Exploring the synergy of EnMAP hyperspectral imagery with Machine Learning for land use- land cover mapping in a Mediterranean setting

The Environmental Mapping and Analysis Program (EnMAP), is a new spaceborne German hyperspectral satellite mission for monitoring and characterizing the Earth’s environment on a global scale. EnMAP mission supports the retrieval of high-quality and abundant detailed spectral information in VNIR and SWIR ranges within a large-scale area in wide temporal coverage and high spatial resolution. Taking advantage of high-quality data freely available to the scientific community great potential is revealed in a wide range of ecological and environmental applications, such as i.e. accurate and up-to-date LULC thematic maps.

The objectives of the present study are to explore the accuracy of EnMAP in land cover mapping over a heterogeneous landscape. As a case study is used a typical Mediterranean setting located in Greece. The methodology is based on the synergistic use of machine learning techniques and ENMAP imagery coupled with other ancillary data and was carried out in EnMAP Box-3, a toolbox designed within a GIS open-source software. Validation of the derived LULC maps has been carried out using the standard error matrix approach and also via comparisons versus existing LULC operational products.

To our knowledge, this research is one of the first to explore the advantages of the hyperspectral EnMAP satellite mission in the context of LULC mapping. Results of the present study are expected to provide valuable input for applications of LULC mapping and demonstrate the potential of hyperspectral EnMAP data for improved performance and the highest accuracy of LULC mapping.

 

KEYWORDS: EnMAP, Land cover, Land use, Hyperspectral remote sensing, Machine Learning