The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission that aims at monitoring and characterizing the Earth’s environment on a global scale. EnMAP was launched on the 1st April 2022 and provides operational, accurate and diagnostic surface parameters for terrestrial and aquatic ecosystems which are used in a range of application fields. EnMAP allows to quantify and model crucial ecosystem processes, to study the diverse effects of human interventions and to support the management of natural resources.

The main objective of the project CHIME E2E is the development of an end-to-end satellite simulator for ESA, which is able to simulate realistically and very accurately the whole chain starting from data recording, sensor calibration and data pre-processing to sensor products up to final surface parameter maps.

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 multi-sensor remote sensing data requires careful processing using automatic, consistent, flexible and robust methods. In particular, the fusion of passive hyperspectral with active LIDAR sensor characteristics represents a current research focus. By integrating a high spectral with a high spatial and structural resolution, this type of data fusion allows a more comprehensive object characterization. All synergies regarding geometric and spectral adaptation are exploited to improve both data quality and information content. For this purpose, a variety of modern algorithms and methods are used. A combination of physically driven methods (photogrammetry, ray tracing, radiative transfer modelling) and data driven methods (computer vision, deep learning, point cloud processing) allow for the most realistic measurement of surface reflectance free of geometrical, illumination and viewing angle influences.

Mapping the micro- and macro-plastic pollution of the environment ENSURE

Monitoring of arable crops is one of the core applications of the upcoming EnMAP mission. The simulation of multitemporal reflectance patterns posed by varying viewing geometry and plant phenology is of very high interest. For this reason HySimCaR was developed, a spectral, spatial and temporal simulation system for top-of-canopy reflectance including 4D plant models and ray tracing techniques. This data serves as input for the EnMAP scene simulator.