## 1 Introduction
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Distribution of Saline Soil and Its Severity** & **Area (km\({}^{2}\))** & **Percent** \\ \hline Strongly saline, salt surface ground more than 50\% & 284.68 & 1.40 \\ Moderately saline, salt surface ground 10–50\% & 911.94 & 4.49 \\ Slightly saline, salt surface ground 1–10\% & 1709.27 & 8.41 \\ Lower terrain with the potential to be saline soil & 2607.55 & 12.84 \\ Highland with a cover on the rock salt & 2827.40 & 13.92 \\ Relative units of saline soil at least 1–10\% of the area & 16.65 & 0.082 \\ Relative units of highland/salt surface ground 1–10\% & 109.85 & 0.541 \\ Relative units of highland with a cover on the rock salt/lower & 414.19 & 2.04 \\ terrain with the potential to be saline soil & 9025.60 & 44.43 \\ Non-saline & 2406.83 & 11.85 \\ Hill area & **20**,**493.96** & **100** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Distribution of saline soil and its severity in Nakhon Ratchasima province [14]. At a national level, [PERSON] et al. [27] used an EM device, EM38, with field survey data and inversion software to map the distribution of clay and cation exchange capacity (CEC) in sandy and infertile soil in Ban Haet district, Khon Kaen, Thailand. In their study, they firstly examined a linear regression relationship between EM38 ECa with either topsoil (0-0.3 m), subsurface (0.3-0.6 m), or subsoil (0.6-0.9 m) clay and CEC. Then, the predicted equations were applied to map clay and CEC on three different levels. Meanwhile, at an international level, [PERSON] and [PERSON] [34] used a mobile electromagnetic induction system, including Geonics EM38 and EM31, to perform an electromagnetic field survey and quantify the spatial pattern of soil salinity in Dongyihe Village of Kenli County, Shandong Province, China. In their study, the optimal operation modes of EM38 and EM31 were first determined to establish multiple linear regression models for estimating salinity from apparent soil electrical conductivity (ECa). Then, spatial trend and semi-variogram were analyzed, and spatial distribution of field salinity status was mapped. Their results suggested that ECa (EM38 and EM31) data correlated highly with salinity. Likewise, [PERSON] and [PERSON] [35] examined seasonal and spatial soil salinity changes using the electromagnetic induction method and field survey data in the Werigan-Kuga River Delta Oasis, Xinjiang, China. Their results suggested that ECa obtained from EM38 was highly correlated with soil salinity. Similarly, [PERSON] and [PERSON] [36] used Geonics EM38 with field survey data to assess soil salinity in North Nile Delta, Egypt. In this study, statistical relations between ECa and ECe were first examined and then applied to predict and map salinity. They found that the actual and the predicted ECe levels were highly correlated. Besides, [PERSON] et al. [37] used a dual dipole EM38 device and the ground-truth EC survey data to quantify salinization's spatial and temporal progression and identify yield potential for tomatoes in California, USA. They found that the EM-predicted conductivity was consistent with the ground-truth EC survey data and increased with depth. Around 43% surveyed area was conducive to attaining 80% of the total yield potential of tomatoes. With this research, we aimed to predict and map soil salinity severity in the sub-district of the Non Thai district, Nakhon Ratchasima province, with a suitable interpolation method, which is a crucial method to estimate unknown data using known sample data [38]. [PERSON] and [PERSON] [39] categorized the interpolation method for digital soil mapping into four groups: non-geostatistical method, univariate geostatistical method, multivariate geostatistical method, and mixed method. In this study, the Inverse Distance Weighting  
Figure 1: The signal generation and the response of the EM sensors (Modified from [PERSON] et al. [33]).