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JECAM | Joint Experiment for Crop Assessment and Monitoring

France - OSR

Project Overview

The first aim of the OSR is to collect and organize in-situ and remotely-sensed data in order to serve pluridisciplinary research works on various environmental issues related to agriculture, biodiversity, water and carbon management....

The second aim of the OSR is to favor the development of tools and services for territories management through space technologies applications, falling within the framework of the "GMES downstream services". To achieve this goal, partnerships are built between laboratories, end-users (eg. local authorities, decision makers) and private companies. These partnerships are based on the sharing of information.


Project Reports

2017 Site Progress Report

2016 Site Progress Report

2015 Site Progress Report

2014 Site Progress Report

 

Publications:

 

  1. Bontemps, S.; Arias M.; Cara, C.; Dedieu, G; Guzzonato; E.; Hagolle, O; Inglada, J.; Matton, N; Morin, D.;…, ; Valero, S.; Koetz, B.; Defourny, P.(2015)  Building a Sentinel-2 like data set specifically dedicated to agriculture monitoring over 12 sites globally distributed. Remote Sensing, 7 (12) : p16062-16090.
     
  2. Valero, S., Morin, D., Inglada, J., Sepulcre, G., Arias, M., Hagolle, O., ... & Koetz, B. (2016). Production of a dynamic cropland mask by processing remote sensing image series at high temporal and spatial resolutions. Remote Sensing, 8(1), 55.
     
  3. Matton, N.;  Sepulcre Canto, G.;  Waldner, F.;Valero, S.; Morin, D; Inglada, J.; Arias, M.; Bontemps, S.; Koetz, B.; Defourny, P. (2015). Cropland Mapping Method along the season for Contrasted Agrosystems using High Resolution Time Series, Remote Sensing, 7(10),13208-13232
     
  4. Inglada, J., Arias, M., Tardy, B., Hagolle, O., Valero, S., Morin, D., ... & Koetz, B. (2015). Assessment of an operational system for crop type map production using high temporal and spatial resolution satellite optical imagery. Remote Sensing, 7(9), 12356-12379.
     
  5. X. Wu, N. Vuichard, P. Ciais, N. Viovy, N. de Noblet-Ducoudré, X. Wang, V. Magliulo, M. Wattenbach, L. Vitale, P. Di Tommasi, E. J. Moors, W. Jans, J. Elbers, E. Ceschia, T. Tallec, C. Bernhofer, T. Grünwald, C. Moureaux, T. Manise, A. Ligne, P. Cellier, B. Loubet, E. Larmanou, and D. Ripoche  (2015) ORCHIDEE-CROP (v0), a new process based Agro-Land Surface Model: model description and evaluation over Europe. Geoscience Model Development, 8, 4653-4696
     
  6. T. Sakai, T. Iizumi, M. Okada, M. Nishimori, T. Grünwald, J. Prueger, A. Cescatti, W. Korres, M. Schmidt, A. Carrara, B. Loubet, E.Ceschia (2015) Varying applicability of four different satellite-derived soil moisture products to global gridded crop model evaluation. Accepted in International Journal of Applied Earth Observation and GeoInformation, Special Issue: Advances in the Validation and Application of Remotely Sensed Soil Moisture
     
  7. M. Campioli, S. Vicca, S. Luyssaert, J. Bilcke, E. Ceschia, F. Chapin III, P. Ciais, M. Fernández-Martínez, Y. Malhi, M. Obersteiner, D. Olefeldt, D. Papale, S. Piao, J. Penuelas, P. Sullivan, X. Wang, T. Zenone, and I. Janssens (2015) Biomass production efficiency controlled by management in temperate and boreal ecosystems. Accepted in Nature Geoscience ([Paper #NGS-2014-11-02090C])
     
  8. M. Ferlicoq & E. Ceschia (2015). La gestion de l’albédo des surfaces cultivées représente un fort potentiel d’atténuation au réchauffement climatique dans Empreinte carbone : évaluer et agir par Bourges Bernard, Gourdon Thomas, Broc Jean-Sébastien 1, Paris : Presses des MINES, collection Développement durable, 2015. 386 pp.    ISBN : 9782356712332
     
  9. T. Tallec, P. Béziat, N. Jarosz, V. Rivalland & E. Ceschia (2013) Crops' water use efficiencies in temperate climate: comparison of stand, ecosystem and agronomical approaches. Agricultural and Forest Meteorology, 168, 69–81.
     
  10. P. Béziat, V. Rivalland, T. Tallec, N. Jarosz, G. Boulet, P. Gentine & E. Ceschia (2013) Crop evapotranspiration partitioning and analysis of the water budget distribution for several crop species: comparison between a simple method based on marginal distribution sampling and a SVAT modeling approach. Agricultural and Forest Meteorology, 177, 46– 56.
     
  11. S. Luyssaert, M. Jammet, P.C. Stoy, S. Estel, J. Pongratz, E. Ceschia, G. Churkina, A. Don, H. Erb, M. Ferlicoq, B. Gielen, T. Grünwald, R.A. Houghton, K. Klumpp, A. Knohl, T. Kolb, T. Kuemmerle, T. Laurila, A. Lohila, D. Loustau, M. J. McGrath, P. Meyfroidt, E. J. Moors, K. Naudts, K. Novick, J. Otto, K. Pilegaard, C. A. Pio, S. Rambal, C. Rebmann, J. Ryder, A. E. Suyker, A. Varlagin, M. Wattenbach & A.J Dolman (2014) Land management and land-cover change have impacts of similar magnitude on surface temperature. Nature Climate change, 4, 389–393.
     
  12. S. Ferrant, S. Gascoin, A. Veloso, J. Salmon-Monviola, M. Claverie, V. Rivalland, G. Dedieu, V. Demarez, E. Ceschia, J.-L. Probst, P. Durand & V. Bustillo (2014) Agro-hydrology and multi temporal high resolution remote sensing: toward an explicit spatial processes calibration. Hydrol. Earth Syst. Sci., 11, 7689-7732, doi:10.5194/hessd-11-7689-2014
     
  13. 2015, Baghdadi N., Zribi M., Paloscia S., Verhoest N.E.C., Lievens H., Baup F., and Mattia F. Semi-empirical Calibration of the Integral Equation Model for Co-polarized L-band Backscattering, Remote Sens. 2015, 7(10), 13626-13640; doi:10.3390/rs71013626.
     
  14. 2015, Ferlicoq M. & E. Ceschia (2015). La gestion de l’albédo des surfaces cultivées représente un fort potentiel d’atténuation au réchauffement climatique. Empreinte carbone : évaluer et agir (édité par l'Ecole des Mines).
     
  15. 2015, Gascoin, S. O. Hagolle, M. Huc, L. Jarlan, J.F. Dejoux, C. Szczypta, R. Marti, and R. Sanchez (2015) A snow cover climatology for the Pyrenees from MODIS snow products, Hydrol. Earth Syst. Sci., 19, 2337-2351, doi:10.5194/hess-19-2337-2015.
     
  16. 2015, Grusson Y., Xiaoling S., Gascoin S., Sauvage S., Raghavan S., Anctil F., Sanchez Perez J.M. (2015) Exploring snow and streamflow dynamics in an alpine watershed using the semi-distributed hydrological model SWAT. Journal of Hydrology, 531(3), 574–588, doi:10.1016/j.jhydrol.2015.10.070
     
  17. 2015, Hagolle, O.; Sylvander, S.; Huc, M.; Claverie, M.; Clesse, D.; Dechoz, C.; Lonjou, V.; Poulain, V (2015).   SPOT-4 (Take 5): Simulation of Sentinel-2 Time Series on 45 Large Sites. Remote Sens. 7, 12242-12264.
     
  18. 2015, Luyssaert et al. (2015).  « Beyond land cover change - Effects of contemporary land management on surface climate», Global Change Biology
     
  19. 2015, M. Campioli, S. Vicca, S. Luyssaert, J. Bilcke, E. Ceschia, F. Chapin III, P. Ciais, M. Fernández-Martínez, Y. Malhi, M. Obersteiner, D. Olefeldt, D. Papale, S. Piao, J. Penuelas, P. Sullivan, X. Wang, T. Zenone, & I. Janssens (2015) Biomass production efficiency controlled by management in temperate and boreal ecosystems.     Nature Geoscience 8, 843–846. doi:10.1038/ngeo2553. Cité 3 fois.
     
  20. 2015, Matteo Campioli, Sara Vicca, Sebastiaan Luyssaert, Joke Bilcke, Eric Ceschia, F. Chapin III, Philippe Ciais, Marcos Fernández-Martínez, Yadvinder Malhi, Michael Obersteiner, David Olefeldt, Dario Papale, Shilong Piao, Josep Penuelas, Patrick Sullivan, Xuhui Wang, Terenzio Zenone, and Ivan Janssens (2015) Biomass production efficiency controlled by management in temperate and boreal ecosystems" Accepté dans Nature Geoscience ([Paper #NGS-2014-11-02090C])
     
  21. 2015, R. Oltra-Carrió, F. Baup., S. Fabre, R. Fieuzal, and X. Briottet, "Improvement of soil moisture retrieval from hyperspectral VNIR-SWIR data using clay content information. From laboratory to field experiments", Remote Sensing, vol. 7, pp. 3184-3205, 2015, doi:10.3390/rs60x000x.
     
  22. 2015, Szczypta C, Gascoin S, Houet T, Hagolle O, Dejoux J-F, Vigneau C, Fanise, P. (2015) Impact of climate and land cover changes on snow cover in a small Pyrenean catchment, J. Hydrol., 521, 84-99, doi:10.1016/j.jhydrol.2014.11.060
     
  23. 2015, Toru Sakai, Toshichika Iizumi, Masashi Okada, Motoki Nishimori, Thomas Grünwald, John Prueger, Alessandro Cescatti, Wolfgang Korres, Marius Schmidt, Arnaud Carrara, Benjamin Loubet, Eric Ceschia (2015) Varying applicability of four different satellite-derived soil moisture products to global gridded crop model evaluation. Accepté dans International Journal of Applied Earth Observation and GeoInformation, Special Issue: Advances in the Validation and Application of Remotely Sensed Soil Moisture
     
  24. 2015, X. Wu, N. Vuichard, P. Ciais, N. Viovy, N. de Noblet-Ducoudré, X. Wang, V. Magliulo, M. Wattenbach, L. Vitale, P. Di Tommasi, E. J. Moors, W. Jans, J. Elbers, E. Ceschia, T. Tallec, C. Bernhofer, T. Grünwald, C. Moureaux, T. Manise, A. Ligne, P. Cellier, B. Loubet, E. Larmanou, and D. Ripoche (2015) ORCHIDEE-CROP (v0), a new process based Agro-Land Surface Model: model description and evaluation over Europe. Geosci. Model Dev. Discuss., 8, 4653-4696
     
  25. 2016, Baghdadi, N., Choker, M., Zribi, M., El Hajj, M., Paloscia, S., Verhoest, N.E.C., Lievens, H., Baup, F., & Mattia, F. (2016). A new empirical model for radar scattering from bare soil surfaces. Accepted in Remote Sensing. November 2016.
     
  26. 2016, Battude M., Al Bitar A., Brut A., Tallec T., Huc M., Cros J., Weber J.J., Lhuissier L., Simonneaux V., Demarez V. (2016) Modeling water needs and supplies of irrigated maize in the south west of France using high spatial and temporal resolution satellite imagery, Agricultural Water Management (under review)
     
  27. 2016, Battude M., Al Bitar A., Morin D., Cros J., Huc M., Marais Sicre C., Le Dantec V., Demarez V. (2016) Estimating maize biomass and yield over large area using high spatial and temporal resolution Sentinel-2 like remote sensing data, Remote Sensing of Environment 184, 668-681 doi: 10.1016/j.rse.2016.07.030
     
  28. 2016, Betbeder, J., Fieuzal, R., and Baup, F. Assimilation of LAI and Dry Biomass Data From Optical and SAR Images Into an Agro-Meteorological Model to Estimate Soybean Yield. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, PP, 1-14.
     
  29. 2017. Fieuzal R., C. Marais Sicre, F. Baup. (2017) Estimation of corn yield using multi-temporal optical and radar satellite data and artificial neural networks, International Journal of Applied Earth Observation and Geoinformation 57 (2017) 14–23.

Implementation Plans

In situ Data:

Over the experimental sites : Both Auradé and Lamasquère sites are ICOS sites and therefore biomass, soil humidity, meteorological, flux measurements... are standardised following the ICOS protocols. See http://gaia.agraria.unitus.it/icos/working-groups

In total, 135 micro-meteorological variables are recorded every 30 minutes at each site. They include air temperature and humidity, air pressure, soil temperature and humidity at 0-5, 5, 10, 30, 100 cm depth, soil heat flux at 5 cm depth, global (shortwave and longwave) and PAR incident radiation, global (shortwave and longwave) and PAR reflected radiation, albedo, transmitted PAR, diffuse PAR and global shortwave radiation, NDVI, PRI, surface temperature, soil CO2 and N2O fluxes (automatic chambers), net CO2, water, sensible heat fluxes by means of the eddy-covariance method... Details concerning biomass, LAI and soil humidity measurements are presented in the Table 2 of Annex 1.


Figure 4: The Lamasquère site from around the micrometeorological station.

 


Figure 5: The Auradé site from around the micrometeorological station

 

 Over the OSR area:

Additional measurements of LAI, biomass were performed over the OSR area. See Table 2 in Annex 1 for details. Also ground truth for Land Use were collected 4 times during the year on approximately 450 plots (see Figure 6).


Figure 6: Lande use Monitoring campaigns have been carried since 2006 with approximately 450 plots monitored each year (see yellow and red fields on the map below) and up to 1500 in 2015.

 

 

Plans for Next Growing Season:

Next growing season, will you maintain your current approach, or modify the approach?  If you plan to modify, please describe your new approach.

In the frame of the Sensagri and Bag’ages project (financed by the Adour Garonne Water Council), we plan to develop SAR data assimilation in our modelling approach of biomass, yield, C & water fluxes and budgets by using the SAFYE-CO2 model. We also plan to account for the effect of cover crops or crop regrowth on thoses fluxes and budgets.

Also in the frame of the Bag’ages project 9 micro-meteorological stations will be installed (see Figure 10), covering a soil (from very dark to clear) and climatic gradient from the south-west part of France till the Eastern Pyrenees. Those stations, installed on cropland with contrasted management, will allow us to monitor air/ sol surface temperature, air humidity, precipitations, soil water content profiles from surface to 1m deep, ETR, incoming/outgoing shortwave radiation (albedo), incoming/outgoing longwave radiations. Automatic cameras will be used to monitor daily crop phenology/soil status/crop management. Those measurements will be used to validate high resolution albedo GAI and SWC products (some of them being developed within the frame of SENSAGRI).

 

Finally, close to 1000 plots will be monitored 5 time a year to follow land cover (mainly on croplands) and crop management within the frame of the Sensagri project. Those data will be used for validation of dynamic high resolution land use maps.  

Figure 10: Location of the 9 micro-meteorological stations that will be installed on agricultural plots in the frame of the Bag'ages project and that will complete the OSR setup (Auradé & Lamaquère sites)


Site Description

Locations

France - Lamasquère
Site Extent   Centroid: 43.78, 1.40
Top left: , Bottom Right: ,

Location: South west of Toulouse, France (area of study is approx 50*50 km) including 2 experimental plots (Auradé and Lamasquère Fluxnet/ICOS sites)
Topography: hilly for Auradé, in a valley for Lamasquère
Soils: Clay at Auradé, Clay loam at Lamaquère
Drainage class/irrigation: irrigation at Lamasquère when maize is grown
Crop calendar: depends on crops
Field size: around 30 ha at Auradé and 20 ha at Lamasquère
Climate and weather: mean annual temperature around 13 °C, mean annual precipitation around 650 mm
Agricultural methods used: crop rotations are winter wheat, sunflower, winter wheat, rapeseed at Auradé and maize for silage, winter wheat at Lamasquère. Auradé only receives mineral fertilisers whereas Lamasquère receives both mineral and organic fertilisers. Lamasquère is irrigated when maize is grown.
Photograph(s):



Figure 1: Area of study of the Regional Space Observatory including the Auradé and the Lamasquère Fluxnet/ICOS sites (installed in 2004)
 


Figure 2: Formosat-2 image from the area around the Auradé experimental plot and its micrometeorological station on the 27th of May 2006.


Specific Project Objectives & Deliverables

Results

Within the frame of the MAISEO project: irrigated crops were mapped over the OSR area and water requirements were estimated with the SAFY-WB model at a regional scale. The model was calibrated and validated against ETR flux measurements performed at the Lamasquère experimental site (see Figure 7).



Figure 7: Evapotranspiration, Water needs, GAI (Green area index) and yield of maize (2006, Lamasquère) observed and modeled with SAFYE model (From M. Battude PhD Thesis, at CESBIO in 2017).
 


Within the frame of the frame of the CICC project: we used a large number of temporal Sentinel-1 together with Sentinel-2-like data to assess the potential of the Sentinel satellites for winter and summer crops monitoring. We applied an adapted multi-image filter to the Sentinel-1 images, taking advantage of the Sentinel-1 dense temporal series to reduce the speckle effect, while preserving the fine structure present in the image, like the crop fields boundaries. The time series of optical NDVI and radar backscatter (VH, VV and VH/VV) were analyzed and physically interpreted with the support of rainfall and temperature data, as well as the destructive in situ measurements (green area index (GAI) and fresh biomass, when available). We showed that dense time series allow to capture short phenological stages and thus to precisely describe various crop development. A better understanding of SAR backscatter and NDVI temporal behaviors under contrasting agricultural practices and environmental conditions will help many upcoming studies related to crop monitoring based on Sentinel-1 and -2, such as dynamic crop mapping and biophysical parameters estimation. Regarding crop mapping, we found that wheat and rapeseed could be better distinguished using VH and VV backscatters between March and July and using NDVI between November and December. Regarding summer crops, we recommend using VH/VV and VV to separate maize, soybean and sunflower during the heading/flowering phase. Results also showed that for barley and maize, both NDVI and VH/VV profiles are in good agreement with the destructive GAI and fresh biomass measurements. Thus, VH/VV ratio could be successfully used for biophysical parameters retrieval and direct biomass assimilation in crop models. VH/VV is also able to detect post-harvest spontaneous regrowth. This is a promising result for applications such as the monitoring of regrowth and intermediate crops for estimating soil carbon storage in the perspective of climate change mitigation.


Figure 8: Observations over winter wheat and rapeseed fields: temporal behavior of optical NDVI, radar VH/VV, VH, and VV, rainfalls and temperatures over winter crops, i.e. 64 wheat crops (in blue) and 10 rapeseed crops (in red). Mean values are represented by dots and standard deviations are represented by the filled color domains surrounding the curves. In the last plot (bottom), temperatures in red were measured at the Sentinel-1 acquisition time 6 a.m. The horizontal red line is the 0°C line. Vertical precipitation bars in blue are drawn in green the same days than Sentinel-1 acquisitions and in red the two days before Sentinel-1 acquisitions, assuming that wet soil due to rainfalls may still affect Sentinel-1 backscatter two days later. Vertical grey bars represent Sentinel-1 acquisition events.

 


Figure 9: Observations on maize, soybean and sunflower fields: temporal behavior of optical NDVI, radar VH/VV, VH, and VV, rainfalls and temperatures over summer crops, i.e. 57 maize crops (in blue), 8 soybean crops (in green) and 116 sunflower crops (in red). Mean values are represented by dots and standard deviations are represented by the filled color domains surrounding the curves. In the last plot (Bottom), temperatures in red were measured at the Sentinel-1 acquisition time 6 a.m. The horizontal red line is the 0°C line. Vertical precipitation bars in blue are drawn in green the same days than Sentinel-1 acquisitions and in red the two days before Sentinel-1 acquisitions, assuming that wet soil due to rainfalls may still affect Sentinel-1 backscatter two days later. Vertical grey bars represent Sentinel-1 acquisition events.


In Situ Observations

  1. Parameter: Biomass, soil humidity, meteorological, flux measurements
    Data Collection Protocol:

    In total, 135 micro-meteorological variables are recorded every 30 minutes at each site. They include air temperature and humidity, air pressure, soil temperature and humidity at 0-5, 5, 10, 30, 100 cm depth, soil heat flux at 5 cm depth, global (shortwave and longwave) and PAR incident radiation, global (shortwave and longwave) and PAR reflected radiation, albedo, transmitted PAR, diffuse PAR and global shortwave radiation, NDVI, PRI, surface temperature, soil CO2 and N2O fluxes (automatic chambers), net CO2, water, sensible heat fluxes by means of the eddy-covariance method.

    Frequency:
  2. Parameter: LAI, biomass
    Data Collection Protocol:

    Additional measurements of LAI, biomass were performed over the OSR area. See Table 2 in Annex 1 for details. Also ground truth for Land Use were collected 4 times during the year on approximately 450 plots 

    Frequency:

EO Data Requirements

Approximate Start Date of Acquisition: September 29 2016 (Landsat), January 5 2016 (Sentinel 2)
Approximate End Date of Acquisition: December 27 2016 (Landsat), October 21 2016 (Sentinel 2)
Spatial Resolution:
Temporal Frequency:
Latency of Data Delivery:
Wavelengths Required:
Across Swath:
Along Track:

SAR Data Requirements

Approximate Start Date of Acquisition: November 6 2014
Approximate End Date of Acquisition: December 7 2015
Spatial Resolution: 10 m
Temporal Frequency:
Latency of Data Delivery:
Wavelengths Required: C band
Polarization
Incidence Angle Restrictions:
Across Track:
Along Track:

Locations

France - Lamasquère

Centroid
Latitude: 43.78
Longitude: 1.40

Site Extent
Top left
Latitude:
Longitude: 1.40
Bottom Right
Latitude:
Longitude:


Optical Sensors

Formosat
Imaging Mode:
Spatial Resolution: 8m
Acquisition Frequency: 18 between Mar 2 and Dec 31, 2014
Pre-Processing Level:
Application:

Pléiade
Imaging Mode:
Spatial Resolution: 8m
Acquisition Frequency: 3 times
Pre-Processing Level:
Application:

JECAM | Joint Experiment for Crop Assessment and Monitoring | Group on Earth Observation

©2013 Joint Experiment for Crop Assessment and Monitoring © HER MAJESTY THE QUEEN IN RIGHT OF CANADA SA MAJESTE LA REINE DU CHEF DU CANADA (2012)