Sentinel-2 Global Land Cover

This project will focus on the classification of Sentinel imagery for the purpose of producing a global land cover map. The first part of this study is an extensive review of the currently available Global Land Cover (GLC) maps and databases. This review, together with feedback from the community, will influence the choices in algorithms and image processing methodologies tested within the scope of this study. The second and third parts of the study are testing of the land-cover classification methodologies and validation of those methods respectively in order to produce not only the highest quality maps, e.g. accuracy >80%, but also harmonised with current GLC products. In order to achieve this complex goal, many different tests of object-oriented as well as pixel based classification approaches will be made. In parallel, advanced data collection strategies for training and validation will be investigated. While the majority of the applied land-cover classification techniques will be based on optical imagery acquired by Sentinel-2 (S2), the team understands that globally this challenge can be supported by the Sentinel-1 SAR data. The different approaches will be benchmarked in order to understand the influence of a variety of factors on the performance of the proposed methods. Factors will include feature relevancy, the impact of atmospheric correction, the selected minimal mapping unit, seasonal changes, the incompleteness of training data, image mosaicking, and multi-temporal S2 data. The final part of the project will be to make recommendations based on the research for future S2 based GLC products.