Validation of operational AVHRR subpixel snow retrievals over the European Alps based on ASTER data

Abstract

Snow is of great economic and social importance for the European Alps. Accurate monitoring of the alpine snow cover is a key component in studying regional climate change as well as in daily weather forecasting and snowmelt run‐off modelling. These applications require snow cover information on a high temporal resolution in near‐real time. For the European Alps, operational snow cover fraction maps are generated on a daily basis using data from the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) platforms. Snow cover distribution is inherently discontinuous and heterogeneous in this mountainous region. We have therefore implemented a straightforward multiple endmember unmixing approach to estimate fractional snow cover. Subpixel proportions are difficult to validate because similar products are not available and appropriate ground‐based observations do not exist. In this study, we validate AVHRR subpixel snow retrievals using binary classified data sets from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to establish absolute errors of our operational approach at three test sites. Our analysis indicates that the AVHRR subpixel maps compare well with the aggregated ASTER data, showing an overall correlation of 0.78 and providing subpixel estimates with a mean absolute error of 10.4% fractional snow cover. Discrepancies between AVHRR and ASTER snow fraction maps can be attributed to varying snow conditions, terrain effects and density in forest cover.

Acknowledgements

We thank Dr Manfred Stähli and the referee for their valuable comments on this manuscript. ASTER data were obtained from the US Geological Survey (USGS) Earth Resources Observation Systems (EROS) Data Center. We also thank MeteoSwiss and the National Center for Environmental Prediction (NCEP) for supplying meteorological data, and the Swiss Federal Statistical Office for providing land cover data.