Urban surface material composition from spaceborne imaging spectroscopy data

Abstract

The material composition of urban areas is important for urban climate modeling and urban canopy models. Since 2018, a few spaceborne imaging spectrometers have been launched such as the DESIS instrument on the ISS and the Italian PRISMA. The spatial resolution of these systems is around 30 m, which is in many cases not sufficient for urban applications. However, the spectral resolution ranges between few nanometers and thus, offers the potential to detect surface materials on a sub-pixel level. Common approaches for sub-pixel analyses such as spectral mixture analyses (SMA) mostly fail given the high spatial and spectral complexity of urban areas. A possible new way to interpret complex spectral mixtures in urban areas is to use the gradient concept, which so far has been applied to map species compositions in natural ecosystems. This study analyses whether the gradient concept offers a feasible solution for the analysis of urban data sets with a high amount of spectral mixtures. The presented strategy presumes that the composition of material mixtures changes gradually across urban spaces. This study focuses on 1) the adaptation of gradient analysis from vegetation ecology, 2) the mapping of these material gradients using simulated EnMAP data of Munich, Germany, to identify characteristic material compositions and 3) an initial test to transfer the gained results to areas without knowledge about material compositions. The achieved insights of this study build an important fundament for future analyses focusing on the transferability of the gradient concept to unknown urban areas.