CRSA - Postdoctoral Position on High-resolution Root Zone Soil Moisture Estimation

UM6P

  • بن جرير
  • دائم
  • دوام كامل
  • قبل 3 أيام
CRSA - Postdoctoral position on high- resolution root zone soil moisture estimation by assimilation of microwave-derived surface soil moistureAbout UM6P: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.About CRSA: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.Job Description:Root zone soil moisture is an essential climate variable (ECV) that is crucial for characterizing the Earth's climate. It is a key variable in hydrology, meteorology and agronomy, 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, biomass production and nutrient absorption. In particular, its estimation is essential for monitoring the vegetation's health and water status, and thus making decisions on irrigation 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 the other hand, it enables efficient irrigation by applying only the amounts needed at the right 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 measurements that can be used to estimate surface variables of interest. In particular, radar data can 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 soil moisture via land surface models that enable linking the surface to root zone processes.Furthermore, combining radar-derived surface soil moisture maps with a land surface model using a data assimilation approach has the potential to produce daily root zone soil moisture estimates while 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.Key Responsibilities:
  • Assessment of different land surface models in terms of root zone soil moisture estimation
  • Combine a land surface model with an appropriate sequential data assimilation technique
  • Assimilate surface soil moisture product to estimate root zone soil moisture
  • Participate in field data collection and data analysis
  • Satellite images processing
  • Publish research findings in peer-reviewed journals and present results at conferences and workshops.
  • Contribute to the supervision of master and PhD students.
Qualifications:
  • Ph.D. in Earth Sciences, Remote Sensing, Physics, Applied mathematics, or related field.
  • Strong background in land surface modeling, remote sensing data analysis, images processing, and geospatial analysis.
  • Experience working with satellite imageries (e.g., Sentinel-1, Landsat, Sentinel-2) and other remote sensing data sources.
  • Proficiency in programming MATLAB and Python for data analysis and algorithm development.
  • Knowledge of data assimilation techniques is a valuable added.
  • Excellent communication skills and ability to work effectively in a collaborative research environment.

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