The PhD position on “Multi-sensor Images Fusion for Snow Cover Mapping and streamflow prediction in the Moroccan Atlas basins” is funded for four years in the framework of the MorSnow project “Monitoring and Quantifying Snowmelt contribution for Moroccan Water Management strategies in the context of Climate Change” at the CRSA center, Mohammed VI Polytechnic University, Morocco.

Seasonal snow cover in high altitude basin in Morocco represent a key information to predict snowmelt runoff and thus for water supply management and regulation by national stakeholders. Recent advances in remote sensing provide opportunities for more reliably estimating and quantification of broad spatial and temporal variability in snow cover on a variety of scales. However, due to technical limitations, there is a trade-off between the spatial and temporal resolutions of currently available remotely satellite sensors. As a result, they are insufficient to capture snow evolution at spatial and temporal scales, especially in Atlas Mountains.

Based on these issues, the combined use of the next generation of multispectral sensor data from the Landsat-8 (L8), Sentinel-2 (S2) and Moderate-Resolution Imaging Spectroradiometer (MODIS) satellites provide unprecedented options for water related applications. In this context, the objective of this PhD proposal is to develop a novel approach to highlights the benefit of spatio-temporal fusion technics to increase revisit time and produce dense series with a high spatial and temporal resolution and asses their accuracy to forecast snowmelt runoff in the Tensift and Oum Er Rbia river basins.

Specifically, this work will be focused on how the synergistic use of optical and Radar sensors can effectively support research on snow hydrology in Morocco and while integrating snow index thresholding (NDSI) based image analysis. Classical fusion models (e.g., ESTARFM, FSDAF) with the adaptation of deep learning techniques will be assessed to merge multispectral sensor data (Sentinel-2, Landsat-8, and MODIS) of different spatial scales (10m, 30m, 250m or 500m), to address the need to predict a dense snow index time series at high spatial and temporal resolution. Automating the Spatio-temporal fusion processes via local and online analysis platforms (e.g., Google Earth Engine).

1. Experimental field work, including site instrumentation to measure meteo-hydrological and snow parameters in the study sites (Atlas mountains);
2. Analysis of a long datasets and develop scripts for operational use;
3. Snowmelt Runoff modelling;
4. Preparing peer-reviewed scientific publications and presentation for scientific conferences.

The ideal candidate should have completed a master or engineering degree either in geomatics, computer sciences, physics sciences, earth and environmental sciences or related disciplines.

The following skills are required :

• Affinity with computer programming tools (Python, Matlab, R);
• Good skills in mathematical modelling and long-term data analysis;
• Good knowledge of hydrological and climatic measurements;
• Knowledge of remote sensing and GIS techniques;
• Communicational skills and team spirit;
• Excellent written and oral English language skills including academic writing skills;
• Ability to work in collaboration with a multidisciplinary team both in the field and in the laboratory;
• Ability to carry out field experiments in mountainous environment.