Position opening: Using machine learning to build a... About CESBIO The CESBIO (Centre d’Etudes Spatiales de la Biosphère) is a joint research unit of Paul Sabatier University, the Centre National de la Recherche Scientifique (CNRS), the Centre National d'Etudes Spatiales (CNES) and the Institut de Recherche pour le Développement (IRD). The laboratory aims at doing research in the domains of observation and modeling...

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8+ years of soil moisture and ocean salinity data over... This animation was prepared by Dimitry Khvorostyanov from LOCEAN with SMOS data from CATDS (Soil moisture level 3 and Ocean salinity debiased V3) Enjoy

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Special Issue New Outstanding Results over Land from... from Amen Al-Yaari and Arnaud Mialon Call for publications Dear Colleagues, Surface soil moisture (the water content in the first centimeters of soil) is an essential climate variable that plays an important role in land–atmosphere interactions. Soil moisture is widely used in improving climate model predictions/projections, weather...

Readmore

A new debiased Seas surface Salinity map from LOCEAN New info from Jacqueline Boutin! A new version (version 3) of debiased SMOS SSS L3 maps generated by the LOCEAN CATDS expertise center is available at CATDS. This third version of Level 3 SMOS SSS corrected from systematic biases uses an improved ‘de-biasing’ technique: with respect to version 2, the adjustment of the long term mean SMOS SSS in very dynamical...

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Position opening: Using machine learning to build a climate data record of soil moisture

Category : Non classé

About CESBIO

The CESBIO (Centre d’Etudes Spatiales de la Biosphère) is a joint research unit of Paul Sabatier University, the Centre National de la Recherche Scientifique (CNRS), the Centre National d’Etudes Spatiales (CNES) and the Institut de Recherche pour le Développement (IRD). 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).

Scientific background

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, 2017a, 2017b, 2018), 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.

Aim of this work

The main goal of this position is to improve the long time series level 4 product by CATDS. By construction, the AMSR-E neural network product shows no global bias with respect to the Level 3 SMOS soil moisture CATDS product but local bias may exist. A systematic search will be done and several methods will be studied to correct possible local bias. In addition, a new machine learning algorithm will 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.

Expected profile of the applicants:

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 grid. Social security benefits are provided. Inquires and applications (resume and motivation letter) should be sent by e-mail before 31/Jan/2019 to the following contacts list:

Contacts:

Nemesio Rodriguez-Fernandez
CESBIO
18 avenue. Edouard Belin,
bpi 2801,
31401 Toulouse cedex 9
Tel: 05 61 55 85 22
email: nemesio.rodriguez@cesbio.cnes.fr

Philippe Richaume
CESBIO
18 avenue. Edouard Belin,
bpi 2801,
31401 Toulouse cedex 9
Tel: 05 61 55 74 87
email: philippe.richaume@cesbio.cnes.fr

References:

  • Rodríguez-Fernandez, N. J., de Rosnay, P., Albergel, C., Aires, F., Prigent, C., Richaume, P., Kerr, Y., & Drusch, M. (2018, July). SMOS Neural Network Soil Moisture data assimilation. In International Geoscience and Remote Sensing Symposium (IGARSS), 2018 IEEE (pp. 5548-5551)
  • Rodríguez-Fernández, N. J., de Souza, V., Kerr, Y. H., Richaume, P., & Al Bitar, A. (2017, July). Soil moisture retrieval using SMOS brightness temperatures and a neural network trained on in situ measurements. In International Geoscience and Remote Sensing Symposium (IGARSS), 2017b,IEEE (pp. 1574-1577)
  • Rodríguez-Fernández, N. J., Muñoz-Sabater, J.; Richaume, P.; Albergel, C.; de Rosnay, P. & Kerr, Y. H. Evaluation of the SMOS Near-Real-Time soil moisture, Hydrology and Earth System Sciences, 2017a, 21, 5201-5216

  • Rodriguez-Fernández, N. J., Kerr, Y. H., van der Schalie, R., Al-Yaari, A., Wigneron, J. P., de Jeu, R., et al. (2016). Long Term Global Surface Soil Moisture Fields Using an SMOS-Trained Neural Network Applied to AMSR-E Data. Remote Sensing, 8(11), 959.

  • Rodriguez-Fernandez, NJ, Aires F., Richaume, P., Kerr, Y. Prigent, C,, Kolassa, J., Cabot F. Jimenez, C., Mahmoodi, A., Drusch, M. (2015) Soil moisture retrieval using neural networks: application to SMOS, IEEE Transactions on Geoscience and Remote Sensing, 53(11), 5991-6007

8+ years of soil moisture and ocean salinity data over the globe from SMOS

Category : CATDS, L2, L3, Ocean

This animation was prepared by Dimitry Khvorostyanov from LOCEAN with SMOS data from CATDS (Soil moisture level 3 and Ocean salinity debiased V3)

Enjoy

Special Issue « New Outstanding Results over Land from the SMOS Mission »

Category : Data

from Amen Al-Yaari and Arnaud Mialon

Call for publications

Dear Colleagues,

Surface soil moisture (the water content in the first centimeters of soil) is an essential climate variable that plays an important role in land–atmosphere interactions. Soil moisture is widely used in improving climate model predictions/projections, weather forecasting, drought monitoring, rainfall estimations, etc.

Monitoring surface soil moisture at a global scale has recently become possible thanks to microwave remote sensing. SMOS (Soil Moisture and Ocean Salinity) was the first dedicated soil moisture mission that has been in orbit for eight years. The SMOS satellite was launched by the European Space Agency (ESA) in 2009, carrying on board a radiometer in the L-band frequency with a native spatial resolution of ~43 km. Since then, soil moisture and vegetation optical depth (VOD) have been retrieved from multi-angular brightness temperature observations relying mainly on a radiative transfer model.

This is a dedicated Special Issue on SMOS. We welcome studies on all subjects that are related to the SMOS satellite and its products.

Potential topics include, but are not limited to, the following:

  • the improvements in the soil moisture/VOD retrieval algorithms;
  • the evaluation/validation of the SMOS soil moisture and VOD products;
  • SMOS synergy with other remote sensing observations or models simulations;
  • SMOS soil moisture/VOD applications for agriculture, hydrology, etc.

Dr. Amen Al-Yaari
Dr. Arnaud Mialon
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI’s English editing service prior to publication or during author revisions.

http://www.mdpi.com/journal/remotesensing/special_issues/smos_rs

A new debiased Seas surface Salinity map from LOCEAN

Category : CATDS, Cal/Val, L2, Ocean

New info from Jacqueline Boutin!

A new version (version 3) of debiased SMOS SSS L3 maps generated by the
LOCEAN CATDS expertise center is available at CATDS.

This third version of Level 3 SMOS SSS corrected from systematic biases
uses an improved ‘de-biasing’ technique: with respect to version 2, the
adjustment of the long term mean SMOS SSS in very dynamical areas, like
in river plumes, and the bias correction at high latitudes have been
improved. See more information and data link HERE

These products will be presented at IGARSS next week (poster 3447).
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