Image: Siberia (Russia), simulated EnMAP data based on Sentinel-2 RGB (658 nm, 569 nm, 479 nm)
Source: ESA, GFZ, DLR

Despite the environmental limitations for optical sensors in the Polar Regions (i.e., months of polar night, high cloud coverage, and year-round low solar incidence angles), hyperspectral satellite and airborne data provide unique synoptic information on biogeochemical and -physical environmental quantities. Hyperspectral data provide important information in space and time to derive terrestrial and aquatic energy, water, sediment, and carbon fluxes. Accordingly, hyperspectral remote sensing has the potential to measure key diagnostic parameters that map, monitor and model the cryosphere at landscape scales.

EnMAP will provide the first biome scale estimates of key vegetation properties in Arctic tundra landscapes.

  • Monitoring of the spatio-temporal dynamics of permafrost disturbances and ALT
  • Retrieval of surface thermal properties in permafrost landscapes
  • Identification of vegetation succession in disturbed permafrost landscapes
  • Retrieval of plant biophysical and biochemical plant properties in permafrost landscapes

Characterization of snow-covered areas and inland ice is critical for understanding the Earth’s hydrology, climatology, and ecology. This is because of the significant effect of snow-covered areas on the energy balance at the land-atmosphere boundary and their importance as fresh water sources. However, detailed ground-based measurements of snow and inland ice properties are scarce due to the remoteness, challenging and often dangerous logistics. EnMAP can contribute to an improved understanding of snow and ice processes, which is essential to evaluate the associated impacts on climate change.

The following main scientific tasks are related to snow and ice:

  • Development and improvement of new hyperspectral approaches to retrieve snow properties (e.g., albedo, grain size and near-surface liquid water, biological and inorganic LAPs) and spatial snow cover distribution;
  • Exploration of synergies to multispectral sensors with varying spatial scales to improve snow mapping in forests and adapt to angular variability;
  • Determination of the spatial and temporal variability of ponded ice spectral reflectance properties as a key parameter in determining the large-scale sea-ice albedo; and
  • Determination of snow accumulation, melt pond areal fraction, and sediment load on sea ice to overcome problems associated with the significant spatial inhomogeneity observed and the fact that it occurs in largely inaccessible parts of the world's oceans.
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