Research Engineer or PostDoc in remote sensing (M/F)

The P2S2 project (Products for SENTINEL-2) consists in developing and validating a processing chain to characterize crops from images acquired by the SENTINEL-2, LANDSAT-8 and Venµs satellites. The computed variables are: the leaf area index (LAI), the soil cover fraction or the fraction of photosynthetically active radiation absorbed by the vegetation (fAPAR). A first version of the prototype has been validated and the satellite image processing chains are now put into production by the THEIA continental surface data and services cluster to provide continuous images over France. Your mission will be twofold: validate THEIA products and prototype the next version of the biophysical variables estimation algorithm.

GEOV2-AVHRR: Monitoring Changes in Vegetation on a Global Scale over the last 38 Years

Long-term global monitoring of the terrestrial biosphere has raised an increasing interest in recent years. The Global Climate Observing System (GCOS 2010) identified a set of Essential Climate Variables (ECV) […]

You will be hosted in the CAPTE (CAPTeurs et TElédetection) Joint Technology Unit of UMR EMMAH, at the INRAE PACA center in Avignon. You will work in close collaboration with researchers and engineers of the UMT, as well as with its CNES contacts.

• More specifically, you will be in charge of:
  • Intercomparing and validating products from the operational processing chain of data from the three satellites by exploiting an existing database of ground-based data (P2S2 database).
  • Prototyping and evaluating the version 2 crop specific algorithm based on machine learning trained on P2S2 data. You will make comparisons with version 1 (neural networks trained on a database simulated thanks to the PROSAIL radiative transfer model) and evaluate the validity and limitations of the algorith

This 9 month CDD can be extended for two years with CNES funding for the DECADAY project (DECAmetric resolution for DAYly Observations) which aims to provide daily time series of biophysical variables from SENTINEL-2 & LANDSAT-8 optical data using SENTINEL-1 to improve gap filling in case of cloud cover (funding accepted).

For more information on the P2S2 project:  

Weiss, M., et al  (2019). The P2S2 Project: a validation database for SENTINEL-2 GAI and Chlorophyll products for crops In E.S. Agency (Ed.), Living Planet Symposium 2019. Milan, Italy

Weiss, M., et al  (2019). The p2s2 validation database for decametric resolution crop products: green area index, fraction of intercepted light, green fraction and chlorophyll content. In, IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019) (pp. 4588-4591). Yokohama, Japan

Training and skills

Master’s degree/Engineering degree

• Background: Digital science engineer with knowledge in remote sensing, image processing, model inversion, artificial intelligence, radiative transfer
• Skills : good knowledge in computing langages  (Python, Matlab, C++) and remote sensing and image analysis. Dynamic and collaborative team player, autonomy, good English oral and written communication