Assessment of Landsat atmospheric correction methods for water color applications using global AERONET-OC data

• Published in: International journal of applied earth observation and geoinformation 2020-12, Vol.93, p.102192, Article 102192
• Main Authors: Xu, Yang, Feng, Lian, Zhao, Dan, Lu, Jianzhong
• Format: Article
• Language: English
• Subjects: AERONET-OC; Atmospheric correction; Landsat; Remote sensing reflectance
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2020.102192

•Global AERONET-OC data used to validate atmospheric correction algorithms for Landsat.•Five different algorithms developed for water color applications have been evaluated.•Landsat 8 OLI showed more accurate AC retrievals than its predecessors.•Uncertainty sources in various Landsat reflectance products have been discussed. With the longest archive of satellite remote sensing images, the Landsat series of satellites have demonstrated their great potential in aquatic environmental studies. However, although various atmospheric correction (AC) methods have been developed for Landsat observations in water color applications, a comprehensive assessment of their accuracies across different AC methods and instruments has yet to be performed. Using in situ spectral data collected by Aerosol Robotic Network-Ocean Color (AERONET-OC) sites, the performances of five types of AC methods over three different Landsat missions (i.e., Landsat 5/7/8) were evaluated. The Landsat 8 Operational Land Imager (OLI) showed more accurate AC retrievals than the other two instruments, and the results for its green and red bands appeared more reliable than those for the other wavelengths (uncertainty levels of ∼30 %). The iterative NIR algorithm with 2-bands (NIR-SWIR2) model selection embedded in SeaDAS showed the best performances for OLI in two blue bands. Moreover, larger residual errors were found for most Landsat 5/7 bands regardless of the AC methods and spectral bands employed with an uncertainty of >50 %. Interestingly, a simple aerosol subtraction method over the Rayleigh-corrected reflectance (Rrc) outperformed the exponential extrapolation (EXP) algorithms, especially for Landsat 5/7. Neither the image-based AC algorithm nor the surface reflectance (SR) products provided by the United States Geological Survey (USGS) showed acceptable performances over coastal environments. The uncertainties in the various Landsat reflectance products over water surfaces could be associated with a relatively poor signal-to-noise ratio (SNR) in addition to radiometric calibration uncertainties, imperfect aerosol removal methods. Future research is required to collect in situ data across a wider range of water optical properties (particularly more turbid inland waters) to examine the corresponding applicability of Landsat-series observations.