Toward Atmospheric Correction Algorithms for Sentinel-3/OLCI Images of Productive Waters

Abstract

Atmospheric correction of remote sensing imagery over optically complex waters is still a challenging task. Even algorithms showing a good accuracy for moderate and extremely turbid waters need to be tested when being used for eutrophic inland basins. Such a test was carried out in this study on the example of a Sentinel-3/OLCI image of the productive waters of the Gorky Reservoir during the period of intense blue-green algal bloom using data on the concentration of chlorophyll a and remote sensing reflectance measured from the motorboat at many points of the reservoir. The accuracy of four common atmospheric correction (AC) algorithms was examined. All of them showed unsatisfactory accuracy due to incorrect determination of atmospheric aerosol parameters and aerosol radiance. The calculated aerosol optical depth (AOD) spectra varied widely (AOD(865) = 0.005 − 0.692) even over a small area (up to 10 × 10 km) and correlated with the measured chlorophyll a. As a result, a part of the high water-leaving signal caused by phytoplankton bloom was taken as an atmosphere signal. A significant overestimation of atmospheric aerosol parameters, as a consequence, led to a strong underestimation of the remote sensing reflectance and low accuracy of the considered AC algorithms. To solve this problem, an algorithm with a fixed AOD was proposed. The fixed AOD spectrum was determined in the area with relatively “clean” water as 5 percentiles of AOD in all water pixels. The proposed algorithm made it possible to obtain the remote sensing reflectance with high accuracy. The slopes of linear regression are close to 1 and the intercepts tend to zero in almost all spectral bands. The determination coefficients are more than 0.9; the bias, mean absolute percentage error, and root-mean-square error are notably lower than for other AC algorithms.