## 3 Results and Discussion
### Remote Sensing Applications
#### 3.4.5 Other Applications
In [PERSON] et al. (2010), a methodology was developed to map crop calendar events and phenology-related metrics using RS data in the Castilla-La Mancha region, Spain. OBIA techniques are employed. The approach involved three key steps: (1) generation of crop masks, (2) extraction of crop calendar events, and (3) calculation of phenology-related metrics. Validation using real data confirmed the method's reliability in providing accurate estimates of harvest calendar events and phenology-related metrics at the regional scale, showcasing its potential for crop monitoring and yield estimation. Finally, [PERSON] et al. (2011) integrated both satellite and UAV data, addressing the challenges associated with quantifying floral phenology using traditional methods. The authors explored two primary categories of RS methods: classification-based and index-based. The results revealed that the enhanced bloom index (EBI) outperformed other indices in terms of accuracy and sensitivity. The study concluded that EBI represents a valuable tool for monitoring and quantifying spatio-temporal variations in flowering status. Among the studies related to yield prediction using satellite platforms, two employed ML models. [PERSON] et al. (2012) used various models, including stochastic gradient boosting (SGB), to forecast almond orchard yields in the California Central Valley. RS data served as crucial features for model performance, revealing a robust correlation between estimated and actual yields at the orchard level, with an average \(R^{2}\) of 0.71 for predictions in March and June. Factors influencing yields included higher temperatures from April to June benefiting southern orchards and increased March rainfall reducing yields, especially in northern orchards. [PERSON] et al. (2013) focused on improving agricultural management and economic analysis by studying the age distribution of tree crops. Using high-resolution satellite imagery and a RF model, they achieved an 87% OA in mapping tree crop planting years in the California Central Valley. This information proved vital for decision-making in agricultural management, water resource planning, and predicting agricultural product supply and demand. In the realm of satellite-based studies for yield prediction, one employed DL models. [PERSON] et al. (2014) used a data-driven approach with computer vision to predict early almond yield and promote sustainable agriculture. Over three years, they mapped bloom density in almond orchards using digital images. Model accuracy evaluation (precision of 0.76 and recall of 0.71) revealed a significant correlation with manually determined bloom density, offering insights for sustainable agriculture, cost reduction, and optimization of almond yield and quality by minimizing soil and water contamination. In studies leveraging MAV platforms for yield prediction, two approaches were identified--one based on the Linear Regression (LR) model and the other on a combination of ML and DL models. [PERSON] et al. (2015) investigated the effectiveness of CWSI in monitoring almond tree transpiration and water status. They established a method to estimate crop yield based on the correlation between canopy temperature and transpiration. The study demonstrated a strong seasonal correlation between CWSI and final yield (\(R^{2}\) = 0.80) using a non-water stress baseline (NWSB) established over three years. [PERSON] et al. (2016) explored RS technologies for yield estimation in almond tree crops at the field scale. Traditional and ML methods, including Random Forest Regression (RFR), Gradient Boosting Trees for Regression (GBTR), and XGBoost models, were developed, incorporating Landsat VIs and weather data. The study also introduced sophisticated DL models (DNN, CNN, and RNN) to enhance yield estimation with extensive RS datasets. Texture features, when added to the RF and XGBoost models, improved their ability to explain variations in almond yield.