Real-time monitoring of soil salinity based on field samples and laboratory analyses is a costly and time demanding procedure, so that sound methods that could reduce the burden by making use of cheaper data would be a step toward a more sustainable salinity hazards monitoring system on the long run. Typically, this involves replacing presumably error-free laboratory salinity measurements with indirect measurements that are however affected by various source of uncertainties, and these uncertainties need to be accounted for in order to avoid compromising the quality of the final results. More specifically, in a spatiotemporalpredictionframeworkwheresalinitymapsneedtobedrawnrepeatedlyatvarioustimeinstants and where salinity values need to be compared over time for agricultural areas that are prone to salinity hazards, it is of major importance to process these uncertainties in a sound way, as failing to do so would impair our ability to detect salinity changes at an early stage for taking preventive actions. The aim of this paper is to propose a filtered kriging framework that allows the user to rely on cheap field sampled electrical conductivity (EC) measurements, that cannot however be assumed as error-free. Field EC measurements need to be calibrated from laboratory measurements and the corresponding calibration errors cannot be neglected. Moreover, when sampling is repeated over time, positioning errors are quite commonandcanadverselyimpacttheresultsduetotheinclusionofanextravariabilitysource.Itisshown how these uncertainties can be quantified and successfully processed afterwards for improving both the reliability of the spatial predictions and temporal comparisons of soil salinity. The idea is to rely on a same general optimal linear predictor that can be easily adapted to get rid of these unwanted effects. TheprocedureisillustratedbyusingarichdatasetofECmeasurementsthatcoveratimespanofsevenyears inthewesternpartofUrmiaLake,northwestIran.Fromthesedata,itisshownhowcal
