This study validates the accuracy of Sentinel-2 satellite leaf area index (LAI) data obtained through SNAP software for assessing vegetation and guiding agriculture. The research demonstrates the potential of utilizing Sentinel-2 satellite data processed with SNAP software for the estimation of LAI. It compares this data with infield measurements and global LAI outputs at spatial resolutions of 10 m and 20 m. The results revealed a significant level of concurrence between LAI obtained from Sentinel-2 satellite imagery and the LAI measured in the field, with coefficient of determination (R2) values of 0.81 (10 m) and 0.76 (20 m). This correlation was evidenced by lower values of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) in comparison to the LAI derived from Landsat 8 data. Significant associations were identified between LAI and various crops, with potatoes exhibiting a high correlation (R2 > 0.8) compared to peanuts (R2 > 0.75). This finding underscores the possibility of variations in LAI estimation that are specific to different crops. The research highlights the significance of rectifying atmospheric correction errors in enhancing the precision of LAI measurements. Additionally, it implies the necessity of implementing local calibration techniques to improve the resilience of the system. The results highlight the significance of utilizing the SNAP-derived LAI to monitor agriculture on a large scale, thereby making a valuable contribution to global initiatives. Although SNAP-derived LAI has some limitations, it exhibits potential for extensive agricultural monitoring applications, thereby facilitating well-informed decision-making on both regional and global levels.