Preface: the environmental mapping and analysis program (EnMAP) mission: preparing for its scientific exploitation

Open access; distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) licenseThe imaging spectroscopy mission EnMAP aims to assess the state and evolution of terrestrialandaquaticecosystems,examinethemultifacetedimpactsofhumanactivities,andsupport a sustainable use of natural resources. Once in operation (scheduled to launch in 2019), EnMAP will provide high-quality observations in the visible to near-infrared and shortwave-infrared spectral range. The scientific preparation of the mission comprises an extensive science program. This special issue presents a collection of research articles, demonstrating the potential of EnMAP for various applications along with overview articles on the mission and software tools developed within its scientific preparation.Ye

Assessment of Soil erosion parameters in Costa Rica using reflectance hyperspectral and simulated EnMAP imagery

Soil erosion can be linked to relative fractional cover of photosynthetic-active vegetation, non-photosynthetic-active vegetation and bare soil, and terrain characteristics such as the C and LS factors. This study investigates the capacity of EnMAP imagery to map areas prone to soil erosion in a region near San Jose, Costa Rica, characterized by spatially extensive coffee plantations and grazing in a mountainous terrain. Simulated EnMAP imagery is based on airborne hyperspectral HyMap data. Fractional cover estimates are derived using a Multiple End-member Spectral Mixture Analysis approach. The C and LS factors are derived from a digital elevation model. The analysis was done independently for EnMAP and HyMap, the latter treated as reference data as no ground truth is available. Results demonstrate that with EnMAP imagery it is possible to extract quality endmember classes with important spectral features related to PV, NPV and soil, and be able to estimate relative cover fractions. This spectral information is critical to separate soil and NPV which greatly can impact integrated modelling with terrain characteristics. From a regional perspective EnMAP can be used to highlight specific areas that may be prone to erosion where this information can be extract directly from the imagery using automated processes

Estimation of biomass potential based on classification and height information

On the way to make energy supply independent from fossil resources more and more renewable energy sources have to be explored. Biomass has become an important energy resource during the last years and the consumption is rising steadily. Common sources of biomass are agricultural production and forestry but the production of these sources is stagnating due to limited space. To explore new sources of biomass like in the field of landscape conservation the location and available amount of biomass is unknown. Normally, there are no reliable data sources to give information about the objects of interest such as hedges, vegetation along streets, railways and rivers, field margins and ruderal sites. There is a great demand for an inventory of these biomass sources which could be answered by applying remote sensing technology. As biomass objects considered here are sometimes only a few meters wide, spectral unmixing is applied to separate different material mixtures reflected in one image pixel. The spectral images are assumed to have a spatial resolution of 5-20m with multispectral or hyperspectral band configurations. Combining the identified material part fractions with height information and GIS data afterwards will give estimates about the location of biomass objects. The method is applied to test data of a Sentinel-2 simulation and the results are evaluated visually.Federal Ministry of Economics and Technology (BMWi)DLR/50EE1212DLR/50EE1213DLR/50EE121

Are urban material gradients transferable between areas?

Urban areas contain a complex mixture of surface materials resulting in mixed pixels that are challenging to handle with conventional mapping approaches. In particular, for spaceborne hyperspectral images (HSIs) with sufficient spectral resolution to differentiate urban surface materials, the spatial resolution of 30 m (e.g. EnMAP HSIs) makes it difficult to find the spectrally pure pixels required for detailed mapping of urban surface materials. Gradient analysis, which is commonly used in ecology to map natural vegetation consisting of a complex mixture of species, is therefore a promising and practical tool for pattern recognition of urban surface material mixtures. However, the gradients are determined in a data-driven manner, so analysis of their spatial transferability is urgently required. We selected two areas—the Ostbahnhof (Ost) area and the Nymphenburg (Nym) area in Munich, Germany—with simulated EnMAP HSIs and material maps, treating the Ost area as the target area and the Nym area as the well-known area. Three gradient analysis approaches were subsequently proposed for pattern recognition in the Ost area for the cases of (i) sufficient samples collected in the Ost area; (ii) some samples in the Ost area; and (iii) no samples in the Ost area. The Ost samples were used to generate an ordination space in case (i), while the Nym samples were used to create the ordination space to support the pattern recognition of the Ost area in cases (ii) and (iii). The Mantel statistical results show that the sample distributions in the two ordination spaces are similar, with high confidence (the Mantel statistics are 0.995 and 0.990, with a significance of 0.001 in 999 free permutations of the Ost and Nym samples). The results of the partial least square regression models and 10-fold cross-validation show a strong relationship (the calculation-validation R2 values on the first gradient among the three approaches are 0.898, 0.892; 0.760, 0.743; and 0.860, 0.836, and those on the second gradient are 0.433, 0.351; 0.698, 0.648; and 0.736, 0.646) between the ordination scores of the samples and their reflectance values. The mapping results of the Ost area from three approaches also show similar patterns (e.g. the distribution of vegetation, artificial materials, water, and ceremony area) and characteristics of urban structures (the intensity of buildings). Therefore, our findings can help assess the transferability of urban material gradients between similar urban areas