Monitoring seasonal effects in vegetation areas with Sentinel-1 SAR and Sentinel-2 optic satellite images

Classification for land cover mapping is of great importance for accurate analysis and temporal monitoring of natural resources. In this study, the classification process was carried out using four synthetic aperture radar (SAR) and optical satellite images obtained in different seasons at equal intervals within a year. In addition to combining optical and SAR data for classification, single optical and SAR images have been classified separately. Thus, the effect of combining SAR and optical images on classification accuracy was examined. Moreover, the normalized difference vegetation index (NDVI), which is a vegetation index, was added to the image data, and the seasonal effect on accuracy was examined for the region with dense vegetation. In classification, three different object-oriented classification algorithms, support vector machines (SVM), random forest algorithm (RF), and k-nearest neighbors algorithm (kNN), were used. Finally, the number of training samples used for classification was increased, and its effect on accuracy was revealed in the study. The lowest overall classification accuracy was found to be 40.46% with classification using single SAR images, while the highest classification accuracy was found to be 95.12% as a result of the classification of the image obtained by combined SAR and optical satellite images. Furthermore, an additional testing area was considered to validate the method, and consistent results were obtained in that area as well. As a result, monitoring of the natural resources with high accuracy has been discussed, considering the data sources, machine learning methods, and the seasonal effects.