Our group focuses on the development of future EO sensors and state of the art data pre-processing algorithms. Our activities cover a broad spectrum of research fields such as hyperspectral sensors, sensor end-to-end simulations, night illumination, radiative transfer, atmospheric correction, big data processing, as well as geometric fusion of hyperspectral and Lidar data.
The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral mission, scheduled for launch in 2020. The primary goal of EnMAP is to offer accurate, diagnostic information on the state and evolution of terrestrial ecosystems on a timely and frequent basis, and to allow for a detailed analysis of surface parameters with regard to the characterization of vegetation canopies, rock/soil targets and coastal waters on a global scale. EnMAP is designed to record bio-physical, bio-chemical and geo-chemical variables to increase our understanding of biospheric /geospheric processes and to ensure the sustainability of our resources.
EnMAP will monitor the Earth's surface with a ground sampling distance (GSD) of 30 m x 30 m (30 km x 5000 km per day) measuring in the 420-2450 nm range by means of two separate spectrometers covering the visible to near-infrared (VNIR) and short-wave infrared (SWIR) spectral regions with 244 contiguous bands. The mean spectral sampling distance and resolution is of 6.5 nm at the VNIR, and of 10 nm at the SWIR. Accurate radiometric and spectral responses are guaranteed by a defined signal-to-noise ratio (SNR) of ≥ 400:1 in the VNIR and ≥ 170:1 in the SWIR, a radiometric calibration accuracy better than 5% and a spectral calibration uncertainty of 0.5 in the VNIR and 1 nm in the SWIR. An off-nadir pointing capability of up to 30° enables a target revisit time of 4 days. In this program the GFZ Potsdam has the scientific lead, OHB System AG is the industrial prime and provides the bus. The German Space Agency is managing the project and the German Aerospace Establishment is responsible for the ground segment.
GeoMultiSens exploits current scientific and technological advances in Big Data infrastructures, parallel computing environments, Visual Analytics and links them with the potential of satellite remote sensing data to address global challenges such as deforestation, loss of biodiversity, and mega cities.
Artificial light is a unique signature of human activity, with strong correlations to population, economic development level, and electrification rate. It is also a form of global change that remains poorly understood, with consequences for ecosystem services and loss of biodiversity. The spectral, spatial, and temporal patterns of artificial light emission of cities are examined.
Airborne hyperspectral data require careful processing by utilizing automated, consistent, highly flexible and robust methods. The fusion of passive hyperspectral and active LIDAR sensor characteristics represents a current research topic because it enables a comprehensive object characterization by integrating a high spectral with a high spatial resolution. All synergies in terms of geometric and spectral data alignment as well as process refinement are utilized to improve data quality and information content.