CRSA - Postdoc position on high- resolution root zone soil moisture estimation by assimilation of microwave-derived surface soil moisture

Located at the heart of the future Green City of Benguerir, Mohammed VI Polytechnic University (UM6P), a higher education institution with international standards, is established to contribute to the development of Morocco and the African continent. Its vision is honed around research and innovation at the service of education and development. This unique nascent university, with its state-of-the-art campus and infrastructure, has woven a sound academic and research network, and its recruitment process is seeking high quality academics and professionals in order to boost its quality-oriented research environment in the metropolitan area of Marrakech.

CRSA is a transversal structure across several UM6P Programs. Research within the center is organized around several major areas that aim to ensure the challenging Food and Water security goal in Africa, with a special focus on developing methods/tools that use multi-source remotely sensed data. The research aims to improve our understanding of the integrated functioning of continental surfaces and their interaction with climate and humans, with emphasis on sustainable management of natural resources (soil, land, water, agriculture) in the context of Climate Change. One of center’s goals is to provide a set of services and operational products to users (local, national and international) that aid in the decision support of water and food systems.

Root zone soil moisture is an essential climate variable (ECV) that is crucial for characterizing the Earth’s climate. It is akey variable inhydrology,meteorologyandagronomy, regulating the exchange of water, energy and carbon between the land surface and the atmosphere. For agriculture, soil moisture in the root zone controls vegetation health, development, transpiration, biomassproduction and nutrient absorption. In particular, its estimation is essential for monitoring the vegetation's health and water status, and thus making decisions onirrigation scheduling and optimal application rates that, on one hand, ensure optimal crop hydration, promoting healthy root development and facilitating nutrient uptake and, consequently, maximizing crop yields. On theother hand,it enables efficient irrigation by applyingonlythe amounts neededat theright time,thus avoiding nutrient leaching, soil degradation and, above all, water wastage.

In contrast to in situ measurements, remote sensing data provide frequent, large-scale measurementsthatcanbeusedtoestimatesurfacevariablesofinterest.Inparticular,radardatacan nowadays be used to obtain maps of surface soil moisture at a high spatial resolution (a few meters), suitable for plot-scale applications. Surface soil moisture can in turn be used to estimate root zone soilmoisturevialandsurfacemodelsthatenablelinkingthesurfacetorootzoneprocesses.

Furthermore, combining radar-derived surface soil moisture maps with a land surface model using a data assimilationapproachhasthe potentialto produce dailyroot zone soilmoistureestimateswhile improving the accuracy and minimizing the errors of the land surface model. In this context, the objective of this position is to develop an approach combining an appropriate land surface model with radar-derived surface soil moisture product using sequential data assimilation for root zone soil moisture mapping at high spatial resolution.

• Assessmentofdifferentlandsurfacemodelsintermsofrootzonesoilmoistureestimation
• Combinealandsurfacemodelwithanappropriatesequentialdataassimilationtechnique
• Assimilatesurfacesoilmoistureproducttoestimaterootzonesoilmoisture
• Participateinfielddatacollectionanddata analysis
• Satelliteimagesprocessing
• Publish research findings in peer-reviewed journals and present results at conferences andworkshops.
• ContributetothesupervisionofmasterandPhDstudents.

• Ph.D.inEarthSciences,RemoteSensing,Physics,Appliedmathematics,orrelatedfield.
• Strongbackgroundinlandsurfacemodeling,remotesensingdataanalysis,images processing, and geospatial analysis.
• Experience working with satellite imageries (e.g., Sentinel-1, Landsat, Sentinel-2) and other remote sensing data sources.
• ProficiencyinprogrammingMATLABandPythonfordataanalysisandalgorithm development.
• Knowledgeofdataassimilationtechniquesisavaluableadded.
• Excellentcommunicationskillsandabilitytoworkeffectivelyinacollaborativeresearch environment.