The Experts below are selected from a list of 273 Experts worldwide ranked by ideXlab platform

Cedric Bacour - One of the best experts on this subject based on the ideXlab platform.

  • neural Network Estimation of lai fapar fcover and lai cab from top of canopy meris reflectance data principles and validation
    Remote Sensing of Environment, 2006
    Co-Authors: Cedric Bacour, Frederic Baret, D Beal, Marie Weiss, K Pavageau
    Abstract:

    Abstract A neural Network is developed to operationally estimate biophysical variables over land surfaces from the observations of the ENVISAT-MERIS instrument: the leaf area index ( LAI ), the fraction of absorbed photosynthetically active radiation ( fAPAR ), the fraction of vegetation cover ( fCover ), and the canopy chlorophyll content ( LAI × C ab ). The neural Network requires as input the geometry of observation and the top of canopy reflectances, corrected from the atmospheric effects, in eleven spectral bands. It is trained on a reflectance database made of radiative transfer model simulations. The principles underlying the generation of the database and the design of the Network are first presented. The estimated variables are then compared to other existing products, LAI - and fAPAR -MODIS and MGVI -MERIS, and validated against ground measurements performed in the framework of the VALERI project. Results show remarkable consistency of the temporal dynamics between the several products with however some differences in the range of variation. When compared to actual VALERI ground measurements, the proposed algorithm shows the best performances for LAI (RMSE = 0.47) and fAPAR (RMSE = 0.09).

  • neural Network Estimation of lai fapar fcover and lai cab from top of canopy meris reflectance data principles and validation
    Remote Sensing of Environment, 2006
    Co-Authors: Cedric Bacour, Frederic Baret, D Beal, Marie Weiss, K Pavageau
    Abstract:

    Abstract A neural Network is developed to operationally estimate biophysical variables over land surfaces from the observations of the ENVISAT-MERIS instrument: the leaf area index ( LAI ), the fraction of absorbed photosynthetically active radiation ( fAPAR ), the fraction of vegetation cover ( fCover ), and the canopy chlorophyll content ( LAI × C ab ). The neural Network requires as input the geometry of observation and the top of canopy reflectances, corrected from the atmospheric effects, in eleven spectral bands. It is trained on a reflectance database made of radiative transfer model simulations. The principles underlying the generation of the database and the design of the Network are first presented. The estimated variables are then compared to other existing products, LAI - and fAPAR -MODIS and MGVI -MERIS, and validated against ground measurements performed in the framework of the VALERI project. Results show remarkable consistency of the temporal dynamics between the several products with however some differences in the range of variation. When compared to actual VALERI ground measurements, the proposed algorithm shows the best performances for LAI (RMSE = 0.47) and fAPAR (RMSE = 0.09).

K Pavageau - One of the best experts on this subject based on the ideXlab platform.

  • neural Network Estimation of lai fapar fcover and lai cab from top of canopy meris reflectance data principles and validation
    Remote Sensing of Environment, 2006
    Co-Authors: Cedric Bacour, Frederic Baret, D Beal, Marie Weiss, K Pavageau
    Abstract:

    Abstract A neural Network is developed to operationally estimate biophysical variables over land surfaces from the observations of the ENVISAT-MERIS instrument: the leaf area index ( LAI ), the fraction of absorbed photosynthetically active radiation ( fAPAR ), the fraction of vegetation cover ( fCover ), and the canopy chlorophyll content ( LAI × C ab ). The neural Network requires as input the geometry of observation and the top of canopy reflectances, corrected from the atmospheric effects, in eleven spectral bands. It is trained on a reflectance database made of radiative transfer model simulations. The principles underlying the generation of the database and the design of the Network are first presented. The estimated variables are then compared to other existing products, LAI - and fAPAR -MODIS and MGVI -MERIS, and validated against ground measurements performed in the framework of the VALERI project. Results show remarkable consistency of the temporal dynamics between the several products with however some differences in the range of variation. When compared to actual VALERI ground measurements, the proposed algorithm shows the best performances for LAI (RMSE = 0.47) and fAPAR (RMSE = 0.09).

  • neural Network Estimation of lai fapar fcover and lai cab from top of canopy meris reflectance data principles and validation
    Remote Sensing of Environment, 2006
    Co-Authors: Cedric Bacour, Frederic Baret, D Beal, Marie Weiss, K Pavageau
    Abstract:

    Abstract A neural Network is developed to operationally estimate biophysical variables over land surfaces from the observations of the ENVISAT-MERIS instrument: the leaf area index ( LAI ), the fraction of absorbed photosynthetically active radiation ( fAPAR ), the fraction of vegetation cover ( fCover ), and the canopy chlorophyll content ( LAI × C ab ). The neural Network requires as input the geometry of observation and the top of canopy reflectances, corrected from the atmospheric effects, in eleven spectral bands. It is trained on a reflectance database made of radiative transfer model simulations. The principles underlying the generation of the database and the design of the Network are first presented. The estimated variables are then compared to other existing products, LAI - and fAPAR -MODIS and MGVI -MERIS, and validated against ground measurements performed in the framework of the VALERI project. Results show remarkable consistency of the temporal dynamics between the several products with however some differences in the range of variation. When compared to actual VALERI ground measurements, the proposed algorithm shows the best performances for LAI (RMSE = 0.47) and fAPAR (RMSE = 0.09).

Frederic Baret - One of the best experts on this subject based on the ideXlab platform.

  • neural Network Estimation of lai fapar fcover and lai cab from top of canopy meris reflectance data principles and validation
    Remote Sensing of Environment, 2006
    Co-Authors: Cedric Bacour, Frederic Baret, D Beal, Marie Weiss, K Pavageau
    Abstract:

    Abstract A neural Network is developed to operationally estimate biophysical variables over land surfaces from the observations of the ENVISAT-MERIS instrument: the leaf area index ( LAI ), the fraction of absorbed photosynthetically active radiation ( fAPAR ), the fraction of vegetation cover ( fCover ), and the canopy chlorophyll content ( LAI × C ab ). The neural Network requires as input the geometry of observation and the top of canopy reflectances, corrected from the atmospheric effects, in eleven spectral bands. It is trained on a reflectance database made of radiative transfer model simulations. The principles underlying the generation of the database and the design of the Network are first presented. The estimated variables are then compared to other existing products, LAI - and fAPAR -MODIS and MGVI -MERIS, and validated against ground measurements performed in the framework of the VALERI project. Results show remarkable consistency of the temporal dynamics between the several products with however some differences in the range of variation. When compared to actual VALERI ground measurements, the proposed algorithm shows the best performances for LAI (RMSE = 0.47) and fAPAR (RMSE = 0.09).

  • neural Network Estimation of lai fapar fcover and lai cab from top of canopy meris reflectance data principles and validation
    Remote Sensing of Environment, 2006
    Co-Authors: Cedric Bacour, Frederic Baret, D Beal, Marie Weiss, K Pavageau
    Abstract:

    Abstract A neural Network is developed to operationally estimate biophysical variables over land surfaces from the observations of the ENVISAT-MERIS instrument: the leaf area index ( LAI ), the fraction of absorbed photosynthetically active radiation ( fAPAR ), the fraction of vegetation cover ( fCover ), and the canopy chlorophyll content ( LAI × C ab ). The neural Network requires as input the geometry of observation and the top of canopy reflectances, corrected from the atmospheric effects, in eleven spectral bands. It is trained on a reflectance database made of radiative transfer model simulations. The principles underlying the generation of the database and the design of the Network are first presented. The estimated variables are then compared to other existing products, LAI - and fAPAR -MODIS and MGVI -MERIS, and validated against ground measurements performed in the framework of the VALERI project. Results show remarkable consistency of the temporal dynamics between the several products with however some differences in the range of variation. When compared to actual VALERI ground measurements, the proposed algorithm shows the best performances for LAI (RMSE = 0.47) and fAPAR (RMSE = 0.09).

D Beal - One of the best experts on this subject based on the ideXlab platform.

  • neural Network Estimation of lai fapar fcover and lai cab from top of canopy meris reflectance data principles and validation
    Remote Sensing of Environment, 2006
    Co-Authors: Cedric Bacour, Frederic Baret, D Beal, Marie Weiss, K Pavageau
    Abstract:

    Abstract A neural Network is developed to operationally estimate biophysical variables over land surfaces from the observations of the ENVISAT-MERIS instrument: the leaf area index ( LAI ), the fraction of absorbed photosynthetically active radiation ( fAPAR ), the fraction of vegetation cover ( fCover ), and the canopy chlorophyll content ( LAI × C ab ). The neural Network requires as input the geometry of observation and the top of canopy reflectances, corrected from the atmospheric effects, in eleven spectral bands. It is trained on a reflectance database made of radiative transfer model simulations. The principles underlying the generation of the database and the design of the Network are first presented. The estimated variables are then compared to other existing products, LAI - and fAPAR -MODIS and MGVI -MERIS, and validated against ground measurements performed in the framework of the VALERI project. Results show remarkable consistency of the temporal dynamics between the several products with however some differences in the range of variation. When compared to actual VALERI ground measurements, the proposed algorithm shows the best performances for LAI (RMSE = 0.47) and fAPAR (RMSE = 0.09).

  • neural Network Estimation of lai fapar fcover and lai cab from top of canopy meris reflectance data principles and validation
    Remote Sensing of Environment, 2006
    Co-Authors: Cedric Bacour, Frederic Baret, D Beal, Marie Weiss, K Pavageau
    Abstract:

    Abstract A neural Network is developed to operationally estimate biophysical variables over land surfaces from the observations of the ENVISAT-MERIS instrument: the leaf area index ( LAI ), the fraction of absorbed photosynthetically active radiation ( fAPAR ), the fraction of vegetation cover ( fCover ), and the canopy chlorophyll content ( LAI × C ab ). The neural Network requires as input the geometry of observation and the top of canopy reflectances, corrected from the atmospheric effects, in eleven spectral bands. It is trained on a reflectance database made of radiative transfer model simulations. The principles underlying the generation of the database and the design of the Network are first presented. The estimated variables are then compared to other existing products, LAI - and fAPAR -MODIS and MGVI -MERIS, and validated against ground measurements performed in the framework of the VALERI project. Results show remarkable consistency of the temporal dynamics between the several products with however some differences in the range of variation. When compared to actual VALERI ground measurements, the proposed algorithm shows the best performances for LAI (RMSE = 0.47) and fAPAR (RMSE = 0.09).

Marie Weiss - One of the best experts on this subject based on the ideXlab platform.

  • neural Network Estimation of lai fapar fcover and lai cab from top of canopy meris reflectance data principles and validation
    Remote Sensing of Environment, 2006
    Co-Authors: Cedric Bacour, Frederic Baret, D Beal, Marie Weiss, K Pavageau
    Abstract:

    Abstract A neural Network is developed to operationally estimate biophysical variables over land surfaces from the observations of the ENVISAT-MERIS instrument: the leaf area index ( LAI ), the fraction of absorbed photosynthetically active radiation ( fAPAR ), the fraction of vegetation cover ( fCover ), and the canopy chlorophyll content ( LAI × C ab ). The neural Network requires as input the geometry of observation and the top of canopy reflectances, corrected from the atmospheric effects, in eleven spectral bands. It is trained on a reflectance database made of radiative transfer model simulations. The principles underlying the generation of the database and the design of the Network are first presented. The estimated variables are then compared to other existing products, LAI - and fAPAR -MODIS and MGVI -MERIS, and validated against ground measurements performed in the framework of the VALERI project. Results show remarkable consistency of the temporal dynamics between the several products with however some differences in the range of variation. When compared to actual VALERI ground measurements, the proposed algorithm shows the best performances for LAI (RMSE = 0.47) and fAPAR (RMSE = 0.09).

  • neural Network Estimation of lai fapar fcover and lai cab from top of canopy meris reflectance data principles and validation
    Remote Sensing of Environment, 2006
    Co-Authors: Cedric Bacour, Frederic Baret, D Beal, Marie Weiss, K Pavageau
    Abstract:

    Abstract A neural Network is developed to operationally estimate biophysical variables over land surfaces from the observations of the ENVISAT-MERIS instrument: the leaf area index ( LAI ), the fraction of absorbed photosynthetically active radiation ( fAPAR ), the fraction of vegetation cover ( fCover ), and the canopy chlorophyll content ( LAI × C ab ). The neural Network requires as input the geometry of observation and the top of canopy reflectances, corrected from the atmospheric effects, in eleven spectral bands. It is trained on a reflectance database made of radiative transfer model simulations. The principles underlying the generation of the database and the design of the Network are first presented. The estimated variables are then compared to other existing products, LAI - and fAPAR -MODIS and MGVI -MERIS, and validated against ground measurements performed in the framework of the VALERI project. Results show remarkable consistency of the temporal dynamics between the several products with however some differences in the range of variation. When compared to actual VALERI ground measurements, the proposed algorithm shows the best performances for LAI (RMSE = 0.47) and fAPAR (RMSE = 0.09).