# Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach
## 5 Conclusions  
In this manuscript, we describe a new multi-sensor processing chain for processing a large amount of optical satellite data used to map the surface flow velocity of mountain glaciers on a regional scale. We present the first complete maps of glacier surface speed at a resolution of 50 m for the Peruvian Cordillera Blanca (tropical Andes), the European Alps and the Southern Alps of New Zealand. We performed a statistical analysis to establish the precision of Sentinel-2 and Landsat 8 for different repeat cycles. We conclude that the precision of glacier surface velocity mapping is 1.5 to 2 times better with Sentinel-2 than with Landsat 8 for similar repetition cycles, as expected by the better resolution of Sentinel-2. Our estimation of Sentinel-2 precision indicates that only glaciers with ice velocity fluctuations greater than 10 m/year can be monitored on a seasonal basis. We also estimate that, with Sentinel-2 or Landsat 8, the best temporal resolution that can be achieved is about 10 days for speed variations of about 40 m/year. When all the processed pairs are stacked together, we demonstrate the ability of our processing chain to derive the time-averaged ice flow with an accuracy between a few meters per year and about ten meters per year. However, we note that Sentinel-2 and Landsat 8 cannot capture the smallest glaciers (<250m wide) and that snow-covered accumulation areas remain challenging. By comparing Sentinel-2 mappings with those obtained by Pleiades and Venus, we have shown that the latter can still provide a benefit for mapping time-varying glacier surface flow. The products presented in this study will be available on the THEIA platform. The following are available online at: www.mdpi.com/xxx/s1. Figure S1: Example of ice velocity map and the related percentage of filtered values, remaining values after filtering for 0.5-\(\sigma\), 1-\(\sigma\), 2-\(\sigma\) and 3-\(\sigma\) parameters; Figure S2: Surface velocity of glaciers in the European Alps. Background image is from Google Earth. Surveyed areas are in yellow. Insets show a zoom on selected areas where the glacier surface velocity is represented on a logarithmic scale going from brown (slow) to red (fast) and overlaid on a shaded relief version of the SRTM DEM; Figure S3: Surface velocity of glaciers in New Zealand. Background image is from Google Earth. Surveyed areas are in blue. Insets show a zoom on selected areas where the glacier surface velocity is represented on a logarithmic scale going from brown (slow) to red (fast) and overlaid on a shaded relief version of the SRTM DEM; Figure S4: Number of pairs processed at each pixel location, color coded from dark blue (low number of pairs) to dark red (high number of pairs) for Cordillera Blanca (A) and the Mont-Blanc region in the Europan Alps (B); Figure S5: Comparison between satellite-derived and differential GPS glacier surface velocities (identified as yellow dots) between 2016 and 2018 along the central flowline of Argentiere Glacier in the Mont-Blanc area (European Alps). Displayed error bars are the weighted standard deviation (1-sigma). Background is the glacier surface velocity mosaic on a logarithmic scale from 1.5 (brown) to 300 m/year (red) overlaid on a CNES's Pleiades ortho-image from 2017; Figure S6: Standard deviation on stable ground of the Planet labs glacier surface velocity mosaics as a function of the repeat cycle length. **Author Contributions:** [PERSON], [PERSON] and [PERSON] designed the study. [PERSON], [PERSON], [PERSON] and [PERSON] designed the processing chain. [PERSON], [PERSON], [PERSON] and [PERSON] processed the data. [PERSON] performed the analysis. [PERSON], [PERSON] and [PERSON] wrote the article. All co-authors helped discussing and reviewing the article. **Funding:** This research was funded by a CNES Post-doctoral fellowship, the CNES MaISON and Kalideos Alpes projects.