Information about the spatiotemporal variability of soil salinity is important for managing salinization in gravel-sand mulched fields. We used inverse distance weighting (IDW) and cokriging to model the spatial variability of soil salinity from 2013 to 2016 and used an autoregressive moving-average (ARMA) model time series to analyze the temporal variability. The objectives of this paper are (a) to compare IDW and cokriging for predicting salinity in deep soil layers from surface data, thus finding a more appropriate method to model the spatial variability of soil salinity, and, using ARMA time series, (b) to identify one or a few sampling points, where soil salt content is the most temporally stable, to increase sampling efficiency or decrease cost and to estimate the overall soil salt content of a field. The IDW interpolation was more accurate than cokriging when using surface salt content to estimate the content in deep layers; so, we used IDW to interpolate the data and draw spatial distribution maps of salt content. Salinity in the 0-10 cm layer gradually decreased with the amount of gravel-sand mulching, from 1.02 to 0.7 g/kg over four years, and increased with depth. ARMA was accurate when using sample dates to predict soil salinity in the time series, and the model was more stable. The stability of the salt spatial patterns over time and along the soil profile allowed us to identify a location representative of the field-mean salt content, with mean relative error ranging between 0.56 and 2.19%. The monitoring of soil salt from a few observations is thus a valuable tool for practitioners and will aid the management of soil salt in gravel-sand-mulched fields in arid regions, with a range of potential applications beyond the framework of monitoring salinity.