Post Doc in Evaluation of the potential of multispectral and hyperspectral satellite archives for topsoil and su Contrat : CDI

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Mohammed VI Polytechnic University is an institution dedicated to research and innovation in Africa and aims to position itself among world-renowned universities in its fields

The University is engaged in economic and human development and puts research and innovation at the forefront of African development. A mechanism that enables it to consolidate Morocco's frontline position in these fields, in a unique partnership-based approach and boosting skills training relevant for the future of Africa.

Located in the municipality of Benguerir, in the very heart of the Green City, Mohammed VI Polytechnic University aspires to leave its mark nationally, continentally, and globally


Lot 660, Hay Moulay Rachid, Ben Guerir 43150


Sustainable management of ecosystems services requires a quantitative understanding of soil and water quality. Soil and water functions understanding facilitate the geospatial management and decision making upon agroecosystems. For soil ecosystems, conventional methods of physicochemical properties measurement are time-consuming and require point scale measurements. Thus, mapping soil attributes at the farm or regional scale is limited. Remote sensing could provide effective methods for mapping physicochemical soil properties at different scales. Visible, near-infrared (Vis-NIR), and shortwave-infrared (SWIR) hyperspectral remote sensing has opened new horizons for assessing topsoil properties and producing digital soil maps. Several studies demonstrated that hyperspectral remote sensing can be used to estimate many soil attributes. Examples include soil C, soil texture, pH, and EC. However, soil remote sensing using satellite data presents many challenges including the atmospheric interaction with reflected electromagnetic radiation, surface roughness, and the nature of the land cover. However, with the unprecedented amount of satellite data (hyperspectral and multispectral), new opportunities arise for improving soil properties retrieval using time-series of satellite data and machine learning algorithms.

For surface water quality, inland water bodies are usually monitored through the measurement of physical, chemical, and microbiological attributes. In the last decade, variables such as nitrite-nitrogen, dissolved reactive phosphorus, and chlorophyll-a have been regularly monitored in thousands of marine ecosystems around the world. However, the large number of surface water bodies makes frequent monitoring a challenging task. Remote sensing using satellite data can supplement the efforts of in-situ observations and provide larger water quality data with less cost. This approach could provide an effective spatiotemporal assessment of water bodies across the world. Efforts in using spectral information for water quality assessment started in the 1970s. Previous research focused on quality variables such as chlorophyll-a, suspended sediments, water clarity, and dissolved organic carbon of inland water bodies including lakes, rivers, and nearshore ecosystems. However, the remote sensing approach is still challenging because of the complexity of the spectral features in these water bodies. Nevertheless, recent advances in inland water quality assessment showed unprecedented opportunities for water quality monitoring through pairing available remote sensing data with historical in situ measurements. If conveniently exploited, the open-access satellite data archives could potentially allow near real-time assessment of surface water bodies. Surface reflectance satellite data could provide an unmatched advantage with a reliable estimate of several properties of water bodies. This research project could be divided into two subprojects with different targets: i) soil and ii) water. The soil subproject has the following specific objectives.

  • Explore the full potential of multispectral and hyperspectral satellite archives for soil physicochemical properties (e.g. pH, carbon, clay, EC, phosphorus).
  • Evaluating the use of satellite time-series and machine learning algorithms for soil properties temporal dynamics (e.g. long term carbon losses).
  • Investigating the effect of surface biomass and soil moisture temporal dynamics on critical soil attributes (e.g. microbial activity, aggregate stability, organic carbon), using time-series of satellite data.

The water subproject has the following objectives:

  • Studying the usefulness of existing measured water quality databases in the development of remote approaches for near-real-time surface water quality monitoring.
  • Using multispectral time-series for surface water quality monitoring (e.g. nitrate-nitrogen, dissolved reactive phosphorus, and chlorophyll-a).
  • Determining the factors controlling the predictability of water quality parameters when using satellite multispectral data.

Profile recherché

Criteria of the candidate:
* PhD in the subject discipline

* Proven ability to perform multidisciplinary research and contribute to funded research programs.
* Demonstrated understanding of operational requirements for a successful research project and managing time and resources.
* Proven ability to identify and fulfil the academic writing requirements for development of proposal, reports and scientific publications.
* Good English communication skills, both verbal and writing are important.
* Ability to work both as part of a team and to work independently.

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