**4. Discussion**

Canopy extraction that is based purely on the temperature attribute assumes a distinct difference between soil and canopy temperatures. While this is largely true, canopy temperature can be similar to shadowed or wet soil [40], and the temperature of mixed pixels can be similar to canopy suffering from water stress [16]. In comparison, the canopy of RGB images has a different multispectral signature than soil, enabling the use of spectral vegetation indices for canopy classification. Accordingly, the ExG index, a popular index for vegetation identification [41], served as the basis for binary thresholding in this study. RGB images also have higher spatial resolution in comparison to thermal images. These two characteristics led to the assumption that RGB-based canopy extraction would be more accurate than using a single thermal image. This assumption was supported to some extent by this study. Higher accuracy of canopy extraction was obtained by the RGB-based method compared to the thermal-based methods. However, between-row weeds were misclassified as tree canopy with the RGB-based method, leading to an atypical increase in canopy area during stage III. Additionally, inaccuracies in geographical and geometrical fit

between the RGB mask and the thermal image are a known drawback with multi-source methods, such as RGB-BM [2], and explain the inclusion of warm canopy edges in the canopy mask.

Between-row weeds and canopy edges are highly affected by surrounding high soil temperatures, therefore leading to the overestimation of canopy temperature and CWSI in this study. Camino et al. [16] also found that warm edge pixels cause significant errors in almond tree canopy temperature and CWSI values. The VS method also included warm temperature pixels on the edges of all trees in the orchard. Similar to the RGB-BM method, the VS canopy temperature and CWSI values were higher in comparison to the 2PE and ED methods. Conversely, the 2PE and ED methods were both able to adequately remove canopy edge pixels by incorporating morphological erosion and edge detection algorithms, respectively. The difference between these two groups of methods, 2PE–ED and VS–RGB-BM, was also evident in the SWP–CWSI linear models calculated using the CWSI\_T100% values. The superiority of the 2PE and ED extraction methods over the RGB-BM method implies that the multispectral nature and the high spatial resolution of the RGB images do not obviate the need to incorporate spatial analyses, such as morphological erosion and edge detection algorithms. This suggests that the contribution of the RGB images is not significant for the canopy extraction stage and canopy pixels can be extracted with high accuracy and reliability merely with thermal images. Furthermore, the multi-source approach is slightly more complex and time consuming than the one-source approach, primarily due to the critical georeferencing step. Thus, it is concluded that one-source thermal-based approaches can be preferably used for canopy extraction.

Canopy temperature was estimated in this study using the average of all canopy pixels (T100%) [7,10] and of the coolest 33% canopy pixels (T33%) [6,26,27]. The T100% values were substantially higher than the T33% values for all MCs, dates, and canopy extraction methods. Within-crown temperature variability has been documented for almond trees [16,42] and is partially affected by the inclusion of pixels at the edge of the canopy. The T33% approach is less influenced by canopy temperature heterogeneity [6] and minimizes the effect of mixed pixels. This idea is reinforced in the present study by the similar spatial patterns and canopy temperatures between the canopy extraction methods using the T33% calculation approach. The significant effect on temperature using the T100% approach resulted in a pronounced effect on the CWSI.

Substantial differences were apparent between the extraction methods within the CWSI\_T100% dataset (Figure 7). The VS and RGB-BM values reached unexpectedly high values for well-watered peach trees: 0.53–1.37 (VS) and 0.45–1.39 (RGB-BM). Furthermore, the maximum CWSI\_T100% values of the 2PE and ED methods were extremely high: 0.77 (2PE) and 0.82 (ED). A CWSI value of one indicates an extremely stressed peach tree with closed stomata. For reference, in one of the experimental plots that formed the basis for the SWP–CWSI models in this study, irrigation was suspended for a total of three weeks prior to the imaging campaign. In this plot, and in stark contrast to the VS method, the CWSI\_T100% values of the measurement trees ranged between 0.58 and 0.96. CWSI values higher than one imply that non-canopy pixels are included in the calculation. In contrast, no significant differences were found between the CWSI values that were calculated using the T33% approach. Additionally, and similar to the findings of Cohen et al. (2017) in cotton, the SWP–CWSI models using the T100% approach were inferior in comparison to the T33% approach. Most importantly, the T33% dataset produced similar SWP–CWSI models irrespective of the canopy extraction method used, while the T100% yielded very different models. These results highlight the robustness of the T33% approach and indicate that it is not sensitive to the canopy extraction accuracy.

The robustness of the T33% approach is further emphasized by comparing the SWPe values to the optimal water status range. This optimal range of SWP constitutes the basis for irrigation decision making [27]. Therefore, a comparison of the SWPe distribution to the optimal range can indicate the extent to which a specific canopy extraction method is prone to water stress overestimation and leads to hypothetical over-irrigation as a result. Within the SWPe\_T33%, a large percentage of the estimated SWP values were within range for the 2PE, ED, and VS methods, indicating a theoretical irrigation policy that adequately brings and maintains the MC in the optimal range. Higher percentages of above-range SWP values were calculated with the VS and RGM-BM methods (compared to additional extraction methods), indicating that the orchard was supposedly under a higher degree of stress, necessitating increased irrigation.

The comparison of the SWPe\_T100% distribution of values to the optimal water status range further reinforces the fact that the estimated SWP values calculated with the T100% method, and in particular using the RGB-BM canopy extraction method, possibly overestimate orchard water status, hypothetically resulting in more-than-optimal irrigation application with subsequent agronomic and economic consequences [32]. It should be noted that none of the canopy extraction methods or temperature calculation methods sufficiently estimated above-range (less negative) water status or below-range extremely stressed (more negative) values in the SWPe\_T33% dataset. This result, rather than indicating the quality of the canopy extraction, signifies a general limitation of water status assessment using thermal images. Thermal-based water status estimation suffers from different types of inaccuracies, including the effect of meteorological conditions and different approaches for determination of Twet and Tdry values.
