Post-doc : Estimation of beet yellowing severity and propagation by satellite image time series

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INRAE presentation

The French National Research Institute for Agriculture, Food, and Environment (INRAE) is a major player in research and innovation. It is a community of 12,000 people with 272 research, experimental research, and support units located in 18 regional centres throughout France. Internationally, INRAE is among the top research organisations in the agricultural and food sciences, plant and animal sciences, as well as in ecology and environmental science. It is the world’s leading research organisation specialising in agriculture, food and the environment. INRAE’s goal is to be a key player in the transitions necessary to address major global challenges. Faced with a growing world population, climate change, resource scarcity, and declining biodiversity, the Institute has a major role to play in building solutions and supporting the necessary acceleration of agricultural, food and environmental transitions.

Work environment, missions and activities

Work environment, missions and activities
This position is included in the BEET-SAT project (PNRI-C), located in DYNAFOR lab, in collaboration with BioSP (UR INRAE).

This post-doc in remote sensing aim to detect the beet yellowing propagation over time at parcel and intra-parcel scales with different satellite image time series (SITS). The three main steps of the work are:

 (1) to understand the spectral behavior of beet over a growing season for several years, based on spectral indices of Sentinel-2 that highlight leaf pigment content, leaf structure and water content, all of which are supposed to respond to symptoms of beet yellowing viruses. Water stress also create identical symptoms on beets and it will be important to dissociate these two causes to create a solid database.   
 (2) to evaluate the impact of the spatial resolution of the SITS on the detection and differentiation of the stress and disease. In a first step, native Sentinel-2 time series will be compared to a super-resolved version at 5m based on the SISR architecture (Lac et al. 2023) and the SEN2VENμS dataset (Michel et al. 2022). This evaluation will be carried out over two years and if the results are positives, time series will be generated over the 2017-2026 period. Then, other sensors such as the PlanetScope constellation (approximately 3m) and Pléiades NEO (30 cm) will be tested. The results will be compared to several additional acquisitions from unmanned aerial vehicle (UAV) technologies.

(3) to study the benefit of combining satellite observations and simulation outputs that estimate evapotranspiration and water balance of the crops.

Training and skills

PhD or equivalent
 - PhD in remote-sensing, data science or image processing
- Skills in conceptual mechanistic and spatial modelling are desirable, and knowledge of agronomy would also be appreciated.
-A strong experience in Python programming and a good knowledge of machine learning libraries such as scikit-learn.
-Good interpersonal skills and open to multidisciplinarity

INRAE's life quality

By joining our teams, you benefit from (depending on the type of contract and its duration):

- up to 30 days of annual leave + 15 days "Reduction of Working Time" (for a full time);
- parenting support: CESU childcare, leisure services;
- skills development systems: training, career advise;
- social support: advice and listening, social assistance and loans;
- holiday and leisure services: holiday vouchers, accommodation at preferential rates;
- sports and cultural activities;
- collective catering.

How to apply

I send my CV and my motivation letter