## 3 Results and Discussion
### Remote Sensing Applications
#### 3.4.5 Other Applications  
In studies exploring diverse applications, [PERSON] et al. [102] adeptly gathered geospatial data for agriculture, mapping promising and degraded areas, and economic planning. The outcomes revealed areas suitable for almond production (10.4%). In investigations related to nitrogen assessment, [PERSON] et al. conducted several studies [103; 104; 105; 106]. The first study examined the feasibility of using DESIS imagery from the International Space Station to estimate leaf nitrogen content in almond orchards [103]. Using a radiative transfer model and solar-induced fluorescence data, the study demonstrated that coupled Cab and SIF predicted 90% of leaf nitrogen variability, showcasing the potential for large-scale leaf nitrogen quantification crucial for precision agriculture. In a second study, the use of solar-induced fluorescence (SIF) was explored as a non-destructive indicator for monitoring crop nitrogen status [104]. Employing MAV imaging spectroscopy and modeling methods, the study found a significant relationship between SIF and leaf nitrogen concentration, suggesting SIF's potential as a cost-effective and timely tool for assessing plant health and nitrogen status over large areas. In a third study, the use of MAV RS data to estimate leaf nitrogen in almond orchards is evaluated, employing ML algorithms with input parameters like plant traits, MAV-quantified solar-induced SIF, and CWSI [105]. The study identified MAV-quantified Cab and SIF as the most influential spectral plant traits for predicting leaf nitrogen, emphasizing the significance of using multiple plant traits to enhance prediction model accuracy. In the fourth study, the authors examined the role of leaf Cx in quantifying leaf nitrogen using Fluspect and MAV imaging spectroscopy in almond orchards for optimized fertilizer applications [106]. By employing SIF and chlorophyll a + b content (Cab), the authors demonstrated the superiority of this method over standard VIs, with leaf Cx ranking third after Cab and SIF consistently over two growing seasons. [PERSON] and [PERSON] [107] assessed dust-reducing strategies using aerial-based sensors, specifically the Drone-Based Particulate Matter Sensor (DPMS). Their findings revealed that employing low-dust harvesters and optimizing fan speed significantly reduced PM10 emissions, while water application to the orchard floor showed no significant effect. The study also underscores the effectiveness of the DPMS in evaluating and informing strategies for mitigating particulate matter emissions during almond harvesting. [PERSON] and [PERSON] [108] aimed to quantify directional effects of solar radiation on canopy spectral reflectance, presenting results that highlighted the impact of sun-view geometry on reflectance across different spectral bands. The study outcomes, including significant variations in reflectance and RMSD values, are anticipated to enhance the reliability and repeatability of UAS-based RS analysis. Regarding studies related to phenology, [PERSON] et al. [109] applied satellite data to monitor phenological changes across three diverse locations--an almond orchard in California, a winter wheat area in China, and a northern hardwood forest in New Hampshire. The authors used the MODIS 500 m reflectance anisotropy product bidirectional reflectance (BR)  
Figure 12: Remote sensing platforms (**a**) implemented in water management studies and methods primary employed (**b**) NDV: Normalized Difference Vegetation Index; CWSI: Crop Water Stress Index; METRIC: Mapping Evaporotranspiration at High Resolution with Internal Calibration; S-SEBI: Simplified-Surface Energy Balance Index Algorithm; and TSEB: Two-Source Energy Balance. factor to assess the performance of the MODIS BRDF daily product in estimating key phenological parameters at these sites. The results demonstrated a robust correlation between DB phenology parameters and ground-based observations, underscoring the effectiveness of the MODIS DB BRDF product for monitoring and modeling ecosystem phenology.