## 2 Materials and Methods  
This systematic review was conducted based on the PRISMA protocol, an important framework that provides a standardized approach for conducting and presenting comprehensive analyzes. This protocol was developed to improve the transparency and quality of systematic reviews and meta-analyzes. It consists of a checklist that guides researchers through each stage of the review process. One of the key procedures involved a rigorous process to identify and select relevant records. The search, conducted on 12 December 2023, included two extensive bibliographic databases, Scopus, and Web of Science (WOS). For Scopus, the search query was formulated to capture relevant manuscripts:  
_TITLE-ABS-KEY (\"remote sensing\" OR \"uav\" OR \"unmanned aerial vehicles\" OR \"satellite\" OR \"landsat\" OR \"sentinel\" OR \"MODIS\" OR airborne OR aircraft OR drone OR multispectral OR hyperspectral OR \"thermal infrared\" OR \"land surface temperature\" OR \"RADAR\" OR \"LIDAR\" AND TITLE-ABS-KEY (almond\" OR \"prunus dulcis\")._  
Simultaneously, WOS was queried using the following terms:  
_(T1 = (\"remote sensing\" OR \"uav\" OR \"unmanned aerial vehicles\" OR \"satellite\" OR \"landsat\" OR \"sentinel\" OR \"MODIS\" OR airborne OR aircraft OR drone OR multispectral OR hyperspectral OR \"thermal infrared\" OR \"land surface temperature\" OR \"RADAR\" OR \"LIDAR\") AND TIT = (almond\" OR \"prunus dulcis\")) OR (AB = (\"remote sensing\" OR \"uav\" OR \"unmanned aerial vehicles\" OR \"satellite\" OR \"landsat\" OR \"sentinel\" OR \"MODIS\" OR airborne OR aircraft OR drone OR multispectral OR hyperspectral OR \"thermal infrared\" OR \"land surface temperature\" OR \"RADAR\" OR \"LIDAR\") AND AK = (almond\" OR \"prunus dulcis\"))._  
The combination of both databases resulted in a total of 298 manuscripts: 154 from Scopus and 144 from WOS. Subsequent removal of duplicates identified within and between databases led to 187 unique manuscripts (111 duplicates). In this process, a title and abstract similarity analysis were applied due to small variations. Following this, a screening process excluded non-English-language manuscripts and research reviews, resulting in the consideration of 74 manuscripts (Figure 1). Additionally, a search in another database, Google Scholar, yielded eight studies deemed relevant. To systematically categorize and analyze the identified manuscripts, a second filter was applied, resulting in six distinct RS applications for almond crops. These applications, identified by specific keywords, are as follows (Figure 1):  
1. Tree Segmentation and Parameter Extraction (TSPE): Studies focusing on segmenting almond trees and extracting key parameters, including keywords like tree segmentation, parameter extraction, number of trees, tree height, and canopy area. 2. Imagery Classification (IC): Research centered around the accurate classification of RS imagery related to almond orchards, incorporating keywords like cultivars, classification models, machine learning, and deep learning. 3. Health Monitoring and Disease Detection (HMDD): Studies concentrating on monitoring tree well-being and early disease detection using RS methods, with keywords related to health, pests, disease detection, and pesticides. 4. Water Management (WM): Research related to effective water management strategies for almond orchards employing RS techniques, incorporating keywords like water stress, crop water stress index (CWSI), evapotranspiration, and irrigation. 5. And Other (OTR). This category contains studies on several topics that were not included in the previous RS applications. Upon the examination of the keywords extracted from the manuscript search, significant terms such as remote sensing, almond, Central Valley, evapotranspiration, and the Surface Energy Balance Algorithm for Land (SEBAL) (Figure 2). In addition, terms related to satellites (e.g., Sentinel-2) and sensors (LiDAR, Hyperspectral) were identified.