Mapping urban-rural gradients of settlements and vegetation at national scale using Sentinel-2 spectral-temporal metrics and regression-based unmixing with synthetic training data
Franz Schug, David Frantz, Akpona Okujeni, Sebastian van der Linden, Patrick Hostert
Abstract
(0.14, 0.19) are highly consistent across Germany and Austria. Only a few surface types were not accurately predicted in our nation-wide mapping. Further research is required to optimize mapping of temporally invariant bare soil and rock surfaces that show spectral similarity to built-up surfaces and infrastructure. The proposed methodology combines benefits of both regression-based modelling with synthetically mixed training data and STM, and thus facilitates mapping of LC fractions on a national scale and at high resolution. Such information will allow to better characterize settlements and identifying processes such as densification that are best represented by continuous LC mapping.