Comparison of various uncertainty modelling approaches based on geostatistics and machine learning algorithms

We compared the suitability of several commonly applied digital soil mapping (DSM) techniques to quantify uncertainty with regards to a survey of soil organic carbon stock (SOCS) in Hungary. To represent the wide range of DSM techniques fairly, the followings were selected: universal kriging (UK), sequential Gaussian simulation (SGS), random forest combined with kriging (RFK) and quantile regression forest (QRF). For RFK two different uncertainty quantification approaches were adopted based on kriging variance (RFK-1) and bootstrapping (RFK-2). The selection of the potential environmental covariates was based on Jenny's factorial model of soil formation. The spatial predictions of SOCS and their uncertainty models were evaluated and compared using a control dataset. For this purpose, we applied the most common measures (i.e. mean error and root mean square error), furthermore, accuracy plot and G statistic. According to our results, QRF and SGS produced the best uncertainty models. UK and RFK-2 overestimated the uncertainty whereas RFK-1 produced the worst uncertainty quantification according to the accuracy plots and G statistics. We could draw the general conclusion that there is a need to validate the uncertainty models. Furthermore, great attention should be paid to the assumptions made in uncertainty modelling.