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

Abstract

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.

Towards the assimilation of satellite reflectance into semi-distributed ensemble snowpack simulations

“…Furthermore, a spatially comprehensive assimilation of SCF would be needed beforehand to detect topographic classes where the ensemble and observations disagree on the presence of snow and assess where reflectance can be compared, similarly as in Baba et al (2018).…”

“…From MODIS and S2 spectral Top Of Atmosphere (TOA) radiance products, it is possible to retrieve the snowpack extent as a Snow Cover Fraction by pixel (SCF) and Bottom of Atmosphere (BOA) reflectances which requires to account for the complexity of the radiative transfer in mountainous area (Richter, 1998;Sirguey, 2009). Many studies focus on the assimilation of SCF, showing a strong impact of assimilation in hydrological models (De Lannoy et al, 2012;Thirel et al, 2013;Stigter et al, 2017;Aalstad et al, 2018;Baba et al, 2018). However, SCF is expected to be of less interest for detailed snowpack modelling in alpine terrain, because the information content is limited to the snow line (Andreadis and Lettenmaier, 2006;Toure et al, 2018).…”

“…Recently, Piazzi et al (2018) and Smyth et al (2019) applied the PF to a combination of meteorological and model ensembles, but with a less complex model and at the local scale as well. In parallel, spatialized application of PF has been done in several studies (Thirel et al, 2013;Baba et al, 2018), but with deterministic and low complexity snow models, not suited for avalanche hazard forecasting. This paper fills a gap by implementing a combination of a meteorological ensemble and a multiphysical system of detailed snow models in a spatialized context.…”

Theia Snow collection: high resolution operational snow cover maps from Sentinel-2 and Landsat-8 data

“…In spite of all these limitations, the Theia snow products have already been successfully used for the evaluation of the MODIS snow products (Masson et al, 2018) and the assimilation of snow cover area data into a snowpack model in the High Atlas (Baba et al, 2018). Given that the snow cover is a key driver of many natural processes in mountains regions, we envision various potential applications of the Theia snow products, including the modelling of the distribution of the permafrost in mountain regions, the validation and calibration of hydrological models in snow-dominated catchments, and the spatial modelling of biodiversity and productivity of ecosystems in mountain regions.…”

Performance Assessment of Optical Satellite-Based Operational Snow Cover Monitoring Algorithms in Forested Landscapes

“…Therefore, this investigation provides an opportunity to build upon the existing knowledge from the past analyses by further assessing the algorithms' performance in other regions to scrutinize the aspect of landscape diversity [22]. Furthermore, this evaluation is important for users of the HR-S&I service, particularly if the FSC products are to be assimilated into a snowpack model [33]. Due to the prevalence of mixed pixels in the forested landscapes, merely binarizing pixels into snow/snow-free can introduce errors while integrating the snow-covered area (SCA) information for hydro-climatological modeling [34].…”