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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.

To understand processes in urban environments, such as urban energy fluxes or surface temperature patterns, it is important to map urban surface materials. Airborne imaging spectroscopy data have been successfully used to identify urban surface materials mainly based on unmixing algorithms. Upcoming spaceborne Imaging Spectrometers (IS), such as the Environmental Mapping and Analysis Program (EnMAP), will reduce the time and cost-critical limitations of airborne systems for Earth Observation (EO). However, the spatial resolution of all operated and planned IS in space will not be higher than 20 to 30 m and, thus, the detection of pure Endmember (EM) candidates in urban areas, a requirement for spectral unmixing, is very limited. Gradient analysis could be an alternative method for retrieving urban surface material compositions in pixels from spaceborne IS. The gradient concept is well known in ecology to identify plant species assemblages formed by similar environmental conditions but has never been tested for urban materials. However, urban areas also contain neighbourhoods with similar physical, compositional and structural characteristics. Based on this assumption, this study investigated (1) whether cover fractions of surface materials change gradually in urban areas and (2) whether these gradients can be adequately mapped and interpreted using imaging spectroscopy data (e.g. EnMAP) with 30 m spatial resolution. Similarities of material compositions were analysed on the basis of 153 systematically distributed samples on a detailed surface material map using Detrended Correspondence Analysis (DCA). Determined gradient scores for the first two gradients were regressed against the corresponding mean reflectance of simulated EnMAP spectra using Partial Least Square regression models. Results show strong correlations with R2 = 0.85 and R2 = 0.71 and an RMSE of 0.24 and 0.21 for the first and second axis, respectively. The subsequent mapping of the first gradient reveals patterns that correspond to the transition from predominantly vegetation classes to the dominance of artificial materials. Patterns resulting from the second gradient are associated with surface material compositions that are related to finer structural differences in urban structures. The composite gradient map shows patterns of common surface material compositions that can be related to urban land use classes such as Urban Structure Types (UST). By linking the knowledge of typical material compositions with urban structures, gradient analysis seems to be a powerful tool to map characteristic material compositions in 30 m imaging spectroscopy data of urban areas.

Future spaceborne imaging spectrometers such as EnMAP allow for the monitoring the Earth’s surface and overcome the time- and cost critical limits of airborne based hyperspectral sensor systems. However, the spatial resolution of EnMAP is 30 m, which is in many cases not sufficient to provide essential information on urban areas that are characterized by a dense network of spectrally different objects. Given the spectral resolution of the EnMAP, this leads to complex mixtures. At the same time, the spectral resolution is an advantage and opens up new opportunities for urban analyses. Despite the spectral and spatial complexity of urban settlements, characteristic materials can be found in the composition of similar urban structures such as industrial areas, residential areas and open spaces. This study presents a new concept of spaceborne imaging spectroscopy data analyses. The presented strategy presumes that the composition of material mixtures changes gradually across urban spaces and thus, gradient analyses applied to urban areas could be a suitable methodology to derive surface material compositions. This information is important for urban weather and climate modeling and specifies the requirements to better describe urban canopy models. This study focuses on 1) the adaptation of gradient analysis from vegetation ecology to analyze gradual material transitions in the composition of urban surface materials and 2) the mapping of these material gradients in simulated EnMAP data of Munich, Germany, to identify characteristic material compositions. The Detrended Correspondence Analysis was used to analyze the similarities of material composition for a number of systematically distributed samples in a detailed material map. Results demonstrate the cooccurrences of urban materials along two urban gradients. The determined gradient scores serve as an indicator for the composition of urban surface materials in the respective sample. The resulting models were subsequently applied on the image data to map the gradients. These maps reveal a rural-to-urban transition and allow for a differentiation of urban structures with regard to the degree of imperviousness from the first gradient, as well as the differentiation of subtleties of relatively similar urban structures based on the patterns resulting from the second gradient. The results demonstrate that even in spaceborne hyperspectral remote sensing data detailed structural information is retained, which for the first time enables the identification of characteristic material compositions in very complex spectral mixtures.