## 3 Results  
### Distribution of Input Measures  
The classification process takes two variables as inputs in both approaches--'optical' and 'thermal'. NDVI is one variable, which is a common measure used in both approaches, as shown in Figure 5A. Although the NDVI ranges between 0 and \(\geq\)0.8 in CGID (Table 2), the notable areas are those with NDVI \(\geq\) 0.4, shown as shades of green (Figure 5A). These areas are predominantly under perennial horticulture, perennial pastures or summer crops. Figure 4: Centroids showing the location of the farms in CGID used in the validation process. The background is the Landsat-8 images with bands 5/4/3 (displayed as a false colour composite image). Figure 3: (**A**) Concept of thresholding classification (optical approach). \(\alpha\) and \(\varphi\) are the thresholds for NDVI and STR, respectively, determined by an iterative thresholding method by minimizing within-class variance. Irrigated pixels are those with NDVI \(\geq\)\(\alpha\) and STR \(\geq\)\(\varphi\) located within Sector 4. (**B**) Concept of thresholding classification (thermal approach). \(\alpha\) and \(\beta\) are the thresholds for NDVI and Ts–Ta, respectively, determined by an iterative thresholding method by minimizing within-class variance. Irrigated pixels are those with NDVI \(\geq\)\(\alpha\) and Ts–Ta \(\leq\)\(\beta\) in Sector 4. The values of STR in CGID range between 0 and \(\geq\)36 (Table 2). However, the notable areas are those with STR values of 2 and over, indicating the presence of substantial surface moisture (Figure 5B). Here, the distinction must be made between potential irrigation areas, which are exclusively agricultural properties, and the riverine zone of native vegetation, where the source of surface moisture is not irrigation. Figure 5C shows the distribution of NDWI in CGID. All NDWI values above zero indicate the presence of surface water (Table 2). In addition to a few large water bodies like Waranga Basin in the south-central part, farm dams of varying sizes are shown across the district. The areas with surface water were excluded while evaluating STR values. The \"Ts\(-\)Ta' input variable for the thermal approach of classification is largely driven by the satellite-derived surface temperature (Ts), moderated by air temperature (Ta). The spatial variation of Ta in CGID was within 3\({}^{\circ}\)-4 \({}^{\circ}\)C (Table 2). It ranged between under 21 \({}^{\circ}\)C and approximately 24 \({}^{\circ}\)C, increasing from the south-east to the north-west direction (Figure 5D). On the other hand, the Ts ranged between 26\({}^{\circ}\) and over 40 \({}^{\circ}\)C (Table 2). Relatively cooler surfaces were with \(<\)34 \({}^{\circ}\)C, roughly spread in patches but largely contiguous in the riverine areas in the east (Figure 5E). The distribution of Ts\(-\)Ta in CGID ranged between 0\({}^{\circ}\) and 24 \({}^{\circ}\)C (Table 2, Figure 5F). Here, the relatively cooler areas appear more pronounced as compared to those in the Ts. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Statistics** & **NDVI** & **STR *** & **NDWI** & **Ta** & **Ts** & **Ts\(-\)Ta** \\ \hline Minimum & 0.000 & 0.000 & \(-\)0.776 & \(-\)14.938 & 0.000 & 0.000 \\ Mean & 0.286 & 1.800 & \(-\)0.379 & 22.963 & 38.304 & 15.125 \\ Median & 0.239 & 1.356 & \(-\)0.355 & 22.923 & 38.754 & 15.562 \\ Maximum & 0.875 & 36.696 & 0.818 & 36.813 & 47.227 & 24.081 \\ SD & 0.126 & 1.457 & 0.101 & 0.917 & 2.7125 & 2.455 \\ \hline \hline \end{tabular}  
* Excluding pixels with ‘surface water’ (NDVI \(<\) 0.1 and NDWI \(>\) 0.1).  
\end{table} 
Table 2: Statistics of individual input measures used in classification. Figure 5: Maps of individual input measures in CGID. (**A**) NDVI, (**B**) STR, (**C**) NWDI, (**D**) Ta, (**E**) Ts and (**F**) Ts\(-\)Ta. A visual comparison of the Ts\(-\)Ta map (Figure 5F) with that of the STR (Figure 5B) shows marked similarities. The locations of high surface moisture (i.e., high STR and low Ts\(-\)Ta) appear to be the same.