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This study is devoted to the use of varied analytical methods to elucidate the complex relationship between normalized difference vegetation index (NDVI) and wheat production in Taounate, Morocco based on MODIS Satellite data. Linear regression (LR), with a coefficient of determination (R²) of 0.93, provided a solid basis, while the decision tree (DT) showed significant performance with an R² of 0.81. Support vector regression (SVR) performed well with an R² of 0.96, highlighting its ability to capture the nonlinear nuances of the data. Given the complexity inherent in the observed relationships, characterized by non-linear variations, we opted for a combined approach. K-means, closely linked to SVR, was integrated for its ability to identify homogeneous subgroups in the data (R2 up to 0.98). This combination made it possible to circumvent the limits of strictly linear methods, thus reinforcing the robustness of our analysis. These results underline the capacity of the chosen methodology to decode the interactions between NDVI and wheat production in the complex context of Taounate. By providing clear and nuanced perspectives, this study helps inform agricultural decisions and build resilience to climate challenges in the region. [ABSTRACT FROM AUTHOR]

Context and background Wheat is one of the oldest cultivated plants in the world and has always been one of the most important staples for millions of people around the world and especially in North Africa, where wheat is the most used crop for typical food industry. Thus, an operational crop production system is needed to help decision makers make early estimates of potential food availability Yield estimation using remote sensing data has been widely studied, but such information is generally scarce in arid and semi-arid regions such as North Africa, where interannual variations in climatic factors, and spatial variability in particular, are major risks to food security. Goal and Objectives: The aim of this study is to develop a model to estimate wheat yield based on phenological metrics derived from SENTINEL-2 NDVI images in order to generalize a spatial model to estimate wheat yields in Morocco's semi-arid conditions Methodology: The 10 m NDVI time series was integrated into TIMESAT software to extract wheat phenology-related metrics during the 2018-2019 agricultural season, the period in which ground truth data was collected. Through the multiple stepwise regression method, all phenological metrics were used to predict wheat yield. Moreover, the accuracy and stability of produced models were evaluated using a K-fold cross-validation (K-fold CV) method. Results: The results of the obtained models indicated a good linear correlation between predicted yield and field observations (R2 = 0.75 and RMSE of 7.08q/ha). The obtained method could be a good tool for decision makers to orient their actions under different climatic conditions

Context and background Wheat is one of the oldest cultivated plants in the world and has always been one of the most important staples for millions of people around the world and, especially in North Africa, and especially in Morocco where wheat occupies an important place in the agricultural sector. Thus, an operational crop production system is needed to help decision makers make early estimates of potential food. availability Yield estimation using remote sensing data has been widely studied, but such information is generally scarce in arid and semi-arid regions such as North Africa, where interannual variations in climatic factors, and spatial variability in particular, are major risks to food security. Goal and Objectives: The aim of this study is to develop a model to estimate wheat yield based on phenological metrics derived from SENTINEL-2 NDVI images in order to generalize a spatial model to estimate wheat yields in Morocco's semi-arid conditions. Methodology: The 10 m NDVI time series was integrated into TIMESAT software to extract wheat phenology-related metrics during the 2018-2019 agricultural season, the period in which ground truth data was collected. Through the multiple stepwise regression method, all phenological metrics were used to predict wheat yield. Moreover, the accuracy and stability of produced models were evaluated using a Kfold cross-validation (K-fold CV) method. Results: The results of the obtained models indicated a good linear correlation between predicted yield and field observations (R2 = 0.75 and RMSE of 7.08q/ha). The obtained method could be a good tool for decision makers to orient their actions under different climatic conditions. [ABSTRACT FROM AUTHOR]