Annonce en lien avec l’Action/le Réseau : aucun

Laboratoire/Entreprise : CESBIO
Durée : 12 moia
Contact : nemesio.rodriguez@cesbio.cnes.fr
Date limite de publication : 2020-03-20

Contexte :
The SMOS (Soil Moisture and Ocean Salinity) satellite is a passive microwave interferometer that is providing L-band (1.4 GHz) observations of the Earth since 2009. SMOS is the first radiometer that performed systematic L-band measurements from the space. It has been followed by the NASA Soil Moisture Active Passive (SMAP) satellite, launched in 2015 and also carrying an L-Band radiometer. Both satellites have a maximum revisit time of 3 days at the equator.
The soil moisture content is an essential climate variable that has to be monitored over long periods of time (~30 years), however due to the limited lifetime of satellite missions, several satellites are needed to reach this goal. ESA has implemented a Climate Change Initiative (CCI) with a soil moisture chapter. Based on its expertise in using neural networks to retrieve soil moisture from multi-source data (Rodriguez-Fernandez et al. 2015, 2017, 2019), the passive radiometry team of CESBIO has shown that machine learning techniques such as neural networks can be used to build long time series using SMOS as reference to retrieve soil moisture from other sensors such as the ASMR-E radiometer (Rodriguez-Fernandez et al. 2016). This data set, which provides SMOS-like soil moisture data set since 2003, is a level 4 product distributed by CATDS.

Sujet :
The main goal of this position is to improve the long time series level 4 product by CATDS and to contribute to the ESA CCI project evaluating the added value of L-band sensors. Different methods to re-scale and to remove bias in time series obtained from different sensors should be studied. In addition, a new machine learning algorithm should be proposed to extend the time series back to the 80’s using data from other multifrequency radiometers such as TRMM/TMI (from 1997) and those of the SSM/I family (the first one was launched in 1978). The reference soil moisture dataset should be the Level 3 SMOS soil moisture product by CATDS or a merged SMOS/SMAP product. For this goal, a comparison of SMOS/SMAP brightness temperatures should be performed and a consistent retrieval algorithm will be proposed. Results of these analyses are opened for publication.

Profil du candidat :
The candidate should ideally have a PhD in applied mathematics or physics or an Engineering degree with experience in data science. Knowledge in remote sensing (ideally in microwave radiometry) is an asset. The applicant should also be rather autonomous, and creative. The ability to read and write technical documentation in English language is required. The post-doctoral fellow will be based in Toulouse but he will work in collaboration with other international scientists and ESA, meaning that the applicant should fully master English as as spoken language.
Salary will depend on qualifications and experience following the CNRS grids. Social security benefits are provided. Inquires and applications (resume and motivation letter) should be sent by email to the following contacts list:

Formation et compétences requises :
– Knowledge in Geo-physics and microwave sensors
– Statistics. Machine learning. Neural networks
– Array programming in Matlab or Python
– Knowledge in data science libraries
– Oral and writing communication skills
– Teamwork stills

Adresse d’emploi :
The CESBIO (Centre d’Etudes Spatiales de la Biosphère) is located in Toulouse and it is a joint research unit of Paul Sabatier University, the Centre National de la Recherche Scientifique (CNRS), the Centre National d’Etudes Spatiales (CNES), the Institut de Recherche pour le Développement (IRD) and the Institut national de recherche pour l’agriculture, l’alimentation et l’environnement (INRAE) . The laboratory aims at doing research in the domains of observation and modeling of the continental surfaces, addresses the interface between physical and biological sciences and participates in the specification of space missions and the treatment of remotely sensed data to develop the knowledge on continental biosphere dynamics and functioning at various temporal and spatial scales. CESBIO hosts the PIs for two European Space Agency (ESA) satellite missions (SMOS and Biomass missions) and for the French-Israeli Venus satellite.
The CESBIO is the lead « Expert Support Laboratory » for land applications with SMOS for ESA and « Expertise Center » for the SMOS French ground segment, the CATDS (Centre Aval de Traitement de Données SMOS) and it is a partner of the ESA Soil Moisture Climate Change Initiative consortium.

Document attaché : 2020-CDD-CATDS-3.pdf