The magnitude of predicted global warming is largest in high latitude and high altitude regions. Retreating glaciers, decreasing sea ice extent, shorter snow cover periods, and accelerated degradation of permafrost areas testify to this long-standing trend. Complex feedback mechanisms caused by a decrease in surface albedo imply a wide range of climatic, hydrologic, ecologic, and geomorphic changes in these regions. Yet, quantifying the state and changes in snow, glaciers, ice caps, sea ice and permafrost is strongly hampered by a lack of ground observations due to the challenging logistics as a result of the remote and complex terrain.
EnMAP data provide unique information on biogeochemical parameters that are important to model energy, water, sediment, and carbon fluxes in cryospheric ecosystems. As an example, permafrost is an underground thermal phenomenon that cannot be directly observed by optical remote sensing. However, there are a large number of surface indicators such as vegetation biomass, plant functional types, the moisture regime, the insulating moss layer, the surface water ratio, and the dissolved organic carbon and particulate matter concentration that can characterize the state of permafrost for modelling thermal, hydrological, and carbon fluxes.
Characterization of snow-covered areas and glaciers is critical for understanding Earth’s hydrology, climatology and ecology because of their significant effect on the energy balance at the land-atmosphere boundary and their importance as fresh water sources. EnMAP can be used to retrieve key snow parameters, such as snow covered area, albedo, grain size, snow impurities, and liquid water content in the near-surface layer, in order to model their effects on the regional water and energy cycle.
The following main scientific tasks are related to cryospheric ecosystems:
- Assess the state and changes in vegetation biomass, hydrology, and surface morphology in permafrost landscapes;
- Discriminate different permafrost vegetation communities and plant functional types;
- Develop and improve new hyperspectral approaches to retrieve snow properties (e.g., albedo, grain size and near-surface liquid water, mineral and organic contaminants) and spatial snow cover distribution;
- Establish multi-seasonal time series of snow parameters to improve regional hydrologic models;
- Analyse the effect of snow impurities on variations in snow melt timing and magnitude; and
- Explore synergies to multispectral sensors with varying spatial scales to improve snow mapping in forests and adapt to angular variability.