Combining laboratory measurements and proximal soil sensing data in digital soil mapping approaches

Digital soil mapping (DSM) products are limited in accuracy because of the lack inputs. Soil sensing is a promising alternative to direct measurements that could provide much denser spatial samplings. Although using relevant and detailed input DSM considered as vital increase prediction performances, there has been no studies literature compare develop methods for integrating new sources data can be applied inputs DSM. This paper fills this gap on example electrical conductivities from sites with laboratory measurements, in-field EM38MK2 spatially exhaustive covariates. Three different approaches tested putting synergy real measurements: (i) measurement measured points, (ii) used building covariate (iii) soft regression co-kriging approach. According analysis's financial expenditure, choosing an optimal sample size merge analysis surrogate was done best method. The results showed utility salinity Regression method integration impact gains performance becomes greater sizes decrease. Hence, other areas worldwide accessible, research's future utilization possible way tackle one essential constraints