Applications and Validation for Permafrost Landscapes and Arctic Coastal Waters

Permafrost is a subsurface phenomenon and cannot be directly observed with optical, thermal and microwave remote sensing. However, we can derive a range of surface variables that are indicative or drive permafrost-related processes. The AWI remote sensing group RESPONSE develops quantitative and qualitative remote sensing methods for monitoring Arctic vegetation, surface waters, surface temperature, surface moisture, ground freeze/thaw, snow, and topography-related variables.

Remote Sensing applications for high-latitude permafrost landscapes and for Arctic coastal waters need to look into the heterogeneity within the footprint of the sensor-on-satellite and into the difficult viewing geometries of the high-latitudes. We use Experimental Remote Sensing for understanding coarse-scale satellite-derived signals and developing applications in innovative ways. Experimental Remote Sensing is also part of the scientific preparation phase of future space missions.

  • We apply evaluation of the signal-at sensor normalized to surface (optical and microwave remote sensing) and of higher product levels (e.g., vegetation, aquatic variables, ground freeze/thaw and more). Within the programmes of HGF-EDA, BMWi-EnMAP, FP7-PAGE21, ESA DUE Permafrost and ESA DUE GlobPermafrost we apply ground assessments of homogenous fields (ca. 30 m x 30 m) for spatial upscaling to the measured signal at sensor.
  • Buchhorn & Petereit (AWI-Workshop) developed a transportable field goniometer: (AWI-Patent DE10-2011117713.A1) for bi-directional spectral measurements (BRDF) in harsh terrain.
  • We set up operational optical measurements for Coloured Dissolved Organic Matter (cDOM) at the Otto Schmidt Laboratory at the Arctic and Antarctic Institute AARI in St. Petersburg (RU). We use cDOM as aquatic-optical tracer for terrestrial organic matter input into thermokarst lakes and Arctic coastal waters.
  • Within REKLIM we set up the spatio-temporal evaluation of simulated bio- and geophysical surface variables from modelling (climate, land surface and permafrost) using satellite-derived bio- and geophysical surface variables.

Team:

Alison Beamish
Samuel Stettner
Sofia Antonova
Yuri Dvornikov