The Experts below are selected from a list of 458622 Experts worldwide ranked by ideXlab platform

Clement Atzberger - One of the best experts on this subject based on the ideXlab platform.

  • data service platform for sentinel 2 surface reflectance and value added Products system use and examples
    Remote Sensing, 2016
    Co-Authors: Francesco Vuolo, Hannah Wenng, Markus Immitzer, Luca Zappa, Carlo Pipitone, Frédéric Baret, Marie Weiss, Mateusz żoltak, Clement Atzberger
    Abstract:

    This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding Value-Added Products for any land surface on Earth. Using the European Space Agency’s (ESA) Sen2Cor algorithm, the platform processes ESA’s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data. Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates Value-Added Products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value).

  • Data service platform for Sentinel-2 surface reflectance and Value-Added Products: System use and examples
    Remote Sensing, 2016
    Co-Authors: Francesco Vuolo, Mateusz Zóltak, Hannah Wenng, Markus Immitzer, Luca Zappa, Carlo Pipitone, Frédéric Baret, Marie Weiss, Clement Atzberger
    Abstract:

    This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding Value-Added Products for any land surface on Earth (http://s2.boku.eodc.eu/). Using the European Space Agency’s (ESA) Sen2Cor algorithm, the platform processes ESA’s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data (http://www.eodc.eu). Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates Value-Added Products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value).

Francesco Vuolo - One of the best experts on this subject based on the ideXlab platform.

  • data service platform for sentinel 2 surface reflectance and value added Products system use and examples
    Remote Sensing, 2016
    Co-Authors: Francesco Vuolo, Hannah Wenng, Markus Immitzer, Luca Zappa, Carlo Pipitone, Frédéric Baret, Marie Weiss, Mateusz żoltak, Clement Atzberger
    Abstract:

    This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding Value-Added Products for any land surface on Earth. Using the European Space Agency’s (ESA) Sen2Cor algorithm, the platform processes ESA’s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data. Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates Value-Added Products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value).

  • Data service platform for Sentinel-2 surface reflectance and Value-Added Products: System use and examples
    Remote Sensing, 2016
    Co-Authors: Francesco Vuolo, Mateusz Zóltak, Hannah Wenng, Markus Immitzer, Luca Zappa, Carlo Pipitone, Frédéric Baret, Marie Weiss, Clement Atzberger
    Abstract:

    This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding Value-Added Products for any land surface on Earth (http://s2.boku.eodc.eu/). Using the European Space Agency’s (ESA) Sen2Cor algorithm, the platform processes ESA’s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data (http://www.eodc.eu). Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates Value-Added Products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value).

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

  • data service platform for sentinel 2 surface reflectance and value added Products system use and examples
    Remote Sensing, 2016
    Co-Authors: Francesco Vuolo, Hannah Wenng, Markus Immitzer, Luca Zappa, Carlo Pipitone, Frédéric Baret, Marie Weiss, Mateusz żoltak, Clement Atzberger
    Abstract:

    This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding Value-Added Products for any land surface on Earth. Using the European Space Agency’s (ESA) Sen2Cor algorithm, the platform processes ESA’s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data. Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates Value-Added Products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value).

  • Data service platform for Sentinel-2 surface reflectance and Value-Added Products: System use and examples
    Remote Sensing, 2016
    Co-Authors: Francesco Vuolo, Mateusz Zóltak, Hannah Wenng, Markus Immitzer, Luca Zappa, Carlo Pipitone, Frédéric Baret, Marie Weiss, Clement Atzberger
    Abstract:

    This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding Value-Added Products for any land surface on Earth (http://s2.boku.eodc.eu/). Using the European Space Agency’s (ESA) Sen2Cor algorithm, the platform processes ESA’s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data (http://www.eodc.eu). Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates Value-Added Products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value).

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

  • data service platform for sentinel 2 surface reflectance and value added Products system use and examples
    Remote Sensing, 2016
    Co-Authors: Francesco Vuolo, Hannah Wenng, Markus Immitzer, Luca Zappa, Carlo Pipitone, Frédéric Baret, Marie Weiss, Mateusz żoltak, Clement Atzberger
    Abstract:

    This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding Value-Added Products for any land surface on Earth. Using the European Space Agency’s (ESA) Sen2Cor algorithm, the platform processes ESA’s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data. Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates Value-Added Products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value).

  • Data service platform for Sentinel-2 surface reflectance and Value-Added Products: System use and examples
    Remote Sensing, 2016
    Co-Authors: Francesco Vuolo, Mateusz Zóltak, Hannah Wenng, Markus Immitzer, Luca Zappa, Carlo Pipitone, Frédéric Baret, Marie Weiss, Clement Atzberger
    Abstract:

    This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding Value-Added Products for any land surface on Earth (http://s2.boku.eodc.eu/). Using the European Space Agency’s (ESA) Sen2Cor algorithm, the platform processes ESA’s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data (http://www.eodc.eu). Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates Value-Added Products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value).

Carlo Pipitone - One of the best experts on this subject based on the ideXlab platform.

  • data service platform for sentinel 2 surface reflectance and value added Products system use and examples
    Remote Sensing, 2016
    Co-Authors: Francesco Vuolo, Hannah Wenng, Markus Immitzer, Luca Zappa, Carlo Pipitone, Frédéric Baret, Marie Weiss, Mateusz żoltak, Clement Atzberger
    Abstract:

    This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding Value-Added Products for any land surface on Earth. Using the European Space Agency’s (ESA) Sen2Cor algorithm, the platform processes ESA’s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data. Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates Value-Added Products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value).

  • Data service platform for Sentinel-2 surface reflectance and Value-Added Products: System use and examples
    Remote Sensing, 2016
    Co-Authors: Francesco Vuolo, Mateusz Zóltak, Hannah Wenng, Markus Immitzer, Luca Zappa, Carlo Pipitone, Frédéric Baret, Marie Weiss, Clement Atzberger
    Abstract:

    This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding Value-Added Products for any land surface on Earth (http://s2.boku.eodc.eu/). Using the European Space Agency’s (ESA) Sen2Cor algorithm, the platform processes ESA’s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data (http://www.eodc.eu). Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates Value-Added Products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value).