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Wheat and barley are crucial food resources for the global population, making their growth and monitoring essential to enhance food security worldwide. Effective observation of these crops is necessary to address production issues and mitigate the impacts of weather changes. Advancements in remote sensing technology have significantly improved the observation and estimation processes. In this study, various spectral vegetation indices were utilized, along with canopy biophysical properties (such as LAI) and biochemical properties (like chlorophyll). These properties were derived from satellite data, specifically Landsat 8 and Sentinel-2, using tools like Google Earth Engine (GEE) and the R Program. Samples of wheat and barley were collected before reaching their optimal harvest stage, and a correlation was established between the vegetation indices (e.g. NDVI, NDWI, EVI, SAVI, CMFI, SR, RVI, GRVI, and NDRI) and actual production data. Yield prediction algorithms were employed, and the results were used to generate prediction yield maps. The findings revealed a strong relationship between the vegetation indices derived from Sentinel-2 and Landsat images and the actual grain yield, with an R 2  of 0.77 and 0.71, respectively. Additionally, the study demonstrated that the most robust relationship was observed between the LAI data obtained from Sentinel-2 and cereal yield data, achieving an R 2  of 0.68. Among the indices derived from Landsat images, NDWI exhibited the highest correlation with an R 2  of 0.59. The root mean square error (RMSE) was found to be the lowest for Sentinel-2 (0.57) and Landsat 8 (1.54). Furthermore, the study indicated that the least significant relationship for grain yield prediction was observed between the NDRI index for Sentinel-2 (R 2  0.1) and the SAVI index for Landsat images (R 2  0.47).

Sentinel‐2 accurately estimated wheat yield in a semi-arid region compared with Landsat 8