## 4 Discussion  
The feasibility of using the STR along with the NDVI (optical approach) to map irrigated landcover has been confirmed by a high-level overall accuracy assessment (97.6%). These results of irrigated landcover by an optical approach were found to be comparable with those of a parallel process that used the Ts\(-\)Ta along with the NDVI (thermal approach). However, there was a noticeable difference in the user's accuracy for the 'irrigated' class between the two approaches (88% thermal to 98.3% optical). This could have occurred due to the difference in the spatial resolution of a variable for surface moisture. The STR is based on Band 7 of Landsat 8 which has a 30 m resolution, whereas Band 11, which is used in the Ts\(-\)Ta, is at 100 m resolution, though it is resampled to 30 m [51]. For the same reason, the 'wet no vegetation' areas were estimated higher by the optical approach than by the thermal approach (Table 3). The STR is expected to work in all ground conditions, since it has been tested by [PERSON] et al. [25] for a range of soil types, and was used in this study successfully. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{(a) Occurrence (Optical approach)} \\ \hline \multirow{2}{*}{Classification} & \multicolumn{2}{c}{Reference *} & \multirow{2}{*}{_Total_} \\ \cline{2-3} \cline{5-5}  & & \multicolumn{1}{c}{Irrigated} & \multicolumn{1}{c}{Non-Irrigated} \\ \hline Irrigated & 451 & 8 & 459 \\ Non-Irrigated & 18 & 624 & 642 \\ \hline \multicolumn{4}{c}{_Total_} & 469 & 632 & 1101 \\ \hline \multicolumn{4}{c}{_*_ Classification according to water delivery records} \\ \hline \multicolumn{4}{c}{(b) Accuracy (Optical approach)} \\ \hline \multicolumn{4}{c}{Producer’s Accuracy \%} & User’s Accuracy \% \\ \hline Irrigated & 96.2 & 98.3 \\ Non-Irrigated & 98.7 & 97.2 \\ \hline \multicolumn{4}{c}{Overall accuracy = 97.6\%} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation of irrigated landcover classification (Optical approach). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{(a) Occurrence (Thermal approach)} \\ \hline \multirow{2}{*}{Classification} & \multicolumn{2}{c}{Reference *} & \multirow{2}{*}{_Total_} \\ \cline{2-3} \cline{5-5}  & & \multicolumn{1}{c}{Irrigated} & \multicolumn{1}{c}{Non-Irrigated} \\ \hline Irrigated & 404 & 55 & 459 \\ Non-Irrigated & 12 & 630 & 642 \\ \hline \multicolumn{4}{c}{_Total_} & 416 & _685_ & 1101 \\ \hline \multicolumn{4}{c}{_*_ Classification according to water delivery records} \\ \hline \multicolumn{4}{c}{(b) Accuracy (Thermal approach)} \\ \hline \multicolumn{4}{c}{Producer’s Accuracy \%} & User’s Accuracy \% \\ \hline Irrigated & 97.1 & 88.0 \\ Non-Irrigated & 92.0 & 98.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation of irrigated landcover classification (Thermal approach). The overriding objective of our paper was to assess the feasibility of using the STR as a soil moisture measure, in place of the TIR-based measure. We attempted to do that by using a simple but effective method of classification which has been used previously. Our iterative thresholding procedure was based on minimizing the within-class variance for bilevel segmentation. This step is like [PERSON]'s method [48]. However, our approach did not include an automatic optimization process unlike [PERSON]'s method. [PERSON]'s method, with an automatic optimization process, performs well to find a single threshold only when the histogram has a distinct bimodal distribution. In other situations, it does not deliver an optimal result. The variables for our study area were not bimodal. Therefore, we introduced certain constraints to the method, i.e., an initial threshold and iteration limits, as described earlier in Section 2.6. The initial threshold for the NDVI (0.4) was on the assumption that, in an agriculture enterprise, vegetation with NDVI < 0.4 is unlikely to be a managed crop or pasture.