In some regions, mainly occupied by agriculture and cattle breeding, irreversible soil changes, e.g. desertification, have appeared, which can lead to serious environmental and economic problems. This paper considers the application of neural networks for prediction and assessment of desertification-prone lands using satellite images. An autoencoder type of the neural network is applied for these purposes. Datasets were generated for training from the Sentinel-2 satellite open database. The first network was used for prediction. The second network is responsible for segmentation of the image into classes using NDVI index. In this paper we explain the method, the architecture of the network and present some experimental results. The presented method allows making a qualitative and quantitative assessment of possible changes, which can be useful for planning preventive works.