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

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.