Observing glacier elevation changes from spaceborne optical and radar sensors

Quantifying glacier elevation and volume changes is critical to understanding the response of glaciers to climate change and related impacts, such as regional runoff and global sea-level rise. Spaceborne remote sensing techniques enable the quantification of spatially distributed glacier elevation changes at regional and global scales using multi-temporal digital elevation models (DEMs). A growing number of spaceborne studies exist to assess glacier elevation changes but they show widespread differences often beyond the error bars. Here, we present the results of a community-based inter-comparison experiment using spaceborne optical (ASTER) and radar (TanDEM-X) sensors to assess elevation changes for selected individual glaciers and regional glacier samples. Using a predefined set of DEMs, participating groups provided their own estimates using various processing strategies.  

For the selected individual glaciers, the results were validated using airborne data. The validation shows that the median of the spaceborne ensemble is biased by a few decimetres per year with a standard deviation of about half a meter per year. An interesting finding is that no sensor and no processing strategy perform significantly better for all experiment sites. At the regional scale, we find that the co-registration of DEMs is the most relevant processing step for an accurate assessment of elevation change. Other corrections such as gap filling, filtering, and radar penetration have less impact in general but can be essential for individual cases. Temporal corrections (i.e. seasonal and annual) can have a great impact; however, they are not yet well resolved by the remote sensing community.

Our study confirms that the currently available spaceborne geodetic assessments result in relatively widespread glacier elevation changes. Therefore, we recommend an ensemble approach of observations from multiple observational sources. Furthermore, there is a need to establish best practices for related uncertainty estimates.