Reconstruction of the snow cover at high spatial resolution since 1985: an image emulation approach for training a deep learning model without reference data

Multi-decade time series of the snow cover area are typically derived from low resolution sensors such as MODIS (20 years, 500 m) or AVHRR (35 years, 1 km) and fail to capture the high spatial variability of mountain snowpack [1] , [2] . The vast Landsat archive (Landsat 5-8), with an image of the same location captured every 16 days at 30 m spatial resolution since 1984 remains largely untapped in European mountains. In addition, the recent initiative by the French Space Agency (CNES) to release in the public domain the full collection of SPOT 1-5 images with the SPOT World Heritage (SWH) program [3] provides a unique opportunity to densify the Landsat time series from 1986 to 2015 with thousands of 20 m resolution multispectral images, [4] .