GCOS-Project: Surface temperatures of Swiss lakes derived from AVHRR data (1989–2010)

Contact: Michael Riffler or Stefan Wunderle
Funding source: MeteoSwiss

The surface temperature of lakes (LST) can be used as a proxy for climate warming. In some regions of the world the water body temperatures show a pronounced increase, which exceeds air temperature on land (Schneider and Hook, 2010). Hence, LST is defined as an essential climate variable (ECV) by GCOS (2010) to support UNFCCC and IPCC. Only satellite sensors can measure lake surface temperature (LST) on a comprehensive basis and with high temporal resolution. The spatial resolution of a satellite sensor determines the minimum size of a lake to be monitored. For the AVHRR sensor this size is at 25 km2, which means that approximately ten lakes in Switzerland (Lake Geneva, Lake Constance, Lake Neuchâtel, Lake Maggiore, Lake Lucerne, Lake Zurich, Lake Lugano, Lake Thun, Lake Biel, Lake Zug, Lake Brienz, Lake Walen) will be monitored within the frame of a GCOS-Switzerland project.

Example plot of surface temperature of Lake Geneva
Figure: Surface temperature of Lake Geneva, Switzerland based on data of NOAA-15, January 25, 2011

The aim of the project is to process and analyze NOAA-AVHRR data of our archive from 1989 – 2010 to derive LST of the above mentioned lakes. Later on, the final data set will be available for other groups on behalf of GCOS-Switzerland.
The whole work is based on investigation of Dr. D. Oesch who developed algorithms and processed a long LST data set as a contribution for his Ph.D. thesis. His near real-time application will be adjusted throughout the new project.

The timing of freezing and thawing of freshwater lakes across the Northern Hemisphere is locally variable but show overall consistent long-term trends towards shorter ice coverage. A shorter duration of ice cover is characterized by delayed freeze-up and earlier break-up dates. The recent rate of these changes for lakes located within the Northern Hemisphere even exceeded the long-term averaged trend. These changes will have significant impact on aquatic biology and ecology but also socio-economic consequences as valuable recreational resources and transportation on ice roads and by ship are effected.

The link between lake ice phenology and air temperature is well established. Lake ice phenology responds locally and regionally to long-term trends in air temperature driven by large-scale climate forcing and in turn effects regional climate and weather events. Therefore, lake ice is a well-established proxy for climate variability and change. Lake ice phenology has been highlighted since the early 1990s as a key variable for cryospheric observations and is nowadays defined as one of the Essential Climate Variables (ECVs) by the Global Climate Observing System (GCOS). Global observation depend upon in situ and satellite observations, but only few products are available.

Different sensors covering divers time periods and spatial coverage demonstrated the potential of remote sensing for this field of research and monitoring purposes. This study utilized the unique potential of the Advanced Very High Resolution Radiometer (AVHRR) for climate studies. AVHRR offers each day global coverage from the early 1980s expectedly until 2020. Data from the full-resolution archive of the Remote Sensing Research Group of the University of Bern (RSGB) are used to derive lake ice phenology.

The pilot study presented here, focuses on lakes in the Baltic region for which extended observational data is available to validate the results. An automated extraction technique, developed by Latifovic & Pouliot (2007), is applied and adapted to the pre-processed data set. The method utilizes the fact that the reflectance increases with the formation of ice in the visible spectral range. An improvement upon this method includes information of the thermal infrared. In contrast to other studies using thermal infrared the thresholds are derived from the data itself, making it unnecessary to define arbitrary or lake specific thresholds. The validation of both methods shows overall higher accuracy for the new approach. Using the new approach, two time series are derived and discussed in terms of changing ice cover duration and climate conditions. According to findings in literature, in situ time series of the lakes revealed no trends in lake ice phenology, despite large warming trends in air and water temperature of both lakes.

Contact: Helga Weber
Supervisor/Advisor: Michael Riffler, Stefan Wunderle


Riffler, Michael; Lieberherr, Gian-Duri; Wunderle, Stefan (2015). Lake surface water temperatures of European Alpine lakes (1989–2013) based on the Advanced Very High Resolution Radiometer (AVHRR) 1 km data set. Earth System Science Data, 7(1), pp. 1-17. Copernicus Publications 10.5194/essd-7-1-2015

Weber, Helga; Riffler, Michael; Nõges, T.; Wunderle, Stefan (2016). Lake ice phenology from AVHRR data for European lakes: An automated two-step extraction method. Remote sensing of environment, 174, pp. 329-340. Elsevier 10.1016/j.rse.2015.12.014