Gradients in urban material composition: A new concept to map cities with spaceborne imaging spectroscopy data

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

The EnMAP user interface and user request scenarios

EnMAP (Environmental Mapping and Analysis Program) is a German hyperspectral satellite mission providing high quality hyperspectral image data on a timely and frequent basis. Main objective is to investigate a wide range of ecosystem parameters encompassing agriculture, forestry, soil and geological environments, coastal zones and inland waters. The EnMAP Ground Segment will be designed, implemented and operated by the German Aerospace Center (DLR). The Applied Remote Sensing Cluster (DFD) at DLR is responsible for the establishment of a user interface. This paper provides details on the concept, design and functionality of the EnMAP user interface and a first analysis about potential user scenarios. The user interface consists of two online portals. The EnMAP portal (www.enmap.org) provides general EnMAP mission information. It is the central entry point for all international users interested to learn about the EnMAP mission, its objectives, status, data products and processing chains. The EnMAP Data Access Portal (EDAP) is the entry point for any EnMAP data requests and comprises a set of service functions offered for every registered user. The scientific user is able to task the EnMAP HSI for Earth observations by providing tasking parameters, such as area, temporal aspects and allowed tilt angle. In the second part of that paper different user scenarios according to the previously explained tasking parameters are presented and discussed in terms of their feasibility for scientific projects. For that purpose, a prototype of the observation planning tool enabling visualization of different user request scenarios was developed. It can be shown, that the number of data takes in a certain period of time increases with the latitude of the observation area. Further, the observation area can differ with the tilt angle of the satellite. Such findings can be crucial for the planning of remote sensing based projects, especially for those investigating ecosystem gradients in the time domain

Reflectance-Based Imaging Spectrometer Error Budget Field Practicum at the Railroad Valley Test Site, Nevada

Calibration and validation determine the quality and integrity of the data provided by sensors and have enormous downstream impacts on the accuracy and reliabilityof the products generated by these sensors. With the imminent launch of the next generation of space borne imaging spectroscopy sensors, the IEEE Geoscience and Remote Sensing Society's (GRSS's) Geoscience Spaceborne Imaging Spectroscopy Technical Committee (GSIS TC) initiated a calibration and validation initiative.This article reports on a recent reflectance-based imaging spectrometer error budget field practicum focused on radiometric calibration of spaceborne imaging spectroscopy sensors. The field exercise, conducted at Railroad Valley in Nevada, provided valuable training for personnel in a variety of Earth observation (EO) areas, from engineers developing future sensors to calibration scientists actively working in the field. Future work in this area will focus on analyzing the data acquired as part of the training to answer numerous scientific questions, e.g., understanding the spatial and spectral homogeneity of the site being measured, identifying the optimal sampling to characterize the site, and optimizating the sampling techniques, including looking into the automation of some measurement protocol aspects. The training exercise was recorded to ensure that the knowledge can be disseminated across the GRSS and wider imaging spectroscopy community

Solar Panels Area Estimation Using the Spaceborne Imaging Spectrometer DESIS: Outperforming Multispectral Sensors

Solar photovoltaic power plants are in rapid expansion throughout the world, with the total area occupied by panels being linked to the total electrical power produced. This paper considers this case as an instance of the generic problem of estimating the total area occupied by a class of interest in spaceborne hyperspectral images. As the spatial resolution characterizing these sensors is too coarse, spectral unmixing techniques identify the contribution of a specific material to the spectrum related to a single image element. Final results are obtained by summing all contributions in an area of interest, and favourably compared to pixel-based detection, also using higher resolution Sentinel-2 data. The data used in this paper are acquired by the currently operative DESIS sensor, mounted on the International Space Station, encouraging the use of spaceborne imaging spectrometers for such applications

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

Estimating Soil Parameters from DESIS Images using Deep Learning

There are several soil parameters that play a significant role in soil health and thus, crop production. Compared with traditional fieldwork by collecting soil samples, digital soil mapping by remote sensing data has many advantages, such as estimating soil properties efficiently in large areas. Multispectral and hyperspectral data have been used already widely for soil parameter retrieval using spectral soil models. Mostly, hyperspectral data have largely outperformed the model and prediction of soil parameters. However, the accuracy and uncertainty of both model results depend largely on the density of calibration points, which is especially problematic for large areas such as regions and countries. Therefore, new methods are needed that take into account the sparsity of calibration data during the training of the model. This work focuses on the SOC content for the whole Bavarian region in Germany (~70.000 km²). The soil data source is LFU (Bayerisches Landesamt fur Umwelt) and LUCAS 2018 (Land Use and Coverage Area Frame Survey). After data selection, we use 1171 soil samples. As for the hyperspectral images, we use DESIS data in Bavaria, whose spectral range is 400-1000 nm. We use 603 hyperspectral images in experiments. To get spectral reflectance for bare soils, we build temporal reflectance composites surrounding each soil sample from the original images. Specifically, We compute the NDVI value for each pixel and then filter pixels by NDVI threshold to filter out vegetated pixels. Temporal composites are then generated by the pixel-based averaging of the filtered images. These composites are fed into the deep learning model. The model would output the SOC value. Regarding the model framework, it consists of CNN layers followed by fully connected layers. To solve the sparsity of data availability, data augmentation, and transfer learning methodology are investigated in this work. During our experiments, we use cross-validation to evaluate the performance. Root Mean Square Error and R Square are used as the evaluation metrics

Sampling Robustness in Gradient Analysis of Urban Material Mixtures

Many studies analyzing spaceborne hyperspectral images (HSIs) have so far struggled to deal with a lack of pure pixels due to complex mixtures of urban surface materials. Recently, an alternative concept of gradients in urban surface material composition has been proposed and successfully applied to map cities with spaceborne HSIs without the requirement for a previous determination of pure pixels. The gradient concept treats all pixels as mixed and aims to describe and quantify gradual transitions in the cover fractions of surface materials. This concept presents a promising approach to tackle urban mapping using spaceborne HSIs. However, since gradients are determined in a data-driven way, their transferability within urban areas needs to be investigated. For this purpose, we analyze the robustness of urban surface material gradients and their dependence across six systematic and three simple random sampling schemes. The results show high similarity between nine sampling schemes in the primary gradient feature space (Pspace) and individual gradient feature spaces (Ispaces). Comparing the Pspace with the Ispaces, the Mantel statistics show the resemblance of samples' distribution in the Pspace, and each Ispace is rather strong with high credibility, as the significance level is P < 0.01. Therefore, it can be concluded that the material gradients defined in the test area are independent of the specific sampling scheme. This study paves the way for subsequent analysis of the stability of urban surface material gradients and the interpretation of material gradients in other urban environments

Scale-Specific Prediction of Topsoil Organic Carbon Contents Using Terrain Attributes and SCMaP Soil Reflectance Composites

There is a growing need for an area-wide knowledge of SOC contents in agricultural soils at the field scale for food security and monitoring long-term changes related to soil health and climate change. In Germany, SOC maps are mostly available with a spatial resolution of 250 m to 1 km2. The nationwide availability of both digital elevation models at various spatial resolutions and multi-temporal satellite imagery enables the derivation of multi-scale terrain attributes and (here: Landsat-based) multi-temporal soil reflectance composites (SRC) as explanatory variables. In the example of a Bavarian test of about 8000 km2, relations between 220 SOC content samples as well as different aggregation levels of the explanatory variables were analyzed for their scale-specific predictive power. The aggregation levels were generated by applying a region-growing segmentation procedure, and the SOC content prediction was realized by the Random Forest algorithm. In doing so, established approaches of (geographic) object-based image analysis (GEOBIA) and machine learning were combined. The modeling results revealed scale-specific differences. Compared to terrain attributes, the use of SRC parameters leads to a significant model improvement at field-related scale levels. The joint use of both terrain attributes and SRC parameters resulted in further model improvements. The best modeling variant is characterized by an accuracy of R2 = 0.84 and RMSE = 1.99