SENTINEL 2 ХИЙМЭЛ ДАГУУЛ БОЛОН ДРОНЫ ЗУРАГ ...

During this period when the agricultural sector is developing rapidly in Mongolia, it is necessary to create a sustainable development of agricultural production, and for this, there is a need to improve agricultural technology. Also nowadays, remote sensing and geographic information systems are still looking for suitable methods to study this field. In conducting this research, based on Sentinel 2A satellite data and unmanned aerial vehicle (UAV) data, using maximum likelihood (ML) and spectral angle mapper (SAM) methods, the results of classifying farmland by crop type were compared with field measurement GPS points to determine the accuracy of farmland classification. According to the results, the classification accuracy of the ML method was similar, while the classification accuracy of the SAM method was very different. This led to misclassification in some cases due to plant growth stage and whether the plant was healthy or unhealthy. Considering the result of the accuracy, it is observed that the plant growth period is highly dependent on the spectral classification of farmland, and the more complete the plant growth, the better the classification can be a drone.