“…For example, it was possible to predict the soil nutrient contents directly with remote sensing data, e.g., it was informed that SN was closely related to the natural vegetation and the above-ground biomass [36,82], while SP was more related to the parent material [85]. Different techniques have been used in the literature to predict soil distribution, e.g., multiple linear regression [10,25,45,46,82,86], regression kriging [87][88][89], random forest models [26,35,89], geographically weighted regression [88], cubist models [90,91] and principal component regressions [92]. These strategies were successfully implemented in different natural and managed environments and according to different research objectives, but none showed to be the best one for all the stated forests and landscapes.…”