*1.2. Approaches of Thermal Image-Based Canopy Extraction*

Canopy extraction approaches incorporating thermal imagery include methods that use a single thermal infrared image (1-source) and other methods that use a thermal infrared image and additional remotely sensed images, usually RGB (multi-source). One-source methods include purely threshold-based statistical analysis on the one hand and coupled statistical and spatial analyses on the other hand. Statistical analysis of a temperature histogram to identify canopy pixels within a thermal image has been performed in orchards [11,12] where canopy can be distinguished from soil. In such cases, temperature histograms are characterized by a bimodal distribution, where the canopy and soil pixels are represented by cooler and warmer peaks, respectively. Mixed pixels, which include combinations of canopy, soil, weeds, foreign objects, and shade in a single pixel, are generally composed of a "saddle" area between the two peaks. Depending on the crop architecture, the distance between plants, the degree of complexity, and pixel resolution, there can be significant overlap between mixed pixels, pure-canopy pixels, and pure-soil pixels, creating a challenge in identifying pure-canopy pixels. Additionally, water-stressed trees may have higher canopy temperatures and could be misidentified as mixed or soil pixels [13].

An additional group of 1-source methods incorporates statistical and spatial analyses of a single thermal image. Spatial watershed segmentation has been coupled with binary thresholding to extract pure-canopy pixels in palm trees [14] and in vineyards [15]. Camino et al. [16] incorporated watershed segmentation and quartile histogram analysis in an almond orchard. Superpixel algorithms are used to differentiate meaningful regions of interest in an image [17], such as tree crowns in a forest system [18] and fruit detection in orchards [19]. In peach orchards, one technique involved thresholding to remove noncanopy pixels and then morphological erosion to remove mixed pixels and to extract pure-canopy pixels [20]. A second method used edge detection algorithms followed by morphological dilation to remove mixed pixels [21]. Additional methods include delineation of regions of interest of a single canopy [2] as well as pure edge detection analysis [22]. The incorporation of two types of analyses, statistical and spatial, on thermal images alone has been claimed to improve the quality of canopy extraction in comparison to merely statistical-based analysis [23].

In general, multi-source methods are based on statistical analysis of a visible (RGB) or multispectral image to extract canopy pixels, which is then used as a binary mask that is superimposed on a thermal image. This technique has been implemented in crops including potato [9], mint [24], and grape [25]. Additional feature layers, such as maps of irrigation pipes, can be incorporated to improve canopy extraction [26]. However, poor overlap of RGB and thermal images can cause misidentification of canopy pixels.

Currently, there are many methods of canopy extraction, but, to the best of our knowledge, there is no comprehensive quantitative comparison of canopy extraction methods in the context of orchard water status estimation. Thus, the decision of which canopy extraction method to incorporate and how to calculate canopy temperature may be arbitrary and not based on experimental data. Accurate canopy extraction and temperature calculations are crucial to minimizing inaccuracies in thermal image-based estimation of orchard water status that may directly affect irrigation decisions. This study tested the hypothesis that thermal image-based orchard water status estimation is significantly sensitive to the canopy extraction quality and to the temperature calculation approach. The objective was to determine the sensitivity of thermal image-based orchard water status estimation to canopy extraction methodology and quality. Four canopy extraction methods were evaluated. Three methods followed the 1-source approach (thermal images), incorporating both statistical and spatial analyses: (1) 2-pixel erosion (2PE), where non-canopy pixels were removed by thresholding followed by morphological erosion; (2) edge detection (ED), where edges were identified and then morphologically dilated; and (3) vegetation segmentation (VS) using statistical analysis of the temperature histogram followed by spatial watershed segmentation. A fourth method, denoted RGB-BM, followed the multi-source approach and used an RGB image to statistically extract a binary canopy layer to mask the thermal image. Additionally, two approaches to canopy temperature calculation were assessed by calculating the following: (1) the average of 100% of canopy pixels (T100%), and the average of the coolest 33% of canopy pixels (T33%).

#### **2. Materials and Methods**

#### *2.1. Research Area*

A field experiment was conducted during the 2019 season in a 4 ha commercial lateharvest peach orchard (Prunus persica cv. 1881) located near Mishmar Hayarden, Israel (33.01◦N; 35.60◦E) (Figure 1). The elevation of the orchard ranges from 171 to 188 m above sea level, the average slope is 5% to the northwest, and within the orchard, the slope ranges from 0 to 11.3%. The orchard was planted in 2007 with spacing of 2.6 m and 5 m between trees and rows, respectively, and was divided into 22 management cells (MC) of 35 m × 35 m to monitor various orchard parameters, including canopy area and SWP. A precision drip irrigation regime was implemented in the north subplot (MC 1–11), while the south subplot (MC 12–22) was uniformly irrigated. A detailed description of the irrigation design of the entire orchard and the decision-making process in the north subplot using thermal image-based tree water status estimation following the 2PE canopy extraction method is reported in [27]. The experiment was conducted during stage III of

fruit development, which is the primary stage of fruit growth and period when most of the annual irrigation is applied.

**Figure 1.** Mishmar Hayarden peach orchard (green line) divided into 22 management cells (MC) (black dashed squares).

The major steps of data acquisition and analysis are presented as a flow chart in Figure 2.
