Time series analysis of remotely sensed snow cover data: Revealing permafrost thermal state and vegetation dynamics

Kurzfassung

Snow cover plays a crucial role in climate change and is considered an essential climate variable by the Global Climate Observing System (GCOS). To accurately monitor daily snow cover extent, optical medium-resolution remote sensing systems like MODIS and VIIRS are employed. DLR's Global SnowPack product, derived from daily MODIS/VIIRS snow cover data, addresses data gaps caused by clouds or polar night, providing gap-free daily datasets since February 2000. The extended time series allows the identification of trends in snow cover duration, which has implications for the thermal state of permafrost soils and vegetation dynamics. Snow acts as an insulating layer, maintaining higher temperatures in the underlying permafrost layer during cold months. Surface temperature analysis indicates gradual increases in daily minimum and annual mean surface temperatures with thicker snow cover. Comparisons of snow cover duration with MODIS Land Surface Temperature/Emissivity data show this connection. Additionally, snow cover influences vegetation dynamics, including observed developments like greening of the Arctic. It provides insulation, affects water availability, and determines the length of the growing season. The MODIS Vegetation Index Products demonstrates strong correlations in this context. In summary, DLR's Global SnowPack product, derived from the daily snow cover data of MODIS and VIIRS, enables the assessment of snow cover trends, but also provides insights into the thermal state of permafrost soils and vegetation dynamics. The interplay between snow cover, temperature, and vegetation were explored using different MODIS/VIIRS products. The first results of this assessment will be presented, advancing our knowledge of climate change.