Extending the vegetation-impervious-soil model using simulated EnMAP data and machine learning

摘要

The upcoming hyperspectral satellite mission Environmental Mapping and Analysis Program (EnMAP) will provide timely and globally sampled imaging spectrometer data on a frequent basis. This will create unprecedented opportunities for a variety of environmental research fields and lead to manifold novel applications. These opportunities specifically apply to challenging environments, including heterogeneous urban landscapes. In this paper, we explored the potential of EnMAP data for quantifying land cover along the urban-rural gradient of Berlin, Germany. Land cover fraction maps from a simulated EnMAP scene at 30 m spatial resolution were derived based on support vector regression (SVR) combined with synthetically mixed training data. Results demonstrate that EnMAP imagery will be well suited for mapping impervious, vegetation and soil surface types according to the VIS framework. Moreover, EnMAP data will allow extending the VIS framework by more detailed sub-categories such as roof and pavement, or low vegetation and tree. However, we advise caution that spaceborne imaging spectrometer data of improved quality will not completely help to overcome well known phenomena of spectral similarity between materials and spectral confusion caused by the presence of shaded areas. To identify possible benefits and limitations of EnMAP data, comparisons to fraction maps derived from a higher resolution Hyperspectral Mapper (HyMap) image at 9 m spatial resolution and a multispectral Landsat ETM +-like image at 30 m spatial resolution were drawn. First, we demonstrate that both VIS and extended VIS mapping reveal similar accuracies compared to maps from spatially higher resolution data. Second, we illustrate the superiority of the higher spectral information content for improved and extended urban land cover mapping compared to multispectral data. Overall, this study provides important insights into the potential of spaceborne imaging spectrometer and specifically future EnMAP data for urban remote sensing. (C) 2014 Elsevier Inc All rights reserved.