The paper proposes a method for detecting fertile soils based on the processing of satellite images. As a result of its application, a map of the location of fertile and infertile soils for a given region of the earth's surface is formed and the corresponding areas are calculated. The method for detecting fertile soils is based on the fact that fertile soil includes areas covered with vegetation in the spring-summer period. Therefore, by measuring the spectral characteristics of these areas in the late autumn period, when there is no vegetation on them, it is possible to obtain objective parameters of fertile soils. For detection, a number of classifiers are being built that recognize two classes - fertile soil and sand, which is especially important when monitoring areas prone to desertification. The feature vector used for classification is a set of indices similar to the well-known NDVI index. This set of indices is calculated for each pixel of the image by its values in different spectral channels. Classifiers are implemented using CUDA parallel computing technology on a GPU. Based on the results of the experimental study, a classifier is selected that has shown the best characteristics of the recognition quality.