Using Satellite Images the Sentinel-2 for Improving Classification of Agricultural Products via Artificial Intelligence Methods to Manage the Reservoir Dams Operation

Water is one of most important factors in the growth and development of human societies, where water resource limitations have always been one of the main barriers to agricultural development as a major basis for achieving food self-sufficiency. One of the main applications of satellite imagery is its utilization in the field of water resources management and agricultural activities, in which managers can benefit from it for studying cultivation levels, crop classification, crop estimation, and agricultural crisis forecasting. Generally, overall consumption estimation, water/irrigation management, and utilization of dams’ storage capacity are among the most important research topics. This study benefits from the Sentinel-2 satellite for classifying the agricultural crops based on the multi-temporal methods. Besides, four classification methods are adopted for classifying, namely, minimum distance, maximum likelihood, fuzzy, and neural network. Due to the spectral changes of goods during the growing period, using the multi-temporal methods based on the crop calendar can play a decisive role in classifying process, such that the classification accuracy increases to 86 percent via the maximum likelihood and neural network methods. Moreover, the normalized Kappa increased to 90.5 percent, when the neural network method parameters are optimized. The results obtained from the simulation indicate that genetic algorithm is the best method for obtaining the optimal results. After selecting the optimized neural network parameters, the classification has been taken into account and observed that Alfalfa has the largest crop surface, while it requires a considerable amount of water and its demand is in a lower value.