The animation below shows the evolution of the snow cover area near Andorra la Vella, the highest capital city in Europe and a popular ski resort, from 15-Feb-2019 to 27-Feb-2019.
This time series is quite exceptional. It is not common to find 6 successive cloud-free Sentinel-2 images over mountain areas in winter! This is due to the high pressure system which brought unusual warm temperatures and clear skies in west Europe. In addition, the average revisit time of Sentinel-2 in the Principality of Andorra is 2.5 days, thanks to the overlap of acquisitions from two adjacent orbits. We can identify melt areas by differencing the first and the last image of the time series.
We can already observe that the snow is gone (red pixels) preferentially in the south-facing slopes near Andorra la Vella. To verify this observation at larger scale (country scale !) I extracted the slope orientation (i.e. aspect) of all the red pixels in the full Sentinel-2 tile using the SRTM DEM.
This is the polar histogram of the red pixels aspect. Our first impression was correct!
Can we conclude from this analysis that the snow melts faster in southern slopes? Nope, because we don’t know if the snow accumulation was homogeneous at the beginning of the time series on Feb 15!
The snow maps were obtained from the Theia Sentinel-2 snow collection.
4 thoughts on “Snow melt patterns in Andorra in February 2019”
Hi Simon, Great article!How did you get the polar histogram from the image data. Did you first do a segmentation and then looked at the orientation of the segments? (which I don’t know either how you’d exactly do). Cheers, François
Hi François, Thanks for the feedback!the histogram was done at pixel level by selecting all the pixels that have lost snow. The aspect raster was computed using gdaldem. It would be interesting to do an image segmentation, to relate the melt patch size to the slope, aspect etc.. The data are available :)Simon
I see! I didn’t even think of the DEM ^^Cheers,F
ah right sorry I forgot to mention that I used the SRTM DEM oversampled to 20m (the same one that is used to make the slope correction in MAJA)