*2.2. Image Acquisition*

Ten high-resolution thermal images were acquired between 21 July and 26 August 2019. A sensitive (±<sup>2</sup> ◦C) uncooled FLIR SC655 camera (FLIR® Systems, Inc., Billerica, MA, USA) with 640 × 480 resolution was mounted on a six-engine drone (Datamap Group, Bnei Brak, Israel). The flight height for all campaigns was 100 m, and the subsequent ground spatial resolution was approximately 7 cm. All campaigns were conducted midday between 12:30 and 15:15 on cloudless days. Mosaics were created using the ThermCam software (FLIR® Systems, Inc., Billerica, MA, USA) and Pix4D mapper software (Pix4D, Prilly, Switzerland). All of the thermal images were resampled to the average pixel size of the ten images, which was 7.3737 cm.

Two RGB images were acquired on 21 July and 12 August 2019 immediately prior to the respective thermal image campaign using a Phantom 4 Pro V2 (DJI Technology Co., Ltd., Shenzhen, China). The ground spatial resolution was approximately 3 cm.

#### *2.3. Canopy Extraction Methods*

Four methods of canopy extraction from thermal images were applied and evaluated, representing a range of techniques found in the literature. In this study, they were executed primarily using the ArcGIS Pro software (ESRI, Redlands, CA, USA).

**Figure 2.** Data acquisition and analysis of orchard canopy extraction accuracy, canopy temperature, and orchard water status using the 2-pixel erosion (2PE), edge detection (ED), vegetation segmentation (VS), and RGB binary masking (RGB-BM) canopy extraction methods (green boxes). Canopy temperature per management cell (MC) was calculated using the average of 100% of canopy pixels (T100%) (orange boxes) and the average of the coolest 33% of canopy pixels (T33%) (blue boxes). Orchard water status was estimated using the crop water stress index (CWSI) and the estimated stem water potential (SWPe). The SWPe was based on a tree-scale stem water potential (SWP) and CWSI relationship established using each canopy extraction method and each canopy temperature calculation approach.

Three methods followed the 1-source approach, incorporating both statistical and spatial analyses, using only thermal images:

	- a. Extraction of the coolest two-thirds of temperature pixels from the whole orchard histogram to separate canopy from non-canopy (mixed and soil) pixels [27] (statistical).
	- b. Morphological erosion of the two pixels [28] (spatial).
	- a. Image sharpening with high pass filter (spatial).
	- b. Determination of edges (statistical).
	- c. Morphological expansion of three pixels (spatial).
	- d. Thresholding to extract only canopy pixels (statistical).
	- a. Temperature histogram analysis using the Otsu [30] and full-width-half-maxim um [11] algorithms to differentiate between canopy and non-canopy pixels (statistical).
	- b. Watershed segmentation to define the basin of each peach tree [14] (spatial).

The temperature values per pixel of the 2PE, ED, and VS methods were retrieved by multiplying the respective final layer of canopy pixels by the original thermal image.

A fourth method followed the multi-source approach, using a thermal and an RGB image:

	- a. Resampling of the RGB to 7.3737 cm.
	- b. Georeferencing between the RGB and thermal layers.
	- c. The excess green index (ExG) (2G-R-B) is calculated per pixel and effectively differentiates between plant and soil pixels [31].
	- d. Binary thresholding of the ExG layer to separate canopy from non-canopy pixels [30] (statistical).
	- e. Thermal image masking using the ExG layer (post-binary thresholding) [24] to retrieve the temperature values of each pixel (spatial).
