Coupled retrieval of aerosol optical thickness, columnar water vapor and surface reflectance maps from ENVISAT/MERIS data over land

被引:52
作者
Guanter, Luis [1 ]
Gomez-Chova, Luis [2 ]
Moreno, Jose [3 ]
机构
[1] GeoForschungsZentrum Potsdam, Remote Sensing Sect, D-14473 Potsdam, Germany
[2] Univ Valencia, Dept Elect Engn, E-46100 Burjassot, Spain
[3] Univ Valencia, Dept Earth Phys & Thermodynam, E-46100 Burjassot, Spain
关键词
aerosol optical thickness; columnar water vapor; surface reflectance; atmospheric correction; MERIS; AERONET;
D O I
10.1016/j.rse.2008.02.001
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
An algorithm for the derivation of atmospheric parameters and surface reflectance data from MEdium Resolution Imaging Specrometer Instrument (MERIS) on board ENVIronmental SATellite (ENVISAT) images has been developed. Geo-rectified aerosol optical thickness (AOT), columnar water vapor (CWV) and spectral surface reflectance maps are generated from MERIS Level-1b data over land. The algorithm has been implemented so that AOT, CWV and reflectance products are provided on an operational manner, making no use of ancillary parameters apart from those attached to MERIS products. For this reason, it has been named Self-Contained Atmospheric Parameters Estimation from MERIS data (SCAPE-M). The fundamental basis of the algorithm and applicable error figures are presented in the first part of this paper. In particular, errors of +/- 0.03, +/- 4% and +/- 8% have been estimated for AOT, CWV and surface reflectance retrievals, respectively, by means of a sensitivity analysis based on a synthetic data set simulated under a usual MERIS scene configuration over land targets. The assumption of a fixed aerosol model, the coarse spatial resolution of the AOT product and the neglection of surface reflectance directional effects were also identified as limitations of SCAPE-M. Validation results are detailed in the second part of the paper. Comparison of SCAPE-M AOT retrievals with data from AErosol RObotic NETwork (AERONET) stations showed an average Root Mean Square Error (RMSE) of 0.05, and an average correlation coefficient R-2 of about 0.7-0.8. R-2 values grew up to more than 0.9 in the case of CWV after comparison with the same stations. A good correlation is also found with the MERIS Level-2 ESA CWV product. Retrieved surface reflectance maps have been successfully compared with reflectance data derived from the Compact High Resolution Imaging Spectrometer (CHRIS) on board the PRoject for On-Board Autonomy (PROBA) in the first place. Reflectance retrievals have also been compared with reflectance data derived from MERIS images by the Bremen AErosol Retrieval (BAER) method. A good correlation in the red and near-infrared bands was found, although a considerably higher proportion of pixels was successfully processed by SCAPE-M. (C) 2008 Elsevier Inc. All rights reserved.
引用
收藏
页码:2898 / 2913
页数:16
相关论文
共 39 条
  • [1] AMANS V, 2007, EOEPGSOPEOPGTN060001
  • [2] The PROBA/CHRIS mission: A low-cost smallsat for hyperspectral multiangle observations of the earth surface and atmosphere
    Barnsley, MJ
    Settle, JJ
    Cutter, MA
    Lobb, DR
    Teston, F
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2004, 42 (07): : 1512 - 1520
  • [3] A method for aerosol correction from the spectral variation in the visible and near infrared:: application to the MERIS sensor
    Beal, D.
    Baret, F.
    Bacour, C.
    Gu, X-F.
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2007, 28 (3-4) : 761 - 779
  • [4] Retrieval of columnar water vapour over land from backscattered solar radiation using the Medium Resolution Imaging Spectrometer
    Bennartz, R
    Fischer, J
    [J]. REMOTE SENSING OF ENVIRONMENT, 2001, 78 (03) : 274 - 283
  • [5] Berk A., 2003, MODTRAN4 VERSION 3 R
  • [6] CACHORRO VE, 2004, OPT PURA APL, V37, P3401
  • [7] Satellite-based columnar water vapor, retrieval with the multi-spectral thermal imager (MTI)
    Chylek, P
    Bore, CC
    Clodius, W
    Pope, PA
    Rodger, AP
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2003, 41 (12): : 2767 - 2770
  • [8] CHYLEK P, 2005, IEEE T GEOSCI REMOTE, V43, P2767
  • [9] CIOTTI P, 2003, P IGARSS TOUL FRANC
  • [10] COLBY JD, 1991, PHOTOGRAMM ENG REM S, V57, P531