## 3 Results and Discussion
### Remote Sensing Platforms and Sensors  
The distribution of RS platforms and sensors significantly influences the methodology of almond crop studies. Our analysis reveals discernible patterns in the utilization of RS platforms and sensors, providing insights into prevailing preferences within the research community. Considering the most commonly used RS platforms, satellite was the most prevalent, being employed in 46% of the studies. The second most used platform was unmanned aerial vehicles (UAV) at 32%. Finally, the manned aerial vehicle (MAV) was the least used RS platform, representing 22% of the studies analyzed (Figure 5(a)). Among the sensors, the red-green-blue (RGB) sensor emerged as the most widely used, finding application in 40% of the studies analyzed. Multispectral (MSP) and thermal infrared (TIR) sensors were also widely embraced, featuring in 30% and 20% of the studies, respectively. In contrast, hyperspectral (HSP) and LiDAR (LDR) sensors were used less frequently, with only 7% and 3% of studies opting for these technologies, respectively (Figure 5(b)). The predominant use of satellites and UAVs combined with RGB and MSP sensors in RS studies is justified by their clear advantages. Satellites offer global coverage, high revisit times and long operating times, making them suitable for monitoring large areas. UAVs offer flexibility in terms of flight altitude, spatial resolution, and revisit time to meet specific study requirements and enable targeted observations. RGB sensors capture true-color imagery for visual interpretation, while MSP sensors acquire data in several bands, which is particularly beneficial for applications that depend on VIs [17]. In examining the predominant platforms concerning the type of RS application, it is evident that studies using satellite data are predominantly associated with the WM appli  
Figure 5: Percentage distribution of studies by country based on their respective remote sensing applications. TSPE: Tree Segmentation and Parameters Extraction; IC: Imagery Classification; HMDD: Health Monitoring and Disease Detection; WM: Water Management; and OTR: Other Applications. Figure 6: Remote Sensing platforms (**a**) and sensors (**b**) used in the reviewed studies. SAT: satellite; UAV: unmanned aerial vehicle; MAV: manned aerial vehicle; RGB: red, green, blue; MSP: multispectral; TIR: thermal infrared, HSP: hyperspectral; and LDR: LiDAR. cation, constituting 26% of the studies using this platform. The IC application follows with a lower percentage, accounting for 9% of the studies. Conversely, studies focused on the TSPE and HMDD applications exhibit remarkably low utilization of satellites, representing 2% and 1% of the use of this platform, respectively. The TSPE application demands higher resolution images crucial for tree-level segmentation and parameter extraction, rendering the use of satellites less prevalent. Among studies using UAVs, those associated with TSPE application predominate at 11%, followed by IC and WM with 6% each. In contrast, applications with minimal UAV use include OTR applications, namely yield prediction (YP) with 2%. Studies related to YP application often rely on time-series data spanning a more extended period, posing challenges in obtaining such data through UAVs, which are more aptly acquired via satellite data. Among studies using MAV platforms, those related to WM applications stand out at 9%, followed by HMDD applications at 4% (Figure 7a). In terms of the most implemented sensors, several application types generally make use of RGB sensors. However, for more specific requirements, such as almond cultivar classification, water stress assessment, or disease detection, additional sensor types may be necessary. In the analyzed studies, the employment of TIR sensors in the WM application (15%) reveals a crucial role in estimating indices associated with water stress [18].