and an indicator of structural stability; however, its measurement is time-consuming. Although geostatistical methods have been used, a large number of samples must be collected. Using pedometric methods and incorporating easy-to-measure ancillary data into models have improved the efficiency of spatial prediction of soil CEC. However, mapping uncertainty has not been evaluated. In this study, we use an error budget procedure to quantify the relative contributions that model, input and covariate error make to prediction error of a digital map of CEC using gamma-ray (-ray) spectrometry and apparent electrical conductivity (ECa) data. The error budget uses empirical best linear unbiased prediction (E-BLUP) and conditional simulation to produce numerous realizations of the data and their underlying errors. Linear mixed models (LMMs) estimated by residual maximum likelihood (REML) are used to create the prediction models. The combined error of model [5.07 cmol(+)/kg] and input error [12.88 cmol(+)/kg] is similar to 12.93 cmol(+)/kg, which is twice as large as the standard deviation of CEC [6.8 cmol(+)/kg]. The individual covariate errors caused by the -ray [9.64 cmol(+)/kg] and EM error [8.55 cmol(+)/kg] were large. Preprocessing techniques to improve the quality of the -ray data could be considered, whereas the EM error could be reduced by the use of a smaller sampling interval in particular near the edges of the study area and at pedoderm boundaries.