The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-7/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 677–683, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-677-2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 677–683, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-677-2015

  29 Apr 2015

29 Apr 2015

Validation of aerosol estimation in atmospheric correction algorithm ATCOR

B. Pflug1, M. Main-Knorn1, A. Makarau2, and R. Richter2 B. Pflug et al.
  • 1DLR - Remote Sensing Technology Institute, Photogrammetry and Image Analysis, Berlin, Germany
  • 2DLR - Remote Sensing Technology Institute, Photogrammetry and Image Analysis, Oberpfaffenhofen, Germany

Keywords: Image processing, Atmospheric correction, ATCOR, validation, aerosol retrieval, RapidEye, Landsat

Abstract. Atmospheric correction of satellite images is necessary for many applications of remote sensing, i.e. computation of vegetation indices and biomass estimation. The first step in atmospheric correction is estimation of the actual aerosol properties. Due to the spatial and temporal variability of aerosol amount and type, this step becomes crucial for an accurate correction of satellite data. Consequently, the validation of aerosol estimation contributes to the validation of atmospheric correction algorithms. In this study we present the validation of aerosol estimation using own sun photometer measurements in Central Europe and measurements of AERONET-stations at different locations in the world. Our ground-based sun photometer measurements of vertical column aerosoloptical thickness (AOT) spectra are performed synchronously to overpasses of the satellites RapidEye, Landsat 5, Landsat 7 and Landsat 8. Selected AERONET data are collocated to Landsat 8 overflights. The validation of the aerosol retrieval is conducted by a direct comparison of ground-measured AOT with satellite derived AOT using the ATCOR tool for the selected satellite images. The mean uncertainty found in our experiments is ΔAOT550nm ≈ 0.03±0.02 for cloudless conditions with cloud+haze fraction below 1%. This AOT uncertainty approximately corresponds to an uncertainty in surface albedo of Δρ ≈ 0.003. Inclusion of cloudy and hazy satellite images into the analysis results in mean ΔAOT550nm ≈ 0.04±0.03 for both RapidEye and Landsat imagery. About ⅓ of samples perform with the AOT uncertainty better than 0.02 and about ⅔ perform with AOT uncertainty better than 0.05.