Satellite radar backscattering coefficient σ0 data from ENVISAT-ASAR and Normalized Difference Ve... more Satellite radar backscattering coefficient σ0 data from ENVISAT-ASAR and Normalized Difference Vegetation Index (NDVI) data from SPOT-VEGETATION are assimilated in the STEP model of vegetation dynamics. The STEP model is coupled with a radiative transfer model of the radar backscattering and NDVI signatures of the soil and herbaceous vegetation. These models are driven by field data (rainfall time series, soil
Remote sensing estimation of evapotranspiration (ET) was done by combining remote sensing data an... more Remote sensing estimation of evapotranspiration (ET) was done by combining remote sensing data and the ISBA soil‐vegetation‐atmosphere transfer model over the Alpilles test site. We tested the possible use of low resolution data (∼ 1km) to derive leaf area index (LAI) at the field scale using a disaggregation method. Disaggregated LAI were then used as inputs of ISBA for monitoring ET for 9 months. Estimation of LAI and ET were first performed at high resolution for being used as reference for evaluating the use of low resolution data. ...
Received 30 September 2004; accepted 4 November 2004; published 20 January 2005. [1] This article... more Received 30 September 2004; accepted 4 November 2004; published 20 January 2005. [1] This article reports on a multiobjective approach which is carried out on the physically based Soil-Vegetation-Atmosphere Transfer (SVAT) model. This approach is designed for (1) ...
ABSTRACT This study fits within the overall research on the usage of space remote sensing data to... more ABSTRACT This study fits within the overall research on the usage of space remote sensing data to constrain land surface models (LSMs) ( also called SVAT models for soil - vegetation - atmosphere transfer). The goal of this paper is to analyze the potential of using thermal infrared (TIR) remote sensing data for LSM calibration. LSMs are characterized by a large number of parameters and initial conditions that have to be specified. This model calibration is generally performed at a local scale by minimization between measurements and time series difference. Recent studies have shed light on the use of multiobjective approaches for performing calibration and for analyzing the model's sensitivity to input parameters. Such an approach has been implemented in the SEtHyS LSM ( for "Suivi de l'Etat Hydrique des Sols," the French acronym for soil moisture monitoring) with the objective of assessing the information contributed by having knowledge of the remote sensing surface brightness temperature. For this purpose, the model calibration was performed in three different cases at field scale corresponding to different calibration design. The analysis of these numerical experiments permits the authors to show the contribution and the limits of TIR remote sensing data for LSM calibration, in various environmental conditions. The perspectives underline the potential of using a dynamic calibration methodology, taking advantage of the time-varying model parameters' influence.
Satellite radar backscattering coefficient σ0 data from ENVISAT-ASAR and Normalized Difference Ve... more Satellite radar backscattering coefficient σ0 data from ENVISAT-ASAR and Normalized Difference Vegetation Index (NDVI) data from SPOT-VEGETATION are assimilated in the STEP model of vegetation dynamics. The STEP model is coupled with a radiative transfer model of the radar backscattering and NDVI signatures of the soil and herbaceous vegetation. These models are driven by field data (rainfall time series, soil
Remote sensing estimation of evapotranspiration (ET) was done by combining remote sensing data an... more Remote sensing estimation of evapotranspiration (ET) was done by combining remote sensing data and the ISBA soil‐vegetation‐atmosphere transfer model over the Alpilles test site. We tested the possible use of low resolution data (∼ 1km) to derive leaf area index (LAI) at the field scale using a disaggregation method. Disaggregated LAI were then used as inputs of ISBA for monitoring ET for 9 months. Estimation of LAI and ET were first performed at high resolution for being used as reference for evaluating the use of low resolution data. ...
Received 30 September 2004; accepted 4 November 2004; published 20 January 2005. [1] This article... more Received 30 September 2004; accepted 4 November 2004; published 20 January 2005. [1] This article reports on a multiobjective approach which is carried out on the physically based Soil-Vegetation-Atmosphere Transfer (SVAT) model. This approach is designed for (1) ...
ABSTRACT This study fits within the overall research on the usage of space remote sensing data to... more ABSTRACT This study fits within the overall research on the usage of space remote sensing data to constrain land surface models (LSMs) ( also called SVAT models for soil - vegetation - atmosphere transfer). The goal of this paper is to analyze the potential of using thermal infrared (TIR) remote sensing data for LSM calibration. LSMs are characterized by a large number of parameters and initial conditions that have to be specified. This model calibration is generally performed at a local scale by minimization between measurements and time series difference. Recent studies have shed light on the use of multiobjective approaches for performing calibration and for analyzing the model's sensitivity to input parameters. Such an approach has been implemented in the SEtHyS LSM ( for "Suivi de l'Etat Hydrique des Sols," the French acronym for soil moisture monitoring) with the objective of assessing the information contributed by having knowledge of the remote sensing surface brightness temperature. For this purpose, the model calibration was performed in three different cases at field scale corresponding to different calibration design. The analysis of these numerical experiments permits the authors to show the contribution and the limits of TIR remote sensing data for LSM calibration, in various environmental conditions. The perspectives underline the potential of using a dynamic calibration methodology, taking advantage of the time-varying model parameters' influence.
Uploads