19 May

Snow Melting over Greenland Ice Sheet

  • Scientific Data Processing
  • Technical R&D Consulting

Project type: proof-of-concept study
Region: Greenland 
Duration: 2 years (September 2020 - September 2022)
Collaboration partner: Lancaster University


The Greenland and Antarctic ice sheets are major components within Earth's climate system, and improving our knowledge of their dynamics is critical to improve the understanding of climate change. The Surface Mass Balance (SMB) is the net mass balance of accumulation and ablation processes operating on the surface of the ice sheet. Together with basal melt and glacial discharge, SMB forms the total mass balance of the ice sheet. Studying the ice sheet mass balance is of key importance to predict sea level rise, furthermore it also directly affects glacier dynamics, global ocean circulation and marine ecosystems.

Surface melt is an important parameter of the mass balance and can be estimated from different satellite data sources. In the POLAR+ EO4SMB project we apply machine learning techniques to CryoSat-2 radar altimeter waveforms for deriving the surface melt parameter. Training data is derived by spatio-temporally matching of CS2 measurements with MODIS land surface temperature measurements. 

One of the main challenges is the high class imbalance, as surface temperatures on the interior of Greenland rarely reach the freezing point. The model performance is measured by several metrics: F1 score, average recall and Matthews correlation coefficient. The results of this proof-of-concept study indicates feasibility.



The project found that it is indeed possible to utilize Cryosat-2 waveforms for melt monitoring. Here is a sneak peak of the results:

You can read more about the results in our published research paper.

Interested? We could make something cool for you too, just contact us!