# Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction
## 4 Discussion  
The present study aimed to develop ML models to simulate almond yields in the TM region, applying open and proprietary RS data. The comparison was made among various free RS platforms (MODIS Terra LST, GSMaP, Landsat 8, and Sentinel-2) and a paid one (PlanetScope). Several ML regression models (RFR, XGBR, GBR, BR, ABR) were applied, and the optimum feature combination was selected to achieve the best performance. The combination of irrigation data, daytime temperature in March, the NDVI in January, and the SAVI in May (from the PlanetScope platform) showed the best performance (\(R^{2}\) = 0.80), using the XGBR model. Indeed, the use of VIs with a higher resolution (3 m) from the PlanetScope data had a positive influence on the almond yield prediction, as the results obtained with Sentinel-2 (\(R^{2}\) = 0.67--RFR) and Landsat 8 (\(R^{2}\) = 0.72--ABR) data were lower. However, it is worth noting that free data with lower resolution could also be a viable alternative to PlanetScope, particularly RS platforms providing climatic data, such as MODIS Terra LST and GSMaP, achieving an \(R^{2}\) of 0.68 when using the RFR model with  
Figure 5: Correlation charts on the yield with: (**a**) irrigation; (**b**) NDVI in January; (**c**) SAVI in May; and (**d**) daytime temperature in March, Celsius degrees (\({}^{\circ}\)C). irrigation and climate data. Regarding the XGB model, it was observed that the best results were reached when using three groups of features (irrigation, climate data, and VIS) (\(R^{2}\) = 0.80). On the other hand, the XGB model obtained inferior performance when using only irrigation information (\(R^{2}\): 0.32), only climate data (\(R^{2}\): 0.19), or only VIS (PlanetScope--\(R^{2}\) = 0.44). In this case, the RFR model achieved a higher level of performance than the XGB model. This situation might be due to the XGB model being more capable of handling complex relationships between features [45], while the RFR model is known to perform better with simpler features. Considering other studies related to almond yield prediction, [PERSON] et al. tested several ML models, obtaining the best performance with the XGB model, with an \(R^{2}\) of 0.71, which is also a boosting model [20]. Considering the most important features, irrigation and daytime temperature in March stood out, highlighting the role of water availability and suitable temperatures for almond yields [53]. In fact, AG3 and AG4 show higher yield values, due to the available irrigation. Furthermore, March is considered a crucial period for almond trees, as flowering occurs at this stage [54]. Almond trees are highly sensitive to climatic conditions during the flowering period, and adequate temperatures are essential for successful pollination and fruit development. According to [PERSON] [54], the ideal temperature for almond tree flowering during the day is between 15 \({}^{\circ}\)C and 30 \({}^{\circ}\)C, and temperatures outside this range can lead to problems, resulting in reduced fruit production. In effect, daytime temperatures in March in the agricultural fields of AG2 were recorded, with a minimum temperature of 12.2 \({}^{\circ}\)C recorded in 2018, which is below the considered ideal temperature for almond flowering, which can explain the low production in that year (Supplementary Figure S1). On the other hand, in the agricultural fields of AG4, a maximum temperature of 22.5 \({}^{\circ}\)C was recorded in 2019, falling within the range of ideal temperatures for flowering, resulting in increased production that year. Similar studies have also highlighted temperature-related features. [PERSON] et al. [20] emphasized the importance of the feature \"long-term mean maximum April-June temperature\" in predicting almond production. According to the authors, this factor significantly affects the blooming period of almond trees. Almonds are sensitive to temperature fluctuations during this critical stage, and optimal temperatures promote successful pollination and higher yield.