Abstract. Lezíria Grande de Vila Franca de Xira, located in Portugal, is an important agricultural system where soil faces the risk of salinization due
to climate change, as the level and salinity of groundwater are likely to
increase as a result of the rise of the sea water level and consequently of the estuary. These changes can also affect the salinity of the irrigation
water which is collected upstream of the estuary. Soil salinity can be
assessed over large areas by the following rationale: (1) use of
electromagnetic induction (EMI) to measure the soil apparent electrical
conductivity (ECa, mS m−1); (2) inversion of ECa to obtain
electromagnetic conductivity imaging (EMCI) which provides the spatial distribution of the soil electrical conductivity (σ, mS m−1);
(3) calibration process consisting of a regression between σ and the
electrical conductivity of the saturated soil paste extract (ECe,
dS m−1), used as a proxy for soil salinity; and (4) conversion of EMCI into salinity cross sections using the obtained calibration equation. In this study, EMI surveys and soil sampling were carried out between
May 2017 and October 2018 at four locations with different salinity levels
across the study area of Lezíria de Vila Franca. A previously developed
regional calibration was used for predicting ECe from EMCI. Using time-lapse EMCI data, this study aims (1) to evaluate the ability of the regional calibration to predict soil salinity and (2) to perform a preliminary qualitative analysis of soil salinity dynamics in the study
area. The validation analysis showed that ECe was predicted with a
root mean square error (RMSE) of 3.14 dS m−1 in a range of 52.35 dS m−1, slightly overestimated (−1.23 dS m−1), with a strong Lin's concordance correlation coefficient (CCC) of 0.94 and high linearity between
measured and predicted data (R2=0.88). It was also observed that
the prediction ability of the regional calibration is more influenced by
spatial variability of data than temporal variability of data. Soil salinity
cross sections were generated for each date and location of data collection,
revealing qualitative salinity fluctuations related to the input of salts
and water either through irrigation, precipitation, or level and salinity of groundwater. Time-lapse EMCI is developing into a valid methodology for
evaluating the risk of soil salinization, so it can further support the
evaluation and adoption of proper agricultural management strategies,
especially in irrigated areas, where continuous monitoring of soil salinity
dynamics is required.