## 2 Materials and Methods
### Thresholding Process for Landcover Classification  
An operational classification approach based on iterative thresholds was adopted for this study. This approach, which generates bilevel classes of satellite-derived measures, has been previously used for landcover classification in the irrigation regions [46; 47]. Here, we describe the process of both approaches: optical and thermal.  
It is widely accepted that an 'irrigated' crop has high vegetation and high soil moisture as compared to non-irrigated crops or other dry landcovers. On this basis, STR (soil moisture indicator) and NDVI (vegetation status) were segmented into two classes each. STR classes referred to 'irrigated' (relatively high soil moisture) and 'non-irrigated' (relatively low soil moisture), and vegetation classes were 'crop' (relatively high NDVI) and 'non-crop' (relatively low NDVI). An iterative thresholding method was used to achieve the binary classification by minimizing within-class variance, \(\sigma^{2}_{Within}\)[48]:  
\[\sigma^{2}_{Within}(T_{i})=\omega_{0}\sigma^{2}_{0}(T_{i})+\omega_{1}\sigma^{ 2}_{1}(T_{i}) \tag{8}\]  
where \((T_{i})\) is the threshold which varies by iteration \(i\); \(\omega_{0}\) and \(\omega_{1}\) are the weights of the two classes; and \(\sigma^{2}_{0}\) and \(\sigma^{2}_{1}\) are the variance of the two classes.  
For applying the thresholding procedure in operation, the relationship of \(\sigma^{2}_{Within}\) with between-class variance (\(\sigma^{2}_{Between}\)) and total variance (\(\sigma^{2}_{Total}\)) was used as follows [48]:  
\[\sigma^{2}_{Within}(T_{i})=\sigma^{2}_{Total}(T_{i})-\sigma^{2}_{Between}(T_{ i}) \tag{9}\]  
Initial NDVI threshold (\(\alpha\)) was taken as 0.4. All pixels \(\geq\alpha\) were considered as 'crop'. Initial soil moisture threshold was the median value of STR (\(\varphi\)) or Ts\(-\)Ta (\(\beta\)). The iteration interval was set at 0.005 within the limit of \(\pm\)0.025 of the initial thresholds. Altogether, 11 iterations each for NDVI and STR/Ts\(-\)Ta were performed for the image. Thresholds with minimum \(\sigma^{2}_{Within}\) were used for binary classification.  
In the context of the 'optical approach', each pixel was identified with the defined binary classes of NDVI and STR. This resulted in four combinations, as shown in Figure 3A. Pixels in sector (1) denote dry conditions with no or low vegetation. Pixels in sector (2) denote wet conditions with low or no vegetation. Pixels in sector (3) indicate some vegetation, which may be crop or pasture but without irrigation. Sector (4) denotes vegetation with wet condition indicating irrigated crop or pasture.  
In the context of the 'thermal approach', the thresholding process for both NDVI and Ts\(-\)Ta was the same as described earlier. As shown in Figure 3B, the four sectors define the vegetation status and wet/dry conditions. Here, the pixels in Sector (4) indicate irrigated crop or pasture.  
Validation was carried out on the pixels aggregated to the extent of irrigation water delivery licences. Water delivery for each license has been linked to spatial unit consisting of one or more paddocks. These units hereinafter are referred to as farms. The mapped results of irrigated landcover were compared with the actual irrigation deliveries to farms. Information on irrigation water supplies at farm level was sourced from the Victorian Water Register (VWR), a state-wide irrigation water database ([[https://waterregister.vic.gov.au/](https://waterregister.vic.gov.au/)]([https://waterregister.vic.gov.au/](https://waterregister.vic.gov.au/)), accessed on 8 December 2023). Figure 4 shows the location of farms which have been included in the validation process. In this figure, the locations of farms have been shown by farm centroids instead of farm boundaries due to privacy reasons.