Imaging spectroscopy, also known as hyperspectral remote sensing, is based on the characterization of Earth surface materials and processes through spectrally-resolved measurements of the light interacting with matter. The potential of imaging spectroscopy for Earth remote sensing has been demonstrated since the 1980s. However, most of the developments and applications in imaging spectroscopy have largely relied on airborne spectrometers, as the amount and quality of space-based imaging spectroscopy data remain relatively low to date. The upcoming Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission is intended to fill this gap. An overview of the main characteristics and current status of the mission is provided in this contribution. The core payload of EnMAP consists of a dual-spectrometer instrument measuring in the optical spectral range between 420 and 2450 nm with a spectral sampling distance varying between 5 and 12 nm and a reference signal-to-noise ratio of 400:1 in the visible and near-infrared and 180:1 in the shortwave-infrared parts of the spectrum. EnMAP images will cover a 30 km-wide area in the across-track direction with a ground sampling distance of 30 m. An across-track tilted observation capability will enable a target revisit time of up to four days at the Equator and better at high latitudes. EnMAP will contribute to the development and exploitation of spaceborne imaging spectroscopy applications by making high-quality data freely available to scientific users worldwide.

Indices of connectivity are critical means for moving from qualitative to (semi-)quantitative evaluations of material (e.g., water, sediment and nutrients) transfer across the building blocks of a terrestrial system. In geomorphology, compared to closely related disciplines like ecology and hydrology, the development of indices has only recently started and as such presents opportunities and challenges that merit attention. In this paper, we review existing indices of sediment connectivity and suggest potential avenues of development for meeting current basic and applied research needs. Specifically, we focus on terrestrial geomorphic systems dominated by processes that are driven by hydro-meteorological forcing, neglecting seismically triggered events, karstic systems and environments controlled by eolian processes. We begin by setting a conceptual framework that combines external forcings (drivers) and system (intrinsic) structural and functional properties relevant to sediment connectivity. This framework guides our review of response variables suitable for sediment connectivity indices. In particular, we consider three sample applications concerned with sediment connectivity in: (i) soil studies at the plot scale, (ii) bedload transport at the reach scale, and (iii) sediment budgets at the catchment scale. In relation to the set of response variables identified, we consider data availability and issues of data acquisition for use in indices of sediment connectivity. We classify currently available indices in raster based, object or network based, and indices based on effective catchment area. Virtually all existing indices address the degree of static, structural connectivity only, with limited attention for process-based, functional connectivity counterparts.

Originally published as:Förster, S., Kaden, K., Foerster, M., Itzerott, S. (2012) Spatially explicit multi-year crop information is required for many environmental applica-17 tions. The study presented here proposes a hierarchical classification approach for per-plot 18 crop type identification that is based on spectral-temporal profiles and accounts for deviations 19 from the average growth stage timings by incorporating agro-meteorological information in 20 the classification process. It is based on the fact that each crop type has a distinct seasonal 21 spectral behaviour and that the weather may accelerate or delay crop development. The classi-22 fication approach was applied to map twelve crop types in a 14 000 km² catchment area in 23Northeast Germany for several consecutive years. An accuracy assessment was performed 24 and compared to those of a maximum likelihood classification. The 7.1 % lower overall clas-25 2 sification accuracy of the spectral-temporal profiles approach may be justified by its inde-26 pendence of ground truth data. The results suggest that the number and timing of image ac-27 quisition is crucial to distinguish crop types. The increasing availability of optical imagery 28 offering a high temporal coverage and a spatial resolution suitable for per-plot crop type map-29 ping will facilitate the continuous refining of the spectral-temporal profiles for common crop 30 types and different agro-regions and is expected to improve the classification accuracy of crop 31 type maps using these profiles. 32 33

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Imaging spectrometry of non-oceanic aquatic ecosystems has been in development since the late 1980s when the first airborne hyperspectral sensors were deployed over lakes. Most water quality management applications were, however, developed using multispectral mid-spatial resolution satellites or coarse spatial resolution ocean colour satellites till now. This situation is about to change with a suite of upcoming imaging spectrometers being deployed from experimental satellites or from the International Space Station. We review the science of developing applications for inland and coastal aquatic ecosystems * C. Giardino