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.

Imaging spectrometry from aerial or spaceborne platforms, also known as hyperspectral remote sensing, provides dense sampled and fine structured spectral information for each image pixel, allowing the user to identify and characterize Earth surface materials such as minerals in rocks and soils, vegetation types and stress indicators, and water constituents. The recently launched DLR Earth Sensing Imaging Spectrometer (DESIS) installed on the International Space Station (ISS) closes the long-term gap of sparsely available spaceborne imaging spectrometry data and will be part of the upcoming fleet of such new instruments in orbit. DESIS measures in the spectral range from 400 and 1000 nm with a spectral sampling distance of 2.55 nm and a Full Width Half Maximum (FWHM) of about 3.5 nm. The ground sample distance is 30 m with 1024 pixels across track. In this article, a detailed review is given on the applicability of DESIS data based on the specifics of the instrument, the characteristics of the ISS orbit, and the methods applied to generate products. The various DESIS data products available for users are described with the focus on specific processing steps. The results of the data quality and product validation studies show that top-of-atmosphere radiance, geometrically corrected, and bottom-of-atmosphere reflectance products meet the mission requirements. The limitations of the DESIS data products are also subject to a critical examination.

The decline of biodiversity is one of the major current global issues. Still, there is a widespread lack of information about the spatial distribution of individual species and biodiversity as a whole. Remote sensing techniques are increasingly used for biodiversity monitoring and especially the combination of LiDAR and hyperspectral data is expected to deliver valuable information. In this study spatial patterns of vascular plant community composition and α-diversity of a temperate montane forest in Germany were analysed for different forest strata. The predictive power of LiDAR (LiD) and hyperspectral (MNF) datasets alone and combined (MNF+LiD) was compared using random forest regression in a ten-fold cross-validation scheme that included feature selection and model tuning. The final models were used for spatial predictions. Species richness could be predicted with varying accuracy (R² = 0.26 to 0.55) depending on the forest layer. In contrast, community composition of the different layers, obtained by multivariate ordination, could in part be modelled with high accuracies for the first ordination axis (R² = 0.39 to 0.78), but poor accuracies for the second axis (R² ≤ 0.3). LiDAR variables were the best predictors for total species richness across all forest layers (R² _LiD = 0.3, R² _MNF = 0.08, R² _MNF+LiD = 0.2), while for community composition across all forest layers both hyperspectral and LiDAR predictors achieved similar performances (R² _LiD = 0.75, R² _MNF = 0.76, R² _MNF+LiD = 0.78). The improvement in R² was small (≤0.07)—if any—when using both LiDAR and hyperspectral data as compared to using only the best single predictor set. This study shows the high potential of LiDAR and hyperspectral data for plant biodiversity modelling, but also calls for a critical evaluation of the added value of combining both with respect to acquisition costs.

Remote sensing multi-decadal time-series provide important information for analysing long-term environmental change. The Advanced Very High Resolution Radiometer (AVHRR) has been providing data since the early 1980s. Normalised Difference Vegetation Index (NDVI) time-series derived thereof can be used for monitoring vegetation conditions. This study presents the novel TIMELINE NDVI product, which provides a consistent set of daily, 10-day, and monthly NDVI composites at a 1 km spatial resolution based on AVHRR data for Europe and North Africa, currently spanning the period from 1981 to 2018. After investigating temporal and spatial data availability within the TIMELINE monthly NDVI composite product, seasonal NDVI trends have been derived thereof for the period 1989–2018 to assess long-term vegetation change in Europe and northern Africa. The trend analysis reveals distinct patterns with varying NDVI trends for spring, summer and autumn for different regions in Europe. Integrating the entire growing season, the result shows positive NDVI trends for large areas within Europe that confirm and reinforce previous research. The analyses show that the TIMELINE NDVI product allows long-term vegetation dynamics to be monitored at 1 km resolution on a pan-European scale and the detection of specific regional and seasonal patterns.