Abstract:The EnMAP-Box is a toolbox that is developed for the processing and analysis of data acquired by the German spaceborne imaging spectrometer EnMAP (Environmental Mapping and Analysis Program). It is developed with two aims in mind in order to guarantee full usage of future EnMAP data, i.e., (1) extending the EnMAP user community and (2) providing access to recent approaches for imaging spectroscopy data processing. The software is freely available and offers a range of tools and applications for the processing of spectral imagery, including classical processing tools for imaging spectroscopy data as well as powerful machine learning approaches or interfaces for the integration of methods available in scripting languages. A special developer version includes the full open source code, an application programming interface and an application wizard for easy integration and documentation of new developments. This paper gives an overview of the EnMAP-Box for users and developers, explains typical workflows along an application example and exemplifies the concept for making it a frequently used and constantly extended platform for imaging spectroscopy applications. OPEN ACCESSRemote Sens. 2015, 7 11250

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Abstract. The CEFLES2 campaign during the Carbo Europe Regional Experiment Strategy was designed to provide simultaneous airborne measurements of solar induced fluorescence and CO 2 fluxes. It was combined with extensive ground-based quantification of leaf-and canopy-level processes in support of ESA's Candidate Earth Explorer Mission of the "Fluorescence Explorer" (FLEX). The aim of this campaign was to test if fluorescence signal detected from an airborne platform can be used to improve estimates of plant mediated exchange on the mesoscale. Canopy fluorescence was quantified from four airborne platforms using a combination of novel sensors: (i) the prototype airborne sensor AirFLEX quantified fluorescence in the oxygen A and B bands, (ii) a hyperspectral spectrometer (ASD) measured reflectance along transects during 12 day courses, (iii) spatially high resolution georeferenced hyperspectral data cubes containing the whole optical spectrum and the thermal region were gathered with an AHS sensor, and (iv) the first employment of the high performance imaging spectrometer HYPER delivered spatially explicit and multi-temporal transects across the whole region. During three measurement periods in April, June and September 2007 structural, functional and radiometric characteristics of more than 20 different vegetation types in the Les Landes region, Southwest France, were extensively characterized on the ground. The campaign concept focussed especially on quantifying plant mediated exchange processes (photosynthetic electron transport, CO 2 uptake, evapotranspiration) and fluorescence emission. The comparison between passive sun-induced fluorescence and active laser-induced fluorescence was performed on a corn canopy in the daily cycle and under desiccation stress. Both techniques show good agreement in detecting stress induced fluorescence change at the 760 nm band. On the large scale, airborne and ground-level measurements of fluorescence were compared on several vegetation types supporting the scaling of this novel remote sensing signal. The multi-scale design of the four airborne radiometric measurements along with extensive ground activities fosters a nested approach to quantify photosynthetic efficiency and gross primary productivity (GPP) from passive fluorescence.

Urban areas and their vertical characteristics have a manifold and far-reaching impact on our environment. However, openly accessible information at high spatial resolution is still missing at large for complete countries or regions. In this study, we combined Sentinel-1A/B and Sentinel-2A/B time series to map building heights for entire Germany on a 10 m grid resolving built-up structures in rural and urban contexts. We utilized information from the spectral/polarization, temporal and spatial dimensions by combining band-wise temporal aggregation statistics with morphological metrics. We trained machine learning regression models with highly accurate building height information from several 3D building models. The novelty of this method lies in the very fine resolution yet large spatial extent to which it can be applied, as well as in the use of building shadows in optical imagery. Results indicate that both radar-only and optical-only models can be used to predict building height, but the synergistic combination of both data sources leads to superior results. When testing the model against independent datasets, very consistent performance was achieved (frequency-weighted RMSE of 2.9 m to 3.5 m), which suggests that the prediction of the most frequently occurring buildings was robust. The average building height varies considerably across Germany with lower buildings in Eastern and South-Eastern Germany and taller ones along the highly urbanized areas in Western Germany. We emphasize the straightforward applicability of this approach on the national scale. It mostly relies on freely available satellite imagery and open source software, which potentially permit frequent update cycles and cost-effective mapping that may be relevant for a plethora of different applications, e.g. physical analysis of structural features or mapping society's resource usage.