# Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach
## 4 Discussion
Therefore, a high-resolution and highly accurate digital elevation model would be required to obtain the best possible orthorectification of Venus images, which should significantly improve glacier surface velocity maps. Due to this limitation, it is not yet possible to draw conclusions on the benefits of the higher spatial resolution provided by the Venus data for mapping glacier surface velocity. On the other hand, since the issue of orthorectification seems random (not systematic), the high temporal resolution of Venus (2 days) acquisitions can however be used to generate precise time-averaged map. In addition, Venus will undoubtedly help to improve the time series of glacier speed, as shown in Figure 3C, where Venus captures a short-term acceleration of the Fox Glacier with more details than Sentinel-2. In the case of Planet Labs' satellite constellation, our analysis shows similar problems to those of Venus for the quality of orthorectification of images. However, with additional corrections for uncompensated stereo effects, the Planet Labs data have interesting potential for mapping glacier surface velocity. These results highlight the importance of having frequent acquisitions with very high resolution sensors and an accurate ortho-rectification in order to capture the changes in surface velocity of small mountain glaciers. It also shows that Sentinel-2 and Landsat 8 are able to capture with enough details and precision the main glaciers of the world glacier-covered massits for future studies. Thus, we showed from the comparison with very high resolution sensors and GPS measurements that \"conventional\" sensors (Sentinel and Landsat) capture surface flow velocity for most glaciers larger than 250 m (Figure 3 and Figures 5-7). Currently, Pleiades, Planet Labs or Venus can locally improve the large scale acquisitions from Sentinel-2 and Landsat 8 and therefore supplement the record of glacier dynamics at even finer spatial and temporal scales. When sufficient amount of data are available and stacked, we found a good agreement between GPS and satellite derived glacier surface velocity for glaciers that are at least 250 m wide (Figure 5), suggesting that the accuracy of the Sentinel-2 time-averaged map would be less than 10 m/year. Glacierized areas with persistent shade, snow cover and/or limited number of surface features still remain challenging for Landsat 8 and Sentinel-2 where precision and accuracy are much lower. The use of an orientation correlation algorithm as described in [26], could potentially improve ice velocity mapping in such regions with strong changes in image intensity. Additional post-processing filtering, for example based on the flow direction of the velocity vector, could be envisioned in future implementations and would certainly improve the final velocity product or time series analysis [8; 41]. We have shown that, by combining all possible repetition cycles, Sentinel-2 or Landsat 8 can be used to produce time-averaged maps at a spatial resolution of 50-m with enough detail to capture most individual mountain glaciers (Figures 1-3). Although the native resolution of Sentinel-2 and Landsat 8 remains a limitation for capturing smaller glaciers (<250 m wide), we observe a gain over existing Landsat 8-based databases for mapping mountain glaciers. We attribute this difference to the higher spatial density of the offset maps generated here and the higher resolution of Sentinel-2 compared to Landsat 8. We note that with our approach, each correlation of offset maps is not independent of these direct neighbours. However, it would seem that this approach provides more convincing results than interpolating correlation maps generated on a coarser grid. Our data processing and use of Sentinel-2 data therefore provides improved mapping of mountain glaciers compared to existing global ice speed products such as the GoLIVE project [15; 37].