Fusion of multi-temporal optical and SAR satellite data for improved snow facies mapping on glaciers

Abstract

Mapping snow cover (SC) on glaciers at the end of the ablation period supports the comprehension how and in which rate a glacier accumulates and ablates, respectively, and which influence climate variabilities have. Furthermore, the SC can be used for the determination of the mass balance. Remote Sensing data are able to provide this information for remote, large glaciers and back in time. With optical satellite imagery it is possible to detect SC on glacier relatively straight forward when no clouds are present and when the bare ice is clearly different from the snow zone on the glacier. Optical satellite data fail, however, to identify whether the snow on the glacier is dry, wet or whether firn is present or not. In contrast, SAR data like TerraSAR-X or Sentinel-1 can discriminate dry snow, wet snow, firn and bare ice. In this contribution, we compare three types of multi-temporal SC maps for a set of glaciers in the Ötztal Alps-Austria. For several acquisition times we performed a multiple digitizing experiment by manually mapping the SC area on the glaciers based on high resolution Landsat image which served in the following as reference data. Additionally, we applied the automatic SC mapping tool named ´automated snow mapping on glaciers´ (ASMAG) based on Landsat imagery. The algorithm automatically determines cloud cover and converts raw digital numbers into topographically corrected top of atmosphere reflectance (TOAR). Subsequently, a reflectance threshold for the near-infrared band is automatically selected to map snow on glaciers. Finally, we used TerraSAR-X and Sentinel-1 data to derive the various snow facies for the acquisition times . A first assessment shows that the manual derived outlines and the SC maps from ASMAG are of high accuracy (~90%). The maps derived from TerraSAR-X are more difficult to interpret. Wet snow maps agree by about 60% with the SC maps from ASMAG, however, dry snow maps, firn and bare ice are difficult to classify. On the other hand, snow facies maps from Sentinel 1 shows a higher agreement in respect to TerraSAR-X, which might be due to the different snow conditions during the acquisitions of TerraSAR-X and Sentinel-1 (5:30 a.m. UTC) and Landsat (11 a.m. UTC). We conclude, that the combination of different sensors is highly recommendable to support a continuous SC monitoring and to eliminate the disadvantages of each sensor. Also, the round robin experiment resulted in a high agreement. Due to the high repetition time of Sentinel-1 data, the comparison of the SC maps is facilitated in contrast to TerraSAR-X.