PV-LAC-ATMO:
Proba-V Advanced Atmospheric Correction

   
Minimize Project Documents

The following documents will be provided within this project.

  • Requirements Baseline Document: providing the Project Baseline for the Proba-V Advance Atmospheric Correction. The document includes a review of different atmospheric correction approaches and a description of the proposed method. The Atmospheric Correction Validation approach is also reported.

Algorithm Theoretical Baseline Document (ATBD)

The information provided in this section is applicable to the CISAR algorithm that infers surface reflectance and aerosol optical thickness over land surfaces from observations acquired by the PROBA-V radiometer. The algorithm has been developed within the framework of the PV-LAC ATMO project.

 

Scientific Roadmap (SR)

The Roadmap document discusses a number of elements that relate to the future application of the defined joint aerosol optical thickness and surface reflectance retrieval method (CISAR algorithm).

 

Validation Report (VR)

This document describes the Aerosol Optical Thickness (AOT) and Bi-Hemispherical Reflectance (BHR) validation, which are obtained from PROBA-V observations using the Combined Inversion of Surface and AeRosols (CISAR) algorithm.
The validation objectives are the following:

  • To obtain an indication on the usefulness of applying CISAR to PROBA-V observations and on the method's performance
  • To assess whether CISAR's performance is of added value compared to the current operationally retrieved AOT and TOC reflectance

 

Minimize Outreach Publications

The following section addresses the list of publications related to the PV-LAC ATMO as well as participation in international conferences:

Minimize Literature
  • Berthelot B. and G. Dedieu, 1997, Correction of atmospheric effects for VEGETATION data. Physical Measurements and Signatures in Remote Sensing, p. 19-25.
  • Govaerts, Y. M., S. Wagner, A. Lattanzio, and P. Watts (2010), Joint retrieval of surface reflectance and aerosol optical depth from MSG/SEVIRI observations with an optimal estimation approach: 1. Theory, J. Geophys. Res., 115, D02203, doi: 10.1029/2009JD011780.
  • Maisongrande P., Duchemin, B., Berthelot, B., Dubegny C., Dedieu G. Leroy M, (2001), A new algorithmic concept for atmospheric correction of surface reflectances delivered by the VEGETATION system. Proceedings of the VEGETATION 2000 conference, Belgirate.
  • Rahman, H., and G. Dedieu (1994), SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum. Remote Sensing, 15(1), 123-143.
  • Sterckx, S., I. Benhadj, G. Duhoux, S. Livens, W. Dierckx, E. Goor, S. Adriaensen, W. Heyns, K. Van Hoof, G. Strackx, K. Nackaerts, I. Reusen, T. Van Achteren, J. Dries, T. Van Roey, K. Mellab, R. Duca, J. and Zender, (2014), The PROBA-V mission: image processing and calibration. Int. J. Remote Sens., 35(7), 2565 – 2588.
  • Wagner, S. C., Y. M. Govaerts, and A. Lattanzio (2010), Joint retrieval of surface reflectance and aerosol optical depth from MSG/SEVIRI observations with an optimal estimation approach: 2. Implementation and evaluation, J. Geophys. Res., 115, D02204, doi: 10.1029/2009JD011780
  • Tarantola, A. 1998. Inverse Problem Theory : Methods for Data Fitting and Model Parameter Estimation. Vol. 3rd edition. Elsevier.