Mapping snow cover using multi-source satellite data on big data platforms

Surface melt and subsequent firn air depletion is considered an important precursor for disintegration of Antarctic ice shelves, causing grounded glaciers to accelerate and sea level to rise. Recent studies have highlighted the impact of surface winds on Antarctic ice shelf melt, both on the Antarctic Peninsula and in East AntaSnowmelt is an important and dynamically changing water resource in mountainous regions around the world. In this framework, remote sensing data of snow cover data provides an essential input for hydrological models to model the water contribution from remote mountain areas and to understand how this water resource might alter as a result of climate change. Traditionally, however, many of these remote sensing products show a trade-off between spatial and temporal resolution (e.g., 16-day Landsat at 30m vs. daily MODIS at 500m resolution). With the advent of Sentinel-1 and 2 and the PROBA-V 100m products this trade-off can partially be tackled by having data that corresponds more closely to the spatial and temporal variations in snow cover typically observed over complex mountain areas. This study provides first a quantitative analysis of the trade-offs between the state-of-the-art snow cover mapping methodologies for Landsat, MODIS, PROBA-V, Sentinel-1 and 2 and applies them on big data platforms such as Google Earth Engine (GEE), RSS (ESA Research Service & Support) CloudToolbox, and the PROBA-V Mission Exploitation Platform (MEP). Second, it combines the different sensor data-cubes in one multi-sensor classification approach using newly developed spatio-temporal probability classifiers within the big data platform environments. Analysis of the spatio-temporal differences in derived snow cover areas from the different sensors reveals the importance of understanding the spatial and temporal scales at which variations occur. Moreover, it shows the importance of i) temporal resolution when monitoring highly dynamical properties such as snow cover and of ii) differences in satellite viewing angles over complex mountain areas. Finally, it highlights the potential and drawbacks of big data platforms for combining multi-source satellite data for monitoring dynamical processes such as snow cover.rctica. In the Antarctic Peninsula, foehn winds enhance melting near the grounding line, which in the recent past has led to the disintegration of the most northerly ice shelves. On the East Antarctic ice shelves, on the other hand, meltwater-induced firn air depletion is found in the grounding zone as result of persistent katabatic winds, regionally warming the atmosphere and inducing a melt-albedo feedback. Here, we use a combination multi-source satellite imagery, snow modelling, climate model output and insitu observations to highlight the importance of this wind-induced melt and to show its widespread occurrence across Antarctica. The satellite imagery gives insight in the meltwater drainage systems, showing spatio-temporal changes in both supraglacial and englacial water throughout the melt season and during the subsequent winter. Although the wind-induced melt is a regional phenomenon with strong inter-annual variability, it is strongly correlated to larger scale climate parameters, such as summer surface temperature. Based on these correlations and snow model output driven by future climate scenarios, we can constrain the future changes to this local melt near the grounding line.