## 6 Conclusions  
Handheld electromagnetic sensors, EM38, were used to measure ECa at 405 locations with two standard measuring modes: HH (0-0.75 m depth) and VV (0-1.5 m depth) over 45 sites in the study area. Thirty soil samples from the topsoil and subsoil layers were collected for the electrical conductivity of the saturated soil paste extract measurement in the laboratory. Then, a linear relationship between the measured ECa and ECe data at the exact location was identified using linear regression analysis for estimating the remainingECa data. The results showed a strong positive relationship between ECa and ECe. R values of HH and VV modes were 0.873 and 0.861, and R\({}^{2}\) were 0.762 and 0.742, respectively. The model has a \(p\)-value of less than 0.001. The total calibrated soil conductivity data were further randomly categorized into two datasets: the modeling dataset (70%), with 290 points, and the testing dataset (30%), with 123 points, for soil salinity prediction and validation, respectively.  
For soil salinity prediction, the modeling dataset with 284 points was successfully applied to predict soil salinity of two measuring modes using IDW, OK, CCK, and RK methods. The patterns of soil salinity distribution from two measuring modes with four different methods at the sub-district level were slightly different. After that, the predicted results of the four methods were assessed for accuracy based on the testing dataset with 121 points using ME, RMSE, R\({}^{2}\) and PBIAS, and they were applied to identify a suitable interpolation method for soil salinity prediction. It was found that CCK is a suitable spatial interpolation method for soil salinity prediction in HH mode. At the same time, IDW is a suitable spatial interpolation method for soil salinity prediction for the VV mode.  
Finally, as a suitable spatial interpolation method, the derived soil salinity prediction data of HH and VV modes using CCK and OK were further applied to classify soil salinity severity according to FAO standards. As expected, the most dominant soil salinity severity classifications under the two measuring modes were strongly and very strongly saline. The spatial distribution of soil salinity severity would be beneficial in supporting site-specific strategies for crop management. Consequently, this research methodology can be applied to conduct soil salinity surveys in other areas to reduce costs and time for government agencies in Thailand.  
Nevertheless, to apply the EMI method for soil salinity surveys, the users should first understand the principle of EMI and how to calibrate and operate the EM device properly for accurate ECa measurement. Additionally, sophisticated regression analysis, such as non-linear regression, should be examined to identify a relationship between ECa and ECe for estimating the ECe of the remaining sample points.  
Conceptualization, S.O. and Y.J.; methodology, S.O. and Y.J.; software, Y.J.; validation, S.O. and Y.J.; formal analysis, S.O. and Y.J.; investigation, S.O. and Y.J.; data curation, Y.J.; writing--original draft preparation, Y.J.; writing--review and editing, S.O.; visualization, Y.J.; supervision, S.O. All authors have read and agreed to the published version of the manuscript.  
This research was supported by external funding from the National Research Council of Thailand (NRCT), Grant Number N41D650186.  
Not applicable.  
This research and innovation activity is funded by the National Research Council of Thailand (NRCT). The authors would like to thank the Suranaree University of Technology for supporting the facilities to undertake this research. Special thanks from the authors to the anonymous reviewers for their valuable comments and suggestions that improved our manuscript from various perspectives.  
The authors declare no conflict of interest.