Contact: Kathrin NaegeliXiaodon Wu
Consortium: Stefan Wunderle (University of Bern), ENVEO (Austria), Environment and Climate Change Canada, EURAC (Italy), FMI (Finland), Gamma Remote Sensing (Switzerland), NR (Norway), CNRS IGE (France), SMHI (Sweden), University of Edinburgh (Scotland)
Funding source: ESA
Duration: Summer 2018 – Summer 2021

Seasonal snow cover is the largest single component of the cryosphere, covering 50% of the northern hemisphere’s land surface during mid-winter, and is an important component of the climate system. Seasonal snow cover is a crucial and challenging research issue in climate analysis and modelling. It influences energy, moisture and gas fluxes between the land surface and atmosphere; its high reflectivity, or albedo, provides a significant feedback effect in a warming climate; and its sensitivity to precipitation and temperature regimes makes it widely recognised as a fundamental indicator of climate variability and change. Snow is also a major, if not dominant, freshwater source in many alpine, high- and mid-latitude regions an important contribution to the global water cycle.

The ESA CCI+ Snow project aims to contribute to the understanding of Snow in the climate system by generating consistent, high quality long-term data sets that meet the requirements of the Global Climate Observing System (GCOS). The Remote Sensing Research Group at the University of Bern is responsible for the workpages 131, 132, 133 Data Access Requirements and holds the lead for the Earth Observation Team within the project. Further, we are contributing to the algorithm development and the generation of snow extent products and to the validation of snow water equivalent products, in particular based on AVHRR GAC data. Further details about the ESA CCI+ Snow project and its partners can be found here (Link project page).


Associated Master Thesis

Snow is a crucial natural resource covers the largest part in cryosphere. The significance of snow cover on climate at regional and global scale is highly recognized. The reflectivity of snow creates higher albedo, which influence the climate by reducing surface net radiation and energy transmission.  Furthermore, the significant changes in the aerial distribution of snow cover affects snow melt runoff, fresh water supply, water balance, hydropower generation, and ground water recharge tourism beside others. Thus, mountain dominated countries like Switzerland depend on snow cover and are affected by its variability. Monitoring of the temporal and spatial variability of snow cover over land areas allows us to understand the global and regional climate, the hydrological process, substantial environmental and socio-economic affect. Moreover, it is a primary indicator for climate change. 

Among all satellite sensors, only AVHRR provides the opportunity to retrieve long time series of more than 40 years to study global Earth surface process on daily basis.  The comprehensive aim of my master thesis will be focused on generating a procedure to address differences and similarities of snow cover products of AVHRR GAC (Global Area Converge) and LAC (Local Area Coverage) data for 5 winter over European region. For that, we will consider different topography and land cover, which is of special interest for the mountainous region of Switzerland. In addition, the accuracy of snow cover products, derived from AVHRR GAC and LAC data, will be measured.

We will be using AVHRR 1.1 km LAC which is archived at University of Bern by RSGB and 4.4 km GAC data available from 1981 until today. We will compare the accuracy of the product from the higher resolution AVHRR LAC data with the coarser GAC data and quantify the change of the snow products derived from two data sets.  This study will add information about the status of snow cover on a continental scale and which can supplement climate change studies in future. Furthermore, the study will fill the gap in existing available approaches dealing with development of long term approaches based on AVHRR data. The new comparison between LAC and GAC data will show their individual benefits and additional value for consistent climatic records.

Master Student: Soumita Patra

Advisor/Supervisor: Kathrin Naegeli, Stefan Wunderle