## 7 Conclusions  
In this study, we presented a method (ASMAG) for the multi-temporal (1985-2016) retrieval of snow cover ratios (SCR) and snow line altitudes (SLA), and the results of its application to selected glaciers in the Otztal Alps as well as Abramov Glacier in the Pamir Alay, Kyrgyzstan. The SC mapping is applied to each glacier individually using an automatically selected top of atmosphere reflectance  
Figure 12: (**a**) The eastern part of HJF with a cloud hiding a part of its tongue. The classification on the right shows that the cloud was mapped as snow, which resulted in wrong values from the SLA detection method. (**b**) Google Earth view (image from 12 August 2003) from HJF. The red outlines show the snow classification from 30 July 2003 and yellow lines mark the glacier mask from 2011. (TOAR) threshold based on the Otsu method. Subsequently, the SC map is intersected with 20 m elevation bins of the DEM and its SLA is retrieved. The overall performance of ASMAG can be seen as good to very good (90% accuracy), but local outliers exist. The SLA detection capability is better than 80% with an accuracy of \(\pm\)19 m. If the acquisition date of the satellite scene is close to the date of the highest snowline, a good agreement of the derived SCR (SLA) values with the measured AAR (ELA) values is achieved, otherwise SCR (SLA) values are systematically higher (lower). However, glacier specific differences are still correctly captured. Problems arise when clouds and their shadows are covering parts of glaciers or when fresh snow is present. A small amount of debris cover has no impact on the result, but for large debris covered glaciers a separate ice mask could be useful. A DEM from one point in time is important and useful for the topographic correction of the NIR band. When analysing a time period of more than ten years, a DEM of one point in time introduces errors of the SLA values especially in low laying areas of the glacier. In contrary, the unchanged glacier mask only has a minor impact on the SCR in long time series. We conclude that ASMAG is a promising tool to derive SCR and the SLA for large glacier samples. The derived snow cover maps can be used to calibrate/validate modelled snow cover evolution and as a proxy for glacier mass balance if cloud-free satellite scenes acquired close to the date of the highest snowline are available. However, Landsat data alone is too sparse for accurate retrieval of maximum snow line elevations, particularly in mountain regions with frequent cloud cover. The inclusion of higher frequency optical satellite data such as the forthcoming abundance of Sentinel-2 imagery will help overcome this restriction and could be relatively readily incorporated into the tool. The following are available online at [[http://www.mdpi.com/2072-4292/11/12/1410/s1](http://www.mdpi.com/2072-4292/11/12/1410/s1)]([http://www.mdpi.com/2072-4292/11/12/1410/s1](http://www.mdpi.com/2072-4292/11/12/1410/s1)). **Author Contributions:** P.R. : Development and coding of the ASMAG algorithm, validation and quality checks of the results based on the suggestions of the co-authors, writing of the manuscript, R.P. : critical assessment of field work data, C.N. : input on the methodology and preparation of the original draft, F.P. : supervision of the study and discussion of results, all co-authors provided feedback on the methodology and contributed to the writing and editing of the manuscript. **Funding:** This research was initiated during the project \"hiSNOW--High resolution monitoring and modelling under climate change condition\" financed by Provincia Autonoma di Bolzano, Alto Adige, Ripartizione Diritto allo Studio, Universita e Ricerca Scientifica. This research was supported by the project CRYOMON--SeiPro: Improve the Science of Processes within the Cryosphere by Integrating Hydrological Modelling with Remote Sensing in a Multi-Level Data Fusion Approach--a Contribution to Cryosphere Monitoring in the Euregio Region.