Improved spatiotemporal monitoring of soil salinity using filtered kriging with measurement errors: An application to the West Urmia Lake, Iran

The procedure is illustrated by using a rich data set of EC measurements that cover a time span of seven years in the western part of Urmia Lake, northwest Iran. From these data, it is shown how calibration errors can be considered as spatially independent and zero-mean Gaussian distributed, while laboratory measurements exhibit a clear spatial structure but are also affected by a not inconsiderable spatial nugget effect, which is in turn impacting the errors for field EC measurements due to the positioning errors. By relying on a linear optimal predictor that reduces here to filtered kriging with measurement errors, it is shown that filtering out these two random effect components clearly improves the quality of the results when it comes to map EC values and to detect changes that occurred over time. Comparing filtered values for the successive sampling campaigns provided evidence that a major salinity shift did occur between autumn 2011 and autumn 2014 while the other parts of the area were left unchanged by comparison. From this study, it can be concluded that even if the only errors involved in this work were linked to calibration and positioning errors, the methodology is general enough to process various sources of uncertainties in general. It is thus a valuable tool for practitioners, with a field of potential applications that goes beyond the framework of salinity monitoring.