# Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach
## 4 Discussion  
We implemented a workflow to process datasets from several optical sensors, we generated time-averaged maps of glacier speed from Sentinel-2 data and compared the results with those of other sensors: Landsat 8 with a slightly lower sensor resolution and Venus, Planet Labs and Pleiades with a higher spatial resolution. We tried to evaluate the performance of Sentinel-2 against other optical sensors to map the surface speed of mountain glaciers in three regions of the world with glaciers having distinct morpho-topographical characteristics. Our statistical analysis shows that the glacier surface velocity precision is exponentially improved with increasing cycle length. We note that calculating velocity fields with pairs that have repeat cycles greater than 335 days provides highly precise velocity maps but does not significantly improve the precision (Figure 4C). For example, we found that Sentinel-2 provides glacier surface velocity with a precision 1.5 to 2 times greater than Landsat, which can be attributed to the better resolution of Sentinel-2 (100 m\({}^{2}\) per pixel versus 225 m\({}^{2}\)). We show that, with Sentinel-2 or Venus (Figure 9), time series of flow velocities can be produced at a frequency of 10 days with a signal-to-noise ratio sufficient to capture variations of about 40 m/year, typically for glaciers with speeds greater than 400 m/year. However, speed fluctuations in the order of 10-20 m/year and 20-40 m/year can only be monitored with a reasonable signal-to-noise ratio at a monthly to quarterly time scale (Figure 8), and capturing fluctuations below 10 m/year remain challenging, if not impractical because only  
Figure 9: Map of cycle length in days that would be needed to monitor a 10% change in surface velocity with Sentinel-2, for (**A**) the Cordillera Blanca; (**B**) The Mont-Blanc massif in the Alps; and (**C**) the Tasman Glacier region in New Zealand. Note that, when a 10% change in glacier surface velocity is too small to be detected, no color is displayed. a repeated cycle longer than 100 days would be effective (Figure 8). At present, the precision achieved for the nominal repeat cycles (5 days for Sentinel-2, 2 days for Venus and 16 days for Landsat) is not enough to capture most of the glacier speed changes, typically for glaciers flowing below 200 m/year (Figures 1 and 2, Figures 4C and 6C). Therefore, these sensors with 5 to 15-m spatial resolution might be limited for catching rapid speed fluctuations smaller than 50 m/year. We note that these data are however perfectly suited for faster flowing glaciers with surface velocity \(>\)500 m/year [14; 28; 39]. As expected, the 50 cm spatial resolution Pleiades images completely outperform the precision achieved by other conventional sensors (Sentinel, Landsat) that have a resolution at least 20 times coarser. The glacier surface velocity fields obtained from Pleiades are ideal to document detailed surface velocity patterns (shear margins, small glaciers, tributaries) and to monitor short term variations in the dynamics of mountain glaciers (Figure 8). However, the production of glacier surface velocity maps with Pleiades requires longer processing time, with additional stereo or tri-stereo processing steps, and larger search windows during the cross correlation. With a potential repeat cycle of 1-day, this high-resolution sensor would be the perfect tool for mountain glaciers, but is currently limited because of the non-systematic, non-global acquisitions and commercial access [40]. Although one would have expected a higher precision due to the improved spatial resolution of Venus compared to Sentinel-2, the similar precision obtained is probably due to the larger errors in the orthorectification of Venus data. These orthorectification errors are mainly the result of slight changes in the angle of view due to frequent changes in the satellite's orbit during tests carried out on the satellite electric engine ([PERSON], pers. comm., 2018).