Egypt, a country with harsh natural environment and rapid population growth, has being plagued by the problem of food security. Application of remote sensing techniques, the original model for assessing the food security in Egypt is presented. By extracting the gray-level co-occurrence matrix (GLCM) mean texture features, arable land used to grow grain crops was first classified and extracted using Support Vector Machine. In terms of the classified results, meteorological data, and NDVI data, the CASA model was adopted to compute the annual net primary production (NPP), and then the NPP-yield conversion formula was used to forecast the annual grain yield. Finally, the evaluation method of food security, which involves four dimensions, i.e., quantity security, economic security, quality security, and resources security, was established to evaluate the food security in Egypt in 2010, 2015, and 2020. Comparing estimated results with statistics data provided by FAO, the classification accuracy of the crop distribution map is above 82%, and the average error of yield estimation is within 5%. Evaluation results show that the food security in Egypt is declining, the quantity and quality security show a large fluctuation, and economic and resources security are relatively stable. This model can satisfy the requirements of estimating the grain yield at a large scale and evaluating the food security on a national level, and it can be used to provide some useful suggestions for government to improve the food security
