Châteauneuf-Grasse, France
Châteauneuf-Grasse, France

Time filter

Source Type

Hungershoefer K.,French National Institute for Agricultural Research | Hungershoefer K.,German Weather Service | Peylin P.,French National Institute for Agricultural Research | Chevallier F.,French National Institute for Agricultural Research | And 6 more authors.
Atmospheric Chemistry and Physics | Year: 2010

In the context of rising greenhouse gas concentrations, and the potential feedbacks between climate and the carbon cycle, there is an urgent need to monitor the exchanges of carbon between the atmosphere and both the ocean and the land surfaces. In the so-called top-down approach, the surface fluxes of CO2 are inverted from the observed spatial and temporal concentration gradients. The concentrations of CO2 are measured in-situ at a number of surface stations unevenly distributed over the Earth while several satellite missions may be used to provide a dense and better-distributed set of observations to complement this network. In this paper, we compare the ability of different CO2 concentration observing systems to constrain surface fluxes. The various systems are based on realistic scenarios of sampling and precision for satellite and in-situ measurements.
It is shown that satellite measurements based on the differential absorption technique (such as those of SCIAMACHY, GOSAT or OCO) provide more information than the thermal infrared observations (such as those of AIRS or IASI). The OCO observations will provide significantly better information than those of GOSAT. A CO2 monitoring mission based on an active (lidar) technique could potentially provide an even better constraint. This constraint can also be realized with the very dense surface network that could be built with the same funding as that of the active satellite mission. Despite the large uncertainty reductions on the surface fluxes that may be expected from these various observing systems, these reductions are still insufficient to reach the highly demanding requirements for the monitoring of anthropogenic emissions of CO2 or the oceanic fluxes at a spatial scale smaller than that of oceanic basins. The scientific objective of these observing system should therefore focus on the fluxes linked to vegetation and land ecosystem dynamics. © 2010 Author(s).

Aubert M.,French National Center for Space Studies | Baghdadi N.N.,IRSTEA | Zribi M.,CNRS Center for the Study of the Biosphere from Space | Ose K.,IRSTEA | And 4 more authors.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2013

TerraSAR-X data are processed for an 'operational' mapping of bare soils moisture in agricultural areas. Empirical relationships between TerraSAR-X signal and soil moisture were established and validated over different North European agricultural study sites. The results show that the mean error on the soil moisture estimation is less than 4% regardless of the TerraSAR-X configuration (incidence angle, polarization) and the soil surface characteristics (soil surface roughness, soil composition). Furthermore, the potential of TerraSAR-X data (signal, texture features) to discriminate bare soils from other land cover classes in an agricultural watershed was evaluated. The mean signal backscattered from bare soils can be easily differentiated from signals from other land cover classes when the neighboring plots are covered by fully developed crops. This was observed regardless of the TerraSAR-X configuration and the soil moisture conditions. When neighboring plots are covered by early growth crops, a TerraSAR-X image acquired under wet conditions can be useful for discriminating bare soils. Bare soil masks were calculated by object-oriented classifications of mono-configuration TerraSAR-X data. The overall accuracies of the bare soils mapping were higher than 84% for validation based on object and pixel. The bare soils mapping method and the soil moisture relationships were applied to TerraSAR-X images to generate soil moisture maps. The results show that TerraSAR-X sensors provide useful data for monitoring the spatial variations of soil moisture at the within-plot scale. The methods of bare soils moisture mapping developed in this paper can be used in operational applications in agriculture, and hydrology. © 2008-2012 IEEE.

Kuppel S.,CEA Saclay Nuclear Research Center | Peylin P.,CEA Saclay Nuclear Research Center | Peylin P.,French National Institute for Agricultural Research | Chevallier F.,CEA Saclay Nuclear Research Center | And 3 more authors.
Biogeosciences | Year: 2012

Assimilation of in situ and satellite data in mechanistic terrestrial ecosystem models helps to constrain critical model parameters and reduce uncertainties in the simulated energy, water and carbon fluxes. So far the assimilation of eddy covariance measurements from flux-tower sites has been conducted mostly for individual sites ("single-site" optimization). Here we develop a variational data assimilation system to optimize 21 parameters of the ORCHIDEE biogeochemical model, using net CO 2 flux (NEE) and latent heat flux (LE) measurements from 12 temperate deciduous broadleaf forest sites. We assess the potential of the model to simulate, with a single set of inverted parameters, the carbon and water fluxes at these 12 sites. We compare the fluxes obtained from this "multi-site" (MS) optimization to those of the prior model, and of the "single-site" (SS) optimizations. The model-data fit analysis shows that the MS approach decreases the daily root-mean-square difference (RMS) to observed data by 22%, which is close to the SS optimizations (25% on average). We also show that the MS approach distinctively improves the simulation of the ecosystem respiration (R eco), and to a lesser extent the gross primary productivity (GPP), although we only assimilated net CO 2 flux. A process-oriented parameter analysis indicates that the MS inversion system finds a unique combination of parameters which is not the simple average of the different SS sets of parameters. Finally, in an attempt to validate the optimized model against independent data, we observe that global-scale simulations with MS optimized parameters show an enhanced phase agreement between modeled leaf area index (LAI) and satellite-based observations of normalized difference vegetation index (NDVI). © 2012 Author(s).

De Meij A.,Noveltis | Bossioli E.,National and Kapodistrian University of Athens | Penard C.,Noveltis | Vinuesa J.F.,Noveltis | Price I.,Noveltis
Atmospheric Environment | Year: 2015

The goal of this study is to investigate the impact of the high resolution Shuttle Radar Topography Mission (SRTM) 90m×90m topography data, together with the 100m×100m resolution Corine Land Cover 2006 on the simulated gas and particulate matter (PM10) concentrations by WRF-Chem. We focused our analysis on the well-known highly urbanized region of the Po Valley. Large differences are found in the geographical distribution of the land cover classes between Corine Land Cover and 30 arc seconds USGS. The simulation with the SRTM and Corine Land Cover increases modelled temperature at 2m and reduces wind speeds due to more friction at the surface induced by the Corine Land Cover. Latent and sensible heat fluxes show large differences between the two simulations and the related boundary layer development and depth. The simulation with the SRTM and Corine Land Cover favours the precipitation amount over a large of part the Alps and follows the pattern of the difference in topography between the two topography data sets. In term of air quality indicators, impacts are also large and geographical dependent. Monthly average of CO, NO and SO2 concentrations over a large part of the Po Valley are higher when using Corine Land Cover, up to ~20, ~50 and ~55%, respectively. With respect to PM10, the impacts are also geographical dependent. Over the Po valley area, calculated PM10 concentrations are in general higher using Corine Land Cover (up to 6.7ug/m3 [~26%] westerly of Milan) while differences are smaller over the Alps (~0.25ug/m3 [~20%]). Although the scope of this work is not to evaluate the model performance in calculated meteorological parameters and gas and PM10 concentrations, calculated values by the simulation with SRTM and Corine Land Cover show a better agreement with the observations than the simulation with the USGS topography and land cover data sets. A quantitative comparison between modelled and observed monthly average PM10 concentrations shows that both simulations underestimate the observed PM10 concentrations by a factor ~4. The agreement is much better during episodes for the simulation with the SRTM and Corine Land Cover. For CO, SO2 and NOx, the modelled monthly mean concentrations are similar for the two simulations. Larger differences are found during some episodes and regions with the SRTM and Corine LC simulation being in better agreement with the observations. © 2014 Elsevier Ltd.

Horwath M.,LEGOS | Horwath M.,TU Munich | Horwath M.,French National Center for Space Studies | Lemoine J.-M.,French National Center for Space Studies | And 2 more authors.
Journal of Geodesy | Year: 2011

The GRACE (Gravity Recovery and Climate Experiment) satellite mission relies on the inter-satellite K-band microwave ranging (KBR) observations. We investigate systematic errors that are present in the Level-1B KBR data, namely in the geometric correction. This correction converts the original ranging observation (between the two KBR antennas phase centers) into an observation between the two satellites' centers of mass. It is computed from data on the precise alignment between both satellites, that is, between the lines joining the center of mass and the antenna phase center of either satellite. The Level-1B data used to determine this alignment exhibit constant biases as large as 1-2 mrad in terms of pitch and yaw alignment angles. These biases induce non-constant errors in the Level-1B geometric correction. While the precise origin of the biases remains to be identified, we are able to estimate and reduce them in a re-calibration approach. This significantly improves time-variable gravity field solutions based on the CNES/GRGS processing strategy. Empirical assessments indicate that the systematic KBR data errors have previously induced gravity field errors on the level of 6-11 times the so-called GRACE baseline error level. The zonal coefficients (from degree 14) are particularly affected. The re-calibration reduces their rms errors by about 50%. As examples for geophysical inferences, the improvement enhances agreement between mass variations observed by GRACE and in-situ ocean bottom pressure observations. The improvement also importantly affects estimates of inter-annual mass variations of the Antarctic ice sheet. © 2010 Springer-Verlag.

Ramillien G.,French National Center for Scientific Research | Biancale R.,French National Center for Space Studies | Gratton S.,National Polytechnic Institute of Toulouse | Vasseur X.,European Center for Research and Advanced Training in Scientific Computation | Bourgogne S.,NOVELTIS
Journal of Geodesy | Year: 2011

We propose an unconstrained approach to recover regional time-variations of surface mass anomalies using Level-1 Gravity Recovery and Climate Experiment (GRACE) orbit observations, for reaching spatial resolutions of a few hundreds of kilometers. Potential differences between the twin GRACE vehicles are determined along short satellite tracks using the energy integral method (i.e., integration of orbit parameters vs. time) in a quasi-inertial terrestrial reference frame. Potential differences residuals corresponding mainly to changes in continental hydrology are then obtained after removing the gravitational effects of the known geophysical phenomena that are mainly the static part of the Earth's gravity field and time-varying contributions to gravity (Sun, Moon, planets, atmosphere, ocean, tides, variations of Earth's rotation axis) through ad hoc models. Regional surface mass anomalies are restored from potential difference anomalies of 10 to 30-day orbits onto 1° continental grids by regularization techniques based on singular value decomposition. Error budget analysis has been made by considering the important effects of spectrum truncation, the time length of observation (or spatial coverage of the data to invert) and for different levels of noise. © 2011 Springer-Verlag.

Ramillien G.L.,Toulouse 1 University Capitole | Ramillien G.L.,French National Center for Scientific Research | Seoane L.,Toulouse 1 University Capitole | Frappart F.,Toulouse 1 University Capitole | And 5 more authors.
Surveys in Geophysics | Year: 2012

We propose a "constrained" least-squares approach to estimate regional maps of equivalent-water heights by inverting GRACE-based potential anomalies at satellite altitude. According to the energy integral method, the anomalies of difference of geopotential between the two GRACE vehicles are derived from along-track K-Band Range-Rate (KBRR) residuals that correspond mainly to the continental water storage changes, once a priori known accelerations (i. e. static field, polar movements, atmosphere and ocean masses including tides) are removed during the orbit adjustment process. Newton's first law merely enables the Difference of Potential Anomalies from accurate KBRR data and the equivalent-water heights to be recovered. Spatial constraints versus spherical distance between elementary surface tiles are introduced to stabilize the linear system to cancel the effects of the north-south striping. Unlike the "mascons" approach, no basis of orthogonal functions (e. g., spherical harmonics) is used, so that the proposed regional method does not suffer from drawbacks related to any spectrum truncation. Time series of 10-day regional maps over South America for 2006-2009 also prove to be consistent with independent data sets, namely the outputs of hydrological models, "mascons" and global GRACE solutions. © 2012 Springer Science+Business Media B.V.

Aubert M.,IRSTEA | Baghdadi N.,IRSTEA | Zribi M.,CNRS Center for the Study of the Biosphere from Space | Douaoui A.,Roche Holding AG | And 4 more authors.
Remote Sensing of Environment | Year: 2011

Soils play a key role in shaping the environment and in risk assessment. We characterized the soils of bare agricultural plots using TerraSAR-X (9.5. GHz) data acquired in 2009 and 2010. We analyzed the behavior of the TerraSAR-X signal for two configurations, HH-25° and HH-50°, with regard to several soil conditions: moisture content, surface roughness, soil composition and soil-surface structure (slaking crust).The TerraSAR-X signal was more sensitive to soil moisture at a low (25°) incidence angle than at a high incidence angle (50°). For high soil moisture (>25%), the TerraSAR-X signal was more sensitive to soil roughness at a high incidence angle (50°) than at a low incidence angle (25°).The high spatial resolution of the TerraSAR-X data (1. m) enabled the soil composition and slaking crust to be analyzed at the within-plot scale based on the radar signal. The two loamy-soil categories that composed our training plots did not differ sufficiently in their percentages of sand and clay to be discriminated by the X-band radar signal.However, the spatial distribution of slaking crust could be detected when soil moisture variation is observed between soil crusted and soil without crust. Indeed, areas covered by slaking crust could have greater soil moisture and consequently a greater backscattering signal than soils without crust. © 2011 Elsevier Inc.

Camarero R.,French National Center for Space Studies | Delaunay X.,NOVELTIS | Thiebaut C.,French National Center for Space Studies
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2012

The huge improvements in resolution and dynamic range of current [1][2] and future CNES remote sensing missions (from 5m/2.5m in Spot5 to 70cm in Pleiades) illustrate the increasing need of efficient on-board image compressors. Many techniques have been considered by CNES during the last years in order to go beyond usual compression ratios: new image transforms or post-transforms [3][4], exceptional processing [5], selective compression [6]. However, even if significant improvements have been obtained, none of those techniques has ever contested an essential drawback in current on-board compression schemes: fixed-rate (or compression ratio). This classical assumption provides highly-predictable data volumes that simplify storage and transmission. But on the other hand, it demands to compress every image-segment (strip) of the scene within the same amount of data. Therefore, this fixed bit-rate is dimensioned on the worst case assessments to guarantee the quality requirements in all areas of the image. This is obviously not the most economical way of achieving the required image quality for every single segment. Thus, CNES has started a study to re-use existing compressors [7] in a Fixed-Quality/Variable bit-rate mode. The main idea is to compute a local complexity metric in order to assign the optimum bit-rate to comply with quality requirements. Consequently, complex areas are less compressed than simple ones, offering a better image quality for an equivalent global bit-rate. "Near-lossless bit-rate" of image segments has revealed as an efficient image complexity estimator. It links quality criteria and bit-rates through a single theoretical relationship. Compression parameters are thus automatically computed in accordance with the quality requirements. In addition, this complexity estimator could be implemented in a one-pass compression and truncation scheme. © 2012 SPIE.

De Meij A.,Noveltis | Vinuesa J.F.,Noveltis
Atmospheric Research | Year: 2014

The objective of this study is to evaluate the impact of the high resolution SRTM topography and Corine Land Cover data on simulated meteorological variables (wind speed at ten metres height, temperature at 2 m height and precipitation) in WRF. We compare the results with the WRF simulation using the standard 30-arc second USGS Land Cover and topography, and with observations of the ARPA network. We focus on the Lombardy region (north Italy) for the periods January-February and July-August 2008.Our analysis shows that simulated average wind speeds are in general lower by the WRF simulation with the SRTM and Corine Land Cover than the WRF simulation with the 30-arc second USGS and agrees better with the observations. The reason for this is that the Corine Land Cover shows a larger fraction of the 'urban and built-up' category than the USGS data set, which leads to more friction and higher roughness in the domain and lowers the wind speeds at ground level. For the winter period, the WRF simulation with the SRTM and Corine Land Cover calculates on average higher temperatures over the model domain (between ~0.2 and ~1.0°C and up to ~1.2°C for Milan) than the simulation using USGS data. For the summer period the differences in average temperatures are larger up to 2.7°C, while for Milan the differences are around 0.7°C. The differences are related to the higher fraction of urban and built-up area in the Corine Land Cover, which affects the sensible and latent heat fluxes in the model domain and holds the heat between the buildings. R2 values are on average a factor of 1.03 and 1.14 higher for the winter and summer periods, respectively. Comparing the hit rate statistics of the precipitation events reveals that probability of detection of the precipitation event and the Hansen-Kuipers score is on average 1% higher by the simulation with SRTM and Corine Land Cover than the WRF simulation with the standard USGS data set. © 2014 Elsevier B.V.

Loading NOVELTIS collaborators
Loading NOVELTIS collaborators