## 4 Results
### Linear Regression Analysis for Soil Electrical Conductivity Estimation  
Table 6 summarizes input data for the LRA to estimate the ECe of HH and VV modes. A scatter plot between the dependent variable (ECe average) and independent variables of two modes (ECa-HH and ECa-VV) for LRA is displayed in Figure 8. The results of LRA for soil ECe estimation of two modes are summarized in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Sub-District** & **Code** & **Longitude (X)** & **Latitude (Y)** & \
\begin{tabular}{c} **Dependent Variable** \\ **ECe\({}_{\text{average}}\) (mS/m)** \\ \end{tabular} &
\begin{tabular}{c} **Independent Variable** \\ **ECe-HH** \\ **(mS/m)** \\ \end{tabular} \\ \hline  
& 480 & 525 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Equations for ECe estimation of HH and VV modes using LRA.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Measuring** & **Regression** & **R** & **R2** & **Adjusted R2** & \
\begin{tabular}{c} _p-Value of_ \\ **Regression** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Samples** \\ \end{tabular} \\ \hline \(\mathrm{HH}\) & ECe = 7.377 & 0.873 & 0.762 & 0.754 & 0.000 & 30 \\  & ECe = 8.508 & 0.861 & 0.742 & 0.732 & 0.000 & 30 \\  & ECe = 1460.998 & 0.861 & 0.742 & 0.732 & 0.000 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The average ECe and ECa of two measuring modes for linear regression analysis.  
As a result, in Table 6 and Figure 8, it can be observed that ECa and ECe data can be divided into two groups. First, relatively large conductivities, where ECe is significantly greater than ECa, occur in the Non Thai, Dan Chak, Samrong, Kampang, Khang Phlu, and Ban Wang sub-districts. Second, relatively small conductivities, where ECa is significantly greater than ECe, occur in the Sai O, Makha, Thanon Pho, and Banlang sub-districts. The main reason to support these phenomena is the variation of soil conductivities due to the dynamic nature of the soluble salts in the study area.  
Moreover, it can be observed that 13 samples with ECe values less than 100 show a moderate correlation with ECa values. These samples should be considered separately. However, when we separate data into two groups, the number of samples is too small. [PERSON] and [PERSON] [61] suggested that the minimum sample size for linear regression should be equal to or more than 25. Additionally, this study aims to examine the distribution of soil salinity and its severity in the whole study area.  
As resulted in Table 7, the R value between ECa and ECe of the two modes are 0.873 and 0.861, respectively. ECa has a very strong positive relationship with ECe [44; 62]. The R\({}^{2}\) values of HH and VV modes are 0.762 and 0.742, respectively. Both R\({}^{2}\) values provide a very strong goodness-of-fit measure for the linear regression model [45; 63; 64]. Besides, the \(p\)-values of regression of HH and VV are 0.000 and 0.000, respectively. The derived \(p\)-values of the two modes, with a value of less than 0.01, show very strong evidence of a  
Figure 8: A scatter plot between ECa of two measuring modes: (**a**) HH and (**b**) VV and ECe for LRA.  
difference between means in a two-sample comparison [46; 65; 66]. So, the result of LRA can be validated and accepted for estimating ECe in the remaining ECa locations.