# Remote Sensing Technologies for Monitoring Argane Forest Stands: A Comprehensive Review
## 4 Remote Sensing Applications
### Productivity Monitoring  
Photosynthesis serves as the primary source for crop yield formation, yet only a fraction of sunlight is harnessed through leaf photosynthesis [138]. To describe a plant's capacity to absorb atmospheric CO2 via photosynthesis, plants convert light into chemical energy and accumulate organic dry matter, introducing the concept of vegetation productivity. Vegetation productivity comprises two key components: gross primary productivity (GPP) and net primary productivity (NPP) [138]. GPP quantifies the total organic carbon fixed by green plants through photosynthesis over a specific time and area unit, while NPP represents the portion of GPP remaining after accounting for plant autotrophic respiration, which consumes organic matter. As NPP and GPP are closely linked to crop biomass, they are frequently employed in studies related to crop yield estimation [138]. Crop yield estimation via remote sensing methods may be categorized into three primary approaches: statistical analysis of remote sensing data, production efficiency models, and crop growth models [138]. The first approach involves the development of relationship models between remote sensing data, such as different band combinations or various remote sensing indices, along with crop yield components [139]. The second approach, based on production efficiency models, posits that crop yields under non-stressed conditions exhibit a linear correlation with the absorbed photosynthetically active radiation. This approach first estimates aboveground crop dry matter using remote sensing data and subsequently transforms it into crop yield [140; 141]. The third method involves using satellite data to calibrate physiologically based crop simulations, emulating real crop growth dynamics to ultimately estimate crop yield [142; 143]. Recently, machine learning models have demonstrated significant promise in retrieving biophysical and physiological parameters, particularly in crop yield prediction from remote sensing data. In the field of machine learning, the Linear Regression model establishes relationships among independent variables and multiple dependent variables [144] in estimating vegetation parameters [23]. Proficiency in a machine learning framework is achieved by utilizing datasets and reducing error or loss (such as root mean square error (RMSE), mean absolute error (MAE), or mean square error (MSE)) through regression algorithms [145]. Various parameters such as precipitation, temperature, soil conditions, and vegetation health are utilized in crop yield prediction through the application of machine learning [146]. Figures 4 and 5 illustrate prediction algorithms used in crop yield forecasting across several studies and a spectrum of performance evaluation metrics utilized in prior crop yield prediction research, respectively, as cited by [145]. It is noted that random forest (RF), artificial neural networks (ANN), and support vector regression (SVR) are highlighted as among the most commonly used methods for crop yield prediction [145]. Among these machine learning models, the random forest (RF) algorithm emerges as a particularly robust tool for yield prediction, with widespread applications in agricultural research [146; 147]. Random forest generates a multitude of regression trees derived from an extensive set of decision trees, enabling accurate regression computations and offering resilience against overfitting and noise [145; 148; 149]. Numerous studies have demonstrated the superior performance of random forest in crop yield prediction. For instance, [PERSON] et al. [146] used random forest to predict cotton yield, achieving promising results by utilizing various predictor variables such as land surface temperature (LST), Standardized Precipitation Index (SPI), Vegetation Condition Index (VCI), and historical yield data.