• Texte niveau 1
• Texte niveau 2
• Texte niveau 3
• Texte niveau 4
• Texte niveau 5
Nemesio RODRIGUEZ FERNANDEZ
C
Université de Toulouse, CNES, CNRS, INRA, IRD, UPS
Des réseaux de neurones pour l’exploitation de bases de données multi-sources : application à la
télédétection et à l’assimilation de données
Soil moisture remote sensing using passive microwaves
Cliquez pour modifier le style du titre
N. Rodríguez Fernández. Atelier Permanent de l’Observatoire Midi-Pyrénées, 7 nov 2017
Passive microwaves and soil moisture
Physical modeling, Radiative transfer, tau-omega model
Observation: brightness temperatures
Geo-physical variables: soil
moisture, vegetation optical depth, soil temperature, soil type, …
Kerr et al. (2012, IEEE Tran. Geo. Remote Sensing)
The drier the soil, the highest the brightness temperature (at constant
soil temperature)
Au commencement était la donnée ...
Machine Learning for SMOS
N.J. Rodriguez-Fernandez, P. Richaume, Y.H. Kerr
Neural networks to obtain an added value from a multi-source data base
Long-time series Multi-sensor synergy
Acknowledgements These studies have been supported by the ESA -ECMWF SMOS contract, ESA support To Science Element SMOS+Neural Network, ESA SMOS Soil Moisture Expert Support Laboratory contract , by CNES (Centre National d’Etudes Spatiales) TOSCA program and by the French SMOS ground segment (CATDS).
ESA LPS– May 2019
Input: AMSR-E multi-frequency brightness temperatures
Target: SMOS CATDS Level 3 soil moisture Goal: Construct long-time series for climate data records of soil moisture
Reference : Rodriguez-Fernandez et al.
2016, Remote Sensing Data dissemination: CATDS
Near-real-time products
Pure data-driven retrievals Application for data assimilation
Centre d’Etudes Spatiales de la BIOsphère (CESBIO) CNES, CNRS, U. Toulouse, IRD, Toulouse, France
Input: SMOS Near-Real-Time multi-incidence angle brightness temperatures and ECWMF model soil temperature
Target:: SMOS ESA Level 2 soil moisture Goal: To retrieve soil moisture from SMOS observation in near-real time (less than 3.5 hours after sensing)
Reference : Rodriguez-Fernandez et al.
2017, Hydrology and Earth System Sciences Data dissemination: EUMETSAT
EumetCast, ESA SMOS dissemination portal
SMOS
Terra/Aqua
MODIS, AMSR-E Metop
ASCAT
Integrated Forecast System
In Situ
measurements
ECOCLIMAP Sand/clay
Input: SMOS Near-Real-Time multi-incidence angle brightness temperatures
Target: ECMWF H-TESSEL soil moisture in the first soil layer (0-7 cm) Goal: To compute from SMOS observation a soil moisture compatible with ECMWF model simulations for efficient data assimilation
Reference : Rodriguez-Fernandez et al. 2019, preprints.org (submitted to Remote Sensing)
Data assimilation: SMOS neural network soil moisture will be
assimilated operationally at ECMWF from cycle 46r1 (June 2019) Input: SMOS Near-Real-Time multi-incidence angle
brightness temperatures, MODIS NDVI, ECWMF model soil temperature
Target: in situ measurements of soil moisture
Goal: To retrieve soil moisture from SMOS observation with a neural network trained on in situ measurements. Retrieval independent of land surface models or radiation transfer computations
Reference : Rodriguez-Fernandez et al. 2017, IGARSS
Input: SMOS Near-Real-Time multi-incidence angle brightness
temperatures, MODIS NDVI, ECOCLIMAP sand/clay fraction, ASCAT backscattering
Target: ECMWF H-TESSEL soil moisture in the first soil layer (0-7 cm) Goal: To retrieve soil moisture extracting the synergies from passive (SMOS) and active (ASCAT) microwaves observations
Reference : Rodriguez-Fernandez et al. 2015, IEEE TGARS
Abstract
The Soil Moisture and Ocean Salinity (SMOS) satellite is an interferometric passive radiometer launched by ESA in 2009. After three year acquiring data, in 2012, the ESA Support to Science Element « SMOS + Neural Network » project started to study the use of machine learning techniques, in particular neural networks (NNs), to process SMOS data to estimate soil moisture. Since then, several
applications have been studied and successfully implemented operationally in the framework of different subsequent projects. All of these projects have in common the use of neural networks to exploit the synergies from multi-sources data bases containing data from passive microwave radiometers at different frequencies, active microwave scatterometers, optical/infrared reflectivities, numerical weather
prediction models, in situ measurements and static maps of soil and vegetation properties.
Nous avons beaucoup de données de sources diverses pour tester des techniques d’apprentissage automatique
D’abord il faut co-localiser les données dans le temps et dans l’espace: interpolations,
agrégation (résolutions différentes), re-échantillonnages,… souvent cette étape demande plus de temps que la phase d’apprentissage elle-même
Données:
• micro-ondes
passives et actives 1.4, 5.5, 10… GHz
• visible/infrarouge
• modèles transfert de rayonnement
• modèles de surface
• cartes statiques
• mesures de terrain
Données:
• pour établir des critères de filtrage
• comme données d’entrée
• pour tester les sorties du modèle statistique
RT models
N. Rodríguez Fernández. Rencontres R&D Meteo France, 18 Juin 2019
Statistical retrievals using Neural Networks
NN soil moisture
Soil moisture examples
• SMOS Level 2 SM
• Radiation transfer models
• Surface models
• In situ measurements
Adapt NN weights
Test different input data
Training: comparison and new modeling step if needed
Input data: SMOS TBs, AMSR-E TBs, ASCAT s , MODIS NDVI,…
Depends on the goal !
Feed forward network
Apprentissage supervisé en regression
Data filtering and sampling : training, validation, test
Multi-layer perceptron, network structure, best input data … Gradient backpropagation
Minimization of the MSE and regularization
Credit figure: Dreyfus et al. 2008, Ed. Eyrolles
Feature extraction
Output errors
Machine Learning for SMOS
N.J. Rodriguez-Fernandez, P. Richaume, Y.H. Kerr
Neural networks to obtain an added value from a multi-source data base
Long-time series Multi-sensor synergy
Acknowledgements These studies have been supported by the ESA -ECMWF SMOS contract, ESA support To Science Element SMOS+Neural Network, ESA SMOS Soil Moisture Expert Support Laboratory contract , by CNES (Centre National d’Etudes Spatiales) TOSCA program and by the French SMOS ground segment (CATDS).
ESA LPS– May 2019
Input: AMSR-E multi-frequency brightness temperatures
Target: SMOS CATDS Level 3 soil moisture Goal: Construct long-time series for climate data records of soil moisture
Reference : Rodriguez-Fernandez et al.
2016, Remote Sensing Data dissemination: CATDS
Near-real-time products
Pure data-driven retrievals Application for data assimilation
Centre d’Etudes Spatiales de la BIOsphère (CESBIO) CNES, CNRS, U. Toulouse, IRD, Toulouse, France
Input: SMOS Near-Real-Time multi-incidence angle brightness temperatures and ECWMF model soil temperature
Target:: SMOS ESA Level 2 soil moisture Goal: To retrieve soil moisture from SMOS observation in near-real time (less than 3.5 hours after sensing)
Reference : Rodriguez-Fernandez et al.
2017, Hydrology and Earth System Sciences Data dissemination: EUMETSAT
EumetCast, ESA SMOS dissemination portal
SMOS
Terra/Aqua
MODIS, AMSR-E Metop
ASCAT
Integrated Forecast System
In Situ
measurements
ECOCLIMAP Sand/clay
Input: SMOS Near-Real-Time multi-incidence angle brightness temperatures
Target: ECMWF H-TESSEL soil moisture in the first soil layer (0-7 cm) Goal: To compute from SMOS observation a soil moisture compatible with ECMWF model simulations for efficient data assimilation
Reference : Rodriguez-Fernandez et al. 2019, preprints.org (submitted to Remote Sensing)
Data assimilation: SMOS neural network soil moisture will be
assimilated operationally at ECMWF from cycle 46r1 (June 2019) Input: SMOS Near-Real-Time multi-incidence angle
brightness temperatures, MODIS NDVI, ECWMF model soil temperature
Target: in situ measurements of soil moisture
Goal: To retrieve soil moisture from SMOS observation with a neural network trained on in situ measurements. Retrieval independent of land surface models or radiation transfer computations
Reference : Rodriguez-Fernandez et al. 2017, IGARSS
Input: SMOS Near-Real-Time multi-incidence angle brightness
temperatures, MODIS NDVI, ECOCLIMAP sand/clay fraction, ASCAT backscattering
Target: ECMWF H-TESSEL soil moisture in the first soil layer (0-7 cm) Goal: To retrieve soil moisture extracting the synergies from passive (SMOS) and active (ASCAT) microwaves observations
Reference : Rodriguez-Fernandez et al. 2015, IEEE TGARS
Abstract
The Soil Moisture and Ocean Salinity (SMOS) satellite is an interferometric passive radiometer launched by ESA in 2009. After three year acquiring data, in 2012, the ESA Support to Science Element « SMOS + Neural Network » project started to study the use of machine learning techniques, in particular neural networks (NNs), to process SMOS data to estimate soil moisture. Since then, several
applications have been studied and successfully implemented operationally in the framework of different subsequent projects. All of these projects have in common the use of neural networks to exploit the synergies from multi-sources data bases containing data from passive microwave radiometers at different frequencies, active microwave scatterometers, optical/infrared reflectivities, numerical weather
prediction models, in situ measurements and static maps of soil and vegetation properties.
Machine Learning for SMOS
N.J. Rodriguez-Fernandez, P. Richaume, Y.H. Kerr
Neural networks to obtain an added value from a multi-source data base
Long-time series Multi-sensor synergy
Acknowledgements These studies have been supported by the ESA -ECMWF SMOS contract, ESA support To Science Element SMOS+Neural Network, ESA SMOS Soil Moisture Expert Support Laboratory contract , by CNES (Centre National d’Etudes Spatiales) TOSCA program and by the French SMOS ground segment (CATDS).
ESA LPS– May 2019
Input: AMSR-E multi-frequency brightness temperatures
Target: SMOS CATDS Level 3 soil moisture Goal: Construct long-time series for climate data records of soil moisture
Reference : Rodriguez-Fernandez et al.
2016, Remote Sensing Data dissemination: CATDS
Near-real-time products
Pure data-driven retrievals Application for data assimilation
Centre d’Etudes Spatiales de la BIOsphère (CESBIO) CNES, CNRS, U. Toulouse, IRD, Toulouse, France
Input: SMOS Near-Real-Time multi-incidence angle brightness temperatures and ECWMF model soil temperature
Target:: SMOS ESA Level 2 soil moisture Goal: To retrieve soil moisture from SMOS observation in near-real time (less than 3.5 hours after sensing)
Reference : Rodriguez-Fernandez et al.
2017, Hydrology and Earth System Sciences Data dissemination: EUMETSAT
EumetCast, ESA SMOS dissemination portal
SMOS
Terra/Aqua
MODIS, AMSR-E Metop
ASCAT
Integrated Forecast System
In Situ
measurements
ECOCLIMAP Sand/clay
Input: SMOS Near-Real-Time multi-incidence angle brightness temperatures
Target: ECMWF H-TESSEL soil moisture in the first soil layer (0-7 cm) Goal: To compute from SMOS observation a soil moisture compatible with ECMWF model simulations for efficient data assimilation
Reference : Rodriguez-Fernandez et al. 2019, preprints.org (submitted to Remote Sensing)
Data assimilation: SMOS neural network soil moisture will be assimilated operationally at ECMWF from cycle 46r1 (June 2019) Input: SMOS Near-Real-Time multi-incidence angle
brightness temperatures, MODIS NDVI, ECWMF model soil temperature
Target: in situ measurements of soil moisture
Goal: To retrieve soil moisture from SMOS observation with a neural network trained on in situ measurements. Retrieval independent of land surface models or radiation transfer computations
Reference : Rodriguez-Fernandez et al. 2017, IGARSS
Input: SMOS Near-Real-Time multi-incidence angle brightness temperatures, MODIS NDVI, ECOCLIMAP sand/clay fraction, ASCAT backscattering
Target: ECMWF H-TESSEL soil moisture in the first soil layer (0-7 cm) Goal: To retrieve soil moisture extracting the synergies from passive (SMOS) and active (ASCAT) microwaves observations
Reference : Rodriguez-Fernandez et al. 2015, IEEE TGARS
Abstract
The Soil Moisture and Ocean Salinity (SMOS) satellite is an interferometric passive radiometer launched by ESA in 2009. After three year acquiring data, in 2012, the ESA Support to Science Element « SMOS + Neural Network » project started to study the use of machine learning techniques, in particular neural networks (NNs), to process SMOS data to estimate soil moisture. Since then, several
applications have been studied and successfully implemented operationally in the framework of different subsequent projects. All of these projects have in common the use of neural networks to exploit the synergies from multi-sources data bases containing data from passive microwave radiometers at different frequencies, active microwave scatterometers, optical/infrared reflectivities, numerical weather
prediction models, in situ measurements and static maps of soil and vegetation properties.
Machine Learning for SMOS
N.J. Rodriguez-Fernandez, P. Richaume, Y.H. Kerr
Neural networks to obtain an added value from a multi-source data base
Long-time series Multi-sensor synergy
Acknowledgements These studies have been supported by the ESA -ECMWF SMOS contract, ESA support To Science Element SMOS+Neural Network, ESA SMOS Soil Moisture Expert Support Laboratory contract , by CNES (Centre National d’Etudes Spatiales) TOSCA program and by the French SMOS ground segment (CATDS).
ESA LPS– May 2019
Input: AMSR-E multi-frequency brightness temperatures
Target: SMOS CATDS Level 3 soil moisture Goal: Construct long-time series for climate data records of soil moisture
Reference : Rodriguez-Fernandez et al.
2016, Remote Sensing Data dissemination: CATDS
Near-real-time products
Pure data-driven retrievals Application for data assimilation
Centre d’Etudes Spatiales de la BIOsphère (CESBIO) CNES, CNRS, U. Toulouse, IRD, Toulouse, France
Input: SMOS Near-Real-Time multi-incidence angle brightness temperatures and ECWMF model soil temperature
Target:: SMOS ESA Level 2 soil moisture Goal: To retrieve soil moisture from SMOS observation in near-real time (less than 3.5 hours after sensing)
Reference : Rodriguez-Fernandez et al.
2017, Hydrology and Earth System Sciences Data dissemination: EUMETSAT
EumetCast, ESA SMOS dissemination portal
SMOS
Terra/Aqua
MODIS, AMSR-E Metop
ASCAT
Integrated Forecast System
In Situ
measurements
ECOCLIMAP Sand/clay
Input: SMOS Near-Real-Time multi-incidence angle brightness temperatures
Target: ECMWF H-TESSEL soil moisture in the first soil layer (0-7 cm) Goal: To compute from SMOS observation a soil moisture compatible with ECMWF model simulations for efficient data assimilation
Reference : Rodriguez-Fernandez et al. 2019, preprints.org (submitted to Remote Sensing)
Data assimilation: SMOS neural network soil moisture will be assimilated operationally at ECMWF from cycle 46r1 (June 2019) Input: SMOS Near-Real-Time multi-incidence angle
brightness temperatures, MODIS NDVI, ECWMF model soil temperature
Target: in situ measurements of soil moisture
Goal: To retrieve soil moisture from SMOS observation with a neural network trained on in situ measurements. Retrieval independent of land surface models or radiation transfer computations
Reference : Rodriguez-Fernandez et al. 2017, IGARSS
Input: SMOS Near-Real-Time multi-incidence angle brightness temperatures, MODIS NDVI, ECOCLIMAP sand/clay fraction, ASCAT backscattering
Target: ECMWF H-TESSEL soil moisture in the first soil layer (0-7 cm) Goal: To retrieve soil moisture extracting the synergies from passive (SMOS) and active (ASCAT) microwaves observations
Reference : Rodriguez-Fernandez et al. 2015, IEEE TGARS
Abstract
The Soil Moisture and Ocean Salinity (SMOS) satellite is an interferometric passive radiometer launched by ESA in 2009. After three year acquiring data, in 2012, the ESA Support to Science Element « SMOS + Neural Network » project started to study the use of machine learning techniques, in particular neural networks (NNs), to process SMOS data to estimate soil moisture. Since then, several
applications have been studied and successfully implemented operationally in the framework of different subsequent projects. All of these projects have in common the use of neural networks to exploit the synergies from multi-sources data bases containing data from passive microwave radiometers at different frequencies, active microwave scatterometers, optical/infrared reflectivities, numerical weather
prediction models, in situ measurements and static maps of soil and vegetation properties.
Machine Learning for SMOS
N.J. Rodriguez-Fernandez, P. Richaume, Y.H. Kerr
Neural networks to obtain an added value from a multi-source data base
Long-time series Multi-sensor synergy
Acknowledgements These studies have been supported by the ESA -ECMWF SMOS contract, ESA support To Science Element SMOS+Neural Network, ESA SMOS Soil Moisture Expert Support Laboratory contract , by CNES (Centre National d’Etudes Spatiales) TOSCA program and by the French SMOS ground segment (CATDS).
ESA LPS– May 2019
Input: AMSR-E multi-frequency brightness temperatures
Target: SMOS CATDS Level 3 soil moisture Goal: Construct long-time series for climate data records of soil moisture
Reference : Rodriguez-Fernandez et al.
2016, Remote Sensing Data dissemination: CATDS
Near-real-time products
Pure data-driven retrievals Application for data assimilation
Centre d’Etudes Spatiales de la BIOsphère (CESBIO) CNES, CNRS, U. Toulouse, IRD, Toulouse, France
Input: SMOS Near-Real-Time multi-incidence angle brightness temperatures and ECWMF model soil temperature
Target:: SMOS ESA Level 2 soil moisture Goal: To retrieve soil moisture from SMOS observation in near-real time (less than 3.5 hours after sensing)
Reference : Rodriguez-Fernandez et al.
2017, Hydrology and Earth System Sciences Data dissemination: EUMETSAT
EumetCast, ESA SMOS dissemination portal
SMOS
Terra/Aqua
MODIS, AMSR-E Metop
ASCAT
Integrated Forecast System
In Situ
measurements
ECOCLIMAP Sand/clay
Input: SMOS Near-Real-Time multi-incidence angle brightness temperatures
Target: ECMWF H-TESSEL soil moisture in the first soil layer (0-7 cm) Goal: To compute from SMOS observation a soil moisture compatible with ECMWF model simulations for efficient data assimilation
Reference : Rodriguez-Fernandez et al. 2019, preprints.org (submitted to Remote Sensing)
Data assimilation: SMOS neural network soil moisture will be assimilated operationally at ECMWF from cycle 46r1 (June 2019) Input: SMOS Near-Real-Time multi-incidence angle
brightness temperatures, MODIS NDVI, ECWMF model soil temperature
Target: in situ measurements of soil moisture
Goal: To retrieve soil moisture from SMOS observation with a neural network trained on in situ measurements. Retrieval independent of land surface models or radiation transfer computations
Reference : Rodriguez-Fernandez et al. 2017, IGARSS
Input: SMOS Near-Real-Time multi-incidence angle brightness temperatures, MODIS NDVI, ECOCLIMAP sand/clay fraction, ASCAT backscattering
Target: ECMWF H-TESSEL soil moisture in the first soil layer (0-7 cm) Goal: To retrieve soil moisture extracting the synergies from passive (SMOS) and active (ASCAT) microwaves observations
Reference : Rodriguez-Fernandez et al. 2015, IEEE TGARS
Abstract
The Soil Moisture and Ocean Salinity (SMOS) satellite is an interferometric passive radiometer launched by ESA in 2009. After three year acquiring data, in 2012, the ESA Support to Science Element « SMOS + Neural Network » project started to study the use of machine learning techniques, in particular neural networks (NNs), to process SMOS data to estimate soil moisture. Since then, several
applications have been studied and successfully implemented operationally in the framework of different subsequent projects. All of these projects have in common the use of neural networks to exploit the synergies from multi-sources data bases containing data from passive microwave radiometers at different frequencies, active microwave scatterometers, optical/infrared reflectivities, numerical weather
prediction models, in situ measurements and static maps of soil and vegetation properties.
Machine Learning for SMOS
N.J. Rodriguez-Fernandez, P. Richaume, Y.H. Kerr
Neural networks to obtain an added value from a multi-source data base
Long-time series Multi-sensor synergy
Acknowledgements These studies have been supported by the ESA -ECMWF SMOS contract, ESA support To Science Element SMOS+Neural Network, ESA SMOS Soil Moisture Expert Support Laboratory contract , by CNES (Centre National d’Etudes Spatiales) TOSCA program and by the French SMOS ground segment (CATDS).
ESA LPS– May 2019
Input: AMSR-E multi-frequency brightness temperatures
Target: SMOS CATDS Level 3 soil moisture Goal: Construct long-time series for climate data records of soil moisture
Reference : Rodriguez-Fernandez et al.
2016, Remote Sensing
Data dissemination: CATDS
Near-real-time products
Pure data-driven retrievals Application for data assimilation
Centre d’Etudes Spatiales de la BIOsphère (CESBIO) CNES, CNRS, U. Toulouse, IRD, Toulouse, France
Input: SMOS Near-Real-Time multi-incidence angle brightness temperatures and ECWMF model soil temperature
Target:: SMOS ESA Level 2 soil moisture Goal: To retrieve soil moisture from SMOS observation in near-real time (less than 3.5 hours after sensing)
Reference : Rodriguez-Fernandez et al.
2017, Hydrology and Earth System Sciences Data dissemination: EUMETSAT
EumetCast, ESA SMOS dissemination portal
SMOS
Terra/Aqua
MODIS, AMSR-E Metop
ASCAT
Integrated Forecast System
In Situ
measurements
ECOCLIMAP Sand/clay
Input: SMOS Near-Real-Time multi-incidence angle brightness temperatures
Target: ECMWF H-TESSEL soil moisture in the first soil layer (0-7 cm) Goal: To compute from SMOS observation a soil moisture compatible with ECMWF model simulations for efficient data assimilation
Reference : Rodriguez-Fernandez et al. 2019, preprints.org (submitted to Remote Sensing)
Data assimilation: SMOS neural network soil moisture will be
assimilated operationally at ECMWF from cycle 46r1 (June 2019) Input: SMOS Near-Real-Time multi-incidence angle
brightness temperatures, MODIS NDVI, ECWMF model soil temperature
Target: in situ measurements of soil moisture
Goal: To retrieve soil moisture from SMOS observation with a neural network trained on in situ measurements. Retrieval independent of land surface models or radiation transfer computations
Reference : Rodriguez-Fernandez et al. 2017, IGARSS
Input: SMOS Near-Real-Time multi-incidence angle brightness
temperatures, MODIS NDVI, ECOCLIMAP sand/clay fraction, ASCAT backscattering
Target: ECMWF H-TESSEL soil moisture in the first soil layer (0-7 cm) Goal: To retrieve soil moisture extracting the synergies from passive (SMOS) and active (ASCAT) microwaves observations
Reference : Rodriguez-Fernandez et al. 2015, IEEE TGARS
Abstract
The Soil Moisture and Ocean Salinity (SMOS) satellite is an interferometric passive radiometer launched by ESA in 2009. After three year acquiring data, in 2012, the ESA Support to Science Element « SMOS + Neural Network » project started to study the use of machine learning techniques, in particular neural networks (NNs), to process SMOS data to estimate soil moisture. Since then, several
applications have been studied and successfully implemented operationally in the framework of different subsequent projects. All of these projects have in common the use of neural networks to exploit the synergies from multi-sources data bases containing data from passive microwave radiometers at different frequencies, active microwave scatterometers, optical/infrared reflectivities, numerical weather
prediction models, in situ measurements and static maps of soil and vegetation properties.
Machine Learning for SMOS
N.J. Rodriguez-Fernandez, P. Richaume, Y.H. Kerr
Neural networks to obtain an added value from a multi-source data base
Long-time series Multi-sensor synergy
Acknowledgements These studies have been supported by the ESA -ECMWF SMOS contract, ESA support To Science Element SMOS+Neural Network, ESA SMOS Soil Moisture Expert Support Laboratory contract , by CNES (Centre National d’Etudes Spatiales) TOSCA program and by the French SMOS ground segment (CATDS).
ESA LPS– May 2019
Input: AMSR-E multi-frequency brightness temperatures
Target: SMOS CATDS Level 3 soil moisture Goal: Construct long-time series for climate data records of soil moisture
Reference : Rodriguez-Fernandez et al.
2016, Remote Sensing
Data dissemination: CATDS
Near-real-time products
Pure data-driven retrievals Application for data assimilation
Centre d’Etudes Spatiales de la BIOsphère (CESBIO)
CNES, CNRS, U. Toulouse, IRD, Toulouse, France
Input: SMOS Near-Real-Time multi-incidence angle brightness temperatures and ECWMF model soil temperature
Target:: SMOS ESA Level 2 soil moisture Goal: To retrieve soil moisture from SMOS observation in near-real time (less than 3.5 hours after sensing)
Reference : Rodriguez-Fernandez et al.
2017, Hydrology and Earth System Sciences Data dissemination: EUMETSAT
EumetCast, ESA SMOS dissemination portal
SMOS
Terra/Aqua
MODIS, AMSR-E Metop
ASCAT
Integrated Forecast System
In Situ
measurements
ECOCLIMAP Sand/clay
Input: SMOS Near-Real-Time multi-incidence angle brightness temperatures
Target: ECMWF H-TESSEL soil moisture in the first soil layer (0-7 cm) Goal: To compute from SMOS observation a soil moisture compatible with ECMWF model simulations for efficient data assimilation
Reference : Rodriguez-Fernandez et al. 2019, Remote Sensing) Data assimilation: SMOS neural network soil moisture will be
assimilated operationally at ECMWF from cycle 46r1 (11 June 2019) Input: SMOS Near-Real-Time multi-incidence angle
brightness temperatures, MODIS NDVI, ECWMF model soil temperature
Target: in situ measurements of soil moisture
Goal: To retrieve soil moisture from SMOS observation with a neural network trained on in situ measurements. Retrieval independent of land surface models or radiation transfer computations
Reference : Rodriguez-Fernandez et al. 2017, IGARSS
Input: SMOS Near-Real-Time multi-incidence angle brightness
temperatures, MODIS NDVI, ECOCLIMAP sand/clay fraction, ASCAT backscattering
Target: ECMWF H-TESSEL soil moisture in the first soil layer (0-7 cm) Goal: To retrieve soil moisture extracting the synergies from passive (SMOS) and active (ASCAT) microwaves observations
Reference : Rodriguez-Fernandez et al. 2015, IEEE TGARS
Abstract
The Soil Moisture and Ocean Salinity (SMOS) satellite is an interferometric passive radiometer launched by ESA in 2009. After three year acquiring data, in 2012, the ESA Support to Science Element « SMOS + Neural Network » project started to study the use of machine learning techniques, in particular neural networks (NNs), to process SMOS data to estimate soil moisture. Since then, several
applications have been studied and successfully implemented operationally in the framework of different subsequent projects. All of these projects have in common the use of neural networks to exploit the synergies from multi-sources data bases containing data from passive microwave radiometers at different frequencies, active microwave scatterometers, optical/infrared reflectivities, numerical weather
prediction models, in situ measurements and static maps of soil and vegetation properties.
RT models
Application: near real time products
• Texte niveau 1
• Texte niveau 2
• Texte niveau 3
• Texte niveau 4
• Texte niveau 5
Problématique: Les centres opérationnels de météorologie ont besoin de l’humidité du sol en temps quasi-réel (moins de trois heures)
Solution proposée: Modèle inverse par réseaux de neurones
Produit officiel distribué par
Projet SMOS Near Real-Time
Algorithme statistique: 100 fois plus rapide que l’algorithme physique !
Temperature Sol (Modèle ECMWF)
Humidité du sol
Rodriguez-Fernandez et al. (2017, Hydrology and Earth System Sciences)
Satellite Soil Moisture Validation and Application Workshop Wien September 19-20 2017-- YHKerr
• Slightly better performances than the original L2 SM data
• Much faster ! Available in near real time for operational applications
Implemented by : With support by :
Delivered to :
Disseminated by:
Near Real Time SM
Extreme events: floods
SMOS Near Real Time soil moisture
2019 Floods in Australia
Application : data assimilation
• Texte niveau 1
• Texte niveau 2
• Texte niveau 3
• Texte niveau 4
• Texte niveau 5
Rodriguez-Fernandez et al.
(2019, Remote Sensing)
Application : data assimilation
• Texte niveau 1
• Texte niveau 2
• Texte niveau 3
• Texte niveau 4
• Texte niveau 5
Projet SMOS Data Assimilation
Rodriguez-Fernandez et al.
(2019, Remote Sensing)
SMOS NN SM data assimilation in meteorological models
T air 850 hPa, forecast 36 hours, July-September SMOS NN SM improves the forecast
Blue: positive impact
Red: negative impact
▪ SMOS soil moisture obtained with neural networks trained on ECMWF models
▪ Surface-only DA system forced by ERA-Interim reanalysis
▪ Atmospheric forecast using the analyzed surface fields
▪ Rodriguez-Fernandez, de Rosnay, Albergel et al. (2019, Remote Sensing)
Assimilation of SMOS NN SM,
Tair 2m, RH 2m Assimilation of
Tair 2m, RH 2m
Atmospheric forecasts: T 2m
Version May 22, 2019 submitted to Remote Sens. 19 of 23
Figure 9. Upper panel: difference of the Pearson correlation of NNSM experiment time series with respect to in situ measurements and the Pearson correlation of OL experiment time series with respect to in situ measurements. Lower panel: difference of the RMS of NNSM experiment time series with respect to in situ measurements and RMS of OL experiment time series with respect to in situ measurements. In both cases, blue points show that the analysed time series are closer to the in situ measurement (higher correlation and lower RMS).
Figure 10. Evaluation of the atmospheric forecast skill. The plots show the RMSE of the T 2m forecast
for the assimilation experiments minus the RMSE for the OL experiment as a function of the forecast
day in two periods : April-June (upper panels) and July-September (lower panels). The results are shown
for three latitude ranges: [-90 , -20 ] (left panels), [-20 , 20 ] (middle panels), [20 , 90 ] (right panels).
5
thSSMVA GMU Fairfax October 24-25 2018 – Yann H. KERR
Operational data assimilation
• SMOS Neural Network SM operationally assimilated at
ECMWF since 11 June 2019
Application: multi-sensor synergy
• Texte niveau 1
• Texte niveau 2
• Texte niveau 3
• Texte niveau 4
• Texte niveau 5
Problématique: Les capteurs passifs SMOS et actifs ASCAT peuvent être complémentaires mais il n’y pas de modèles physiques passif à1.4 GHz et actif à 5 GHz
Solution proposée: Modèle inverse par réseaux de neurones pour utiliser la synergie des deux capteurs
Projet
L’utilisation simultanée des deux capteurs capture mieux la
dynamique de l’humidité du sol
Rodriguez-Fernandez et al 2015 (IEEE Trans. Geoscience and Remote Sensing)
Application: climate data records
• Texte niveau 1
• Texte niveau 2
• Texte niveau 3
• Texte niveau 4
• Texte niveau 5
Projet Fusion AMSR-E/SMOS Climate Change Initiative
Rodriguez-Fernandez et al 2016 (Remote Sensing)
Problématique: L’humidité du sol est une variable climatique essentielle … mais les satellites ont une durée de vie limitée
Solution proposée: Créer des series temporelles longues en utilisant plusieurs satellites et des réseaux de neurones
Produit distribué par le
Longues series temporelles et teleconnections
NN CCI
B. Cluzet
Master Thesis
ULID and SMOS-HR
SMOS-HR
• Interferometric L-band radiometer
• Full polarization
• Multi-angular capabilities
• 10 km spatial resolution
• Phase 0 study at CNES
(CESBIO, Airbus Defence and Space)
ULID
• Unconnected L-band Interferometer Demonstrator
• 3 nano-satellites
• Phase A study at CNES
Deep learning for image reconstruction (visibilities inversion)
Summary
Classical multilayer perceptrons are useful for many
scientific and operational applications in particular dealing with radiometry and not image processing
They can help to extract an added value from multi-source/
hybrid data bases … and they are simple
We are currently using similar techniques to retrieve
vegetation parameters such as biomass or for downscaling coarse resolution soil moisture estimations
@SMOS_satellite, @NemesioRF
nemesio.rodriguez@cesbio.cnes.fr
23
