A CROSS-CORRELATION PHENOLOGY-BASED CROP FIELDS CLASSIFICATION USING SENTINEL-2 TIME-SERIES

Agricultural areas are naturally affected by significant variations within relatively short time intervals, in accordance with the growing season. These dynamics could, in principle, be exploited to classify different types of crops. Thus, this study aims to investigate methodologies and results of crop type classification making use of phenological information extracted from high spatial resolution satellite imagery. Vegetation indices (VI) retrieved from Sentinel-2 imagery are evaluated to track the year-round vegetation behavior. Starting from a multi-temporal image series of the same scene, the phenological profiles can be extracted and introduced into a supervised classification process to detect crop fields, discriminating among different species. Following this, we propose a cross-correlation based model that, using a priori information from ground training data, searches for the best matching phenology. When compared to machine learning models for crop classification, the one proposed in this study can provide useful information about phenology that can be stored and used for better monitoring spatio-temporal variations of crops species through the future years and guiding agricultural management accordingly. Our case studies are the regions of Bothaville and Harrismith, located in South Africa, and the region of Jendouba in Tunisia. The results for the Bothaville region show 89.19% of user accuracy on the main crop type classification (maize crops). For beans and sorghum, the confusion matrix attests 93% and 74% of accuracy respectively, even if their statistics are less significant due to the limited number of available ground data for secondary crops.