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 50 条
  • [1] Improved Aerosol Optical Thickness, Columnar Water Vapor, and Surface Reflectance Retrieval from Combined CASI and SASI Airborne Hyperspectral Sensors
    Yang, Hang
    Zhang, Lifu
    Ong, Cindy
    Rodger, Andrew
    Liu, Jia
    Sun, Xuejian
    Zhang, Hongming
    Jian, Xun
    Tong, Qingxi
    REMOTE SENSING, 2017, 9 (03):
  • [2] Retrieval of aerosol optical depth and surface reflectance over land from NOAA AVHRR data
    Li, Yingjie
    Xue, Yong
    de Leeuw, Gerrit
    Li, Chi
    Yang, Leiku
    Hou, Tingting
    Marir, Farhi
    REMOTE SENSING OF ENVIRONMENT, 2013, 133 : 1 - 20
  • [3] Aerosol Optical Retrieval and Surface Reflectance from Airborne Remote Sensing Data over Land
    Bassani, Cristiana
    Cavalli, Rosa Maria
    Pignatti, Stefano
    SENSORS, 2010, 10 (07) : 6421 - 6438
  • [4] AEROSOL OPTICAL DEPTH AND SURFACE REFLECTANCE RETRIEVAL OVER LAND USING GEOSTATIONARY SATELLITE DATA
    Li, Chi
    Xue, Yong
    Li, Yingjie
    Yang, Leiku
    Hou, Tingting
    Xu, Hui
    Liu, Jia
    2012 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2012, : 7456 - 7459
  • [5] Retrieval of spectral aerosol optical thickness over land using ocean color sensors MERIS and SeaWiFS
    von Hoyningen-Huene, W.
    Yoon, J.
    Vountas, M.
    Istomina, L. G.
    Rohen, G.
    Dinter, T.
    Kokhanovsky, A. A.
    Burrows, J. P.
    ATMOSPHERIC MEASUREMENT TECHNIQUES, 2011, 4 (02) : 151 - 171
  • [6] Sensitivity of Reflectance to Water Vapor and Aerosol Optical Thickness
    Bhatia, Nitin
    Tolpekin, Valentyn A.
    Reusen, Ils
    Sterckx, Sindy
    Biesemans, Jan
    Stein, Alfred
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2015, 8 (06) : 3199 - 3208
  • [7] Joint Retrieval of Aerosol Optical Depth and Surface Reflectance Over Land Using Geostationary Satellite Data
    She, Lu
    Xue, Yong
    Yang, Xihua
    Leys, John
    Guang, Jie
    Che, Yahui
    Fan, Cheng
    Xie, Yanqing
    Li, Ying
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2019, 57 (03): : 1489 - 1501
  • [8] Retrieval of aerosol optical thickness over land using NOAA/AVHRR data
    Takemata, K.
    Fukui, H.
    Kawata, Y.
    ADVANCES IN SPACE RESEARCH, 2006, 38 (10) : 2208 - 2211
  • [9] Retrieval of aerosol optical thickness over land using NOAA/AVHRR data
    Takemata, K.
    Fukui, H.
    Kawata, Y.
    REMOTE SENSING OF OCEANOGRAPHIC PROCESSES AND LAND SURFACES; SPACE SCIENCE EDUCATION AND OUTREACH, 2006, 38 (10): : 2208 - +
  • [10] Retrieval algorithm for optical parameters of aerosol over land surface from POLDER data
    Sun, Xia
    Zhao, Huijie
    Guangxue Xuebao/Acta Optica Sinica, 2009, 29 (07): : 1772 - 1777