Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Assimilation of Sentinel-2 Data into a Snowpack Model in the High Atlas of Morocco

Version 1 : Received: 12 October 2018 / Approved: 12 October 2018 / Online: 12 October 2018 (11:17:16 CEST)

A peer-reviewed article of this Preprint also exists.

Baba, M.W.; Gascoin, S.; Hanich, L. Assimilation of Sentinel-2 Data into a Snowpack Model in the High Atlas of Morocco. Remote Sens. 2018, 10, 1982. Baba, M.W.; Gascoin, S.; Hanich, L. Assimilation of Sentinel-2 Data into a Snowpack Model in the High Atlas of Morocco. Remote Sens. 2018, 10, 1982.


The snow melt from the High Atlas is a critical water resource in Morocco. In spite of its importance, monitoring the spatio-temporal evolution of key snow cover properties like the snow water equivalent remains challenging due to the lack of in situ measurements at high elevation. Since 2015, the Sentinel-2 mission provides high spatial resolution images with a 5 day revisit time, which offers new opportunities to characterize snow cover distribution in mountain regions. Here we present a new data assimilation scheme to estimate the state of the snowpack without in situ data. The model was forced using MERRA-2 data and a particle filter was developed to dynamically reduce the biases in temperature and precipitation using Sentinel-2 observations of the snow cover area. The assimilation scheme was implemented using SnowModel, a distributed energy-balance snowpack model and tested in a pilot catchment in the High Atlas. The study period covers 2015-2016 snow season which corresponds to the first operational year of Sentinel-2A, therefore the full revisit capacity was not yet achieved. Yet, we show that the data assimilation led to a better agreement with independent observations of the snow height at an automatic weather station and the snow cover extent from MODIS. The performance of the data assimilation scheme should benefit from the continuous improvements in MERRA-2 reanalyses and the full revisit capacity of Sentinel-2.


snow; semi-arid climate; data assimilation; particle filter; SWE; MERRA-2


Environmental and Earth Sciences, Waste Management and Disposal

Comments (0)

Comment 1
Received: 30 November 2018
Commenter: Kristoffer Aalstad
The commenter has declared there is no conflict of interests.
Comment: Dear Baba et al.

This is a nice study that I read with great interest as I am working on similar topics in my research.
Since you posted it as a preprint, I have some questions/thoughts that you may want to consider.

1) I could not help but notice the sentence:

"To our knowledge, this study is the first to use Sentinel-2 data to estimate SWE."

I would just like to point you in the direction of the following paper: Where we assimilated Sentinel-2 fSCA data to estimate SWE at test sites in the Arctic.

2) We also mention the equifinality issue in our paper. At least in our case it was not a major problem,
given that the main source of uncertainty is probably the precipitation once local topographic effects
are accounted for in the snowmelt calculation. Could it be that it is more of a problem for you since you are trying to correct forcing at the basin (not grid cell) level, at least as I understand your study.

3) I'm a bit weary of your use of the term "best particle". Best in what sense? Do you mean maximum weight, i.e. maximum a posteriori estimate? More generally, the particle filter is a Bayesian data assimilation scheme that tries to estimate the posterior distribution, it is not an optimization algorithm.

I hope these comments and questions are helpful.
Good luck with your manuscript,
All the best,
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