Abstract
The atmospheric contribution constitutes about 90 percent of the signal measured by satellite sensors over oceanic and inland waters. Over open ocean waters, the atmospheric contribution is relatively easy to correct as it can be assumed that water-leaving radiance in the near-infrared (NIR) is equal to zero and it can be performed by applying a relatively simple dark-pixel-correction-based type of algorithm. Over inland and coastal waters, this assumption cannot be made since the water-leaving radiance in the NIR is greater than zero due to the presence of water components like sediments and dissolved organic particles. The aim of this study is to determine the most appropriate atmospheric correction processor to be applied on Sentinel-2 MultiSpectral Imagery over several types of inland waters. Retrievals obtained from different atmospheric correction processors (i.e., Atmospheric correction for OLI ‘lite’ (ACOLITE), Case 2 Regional Coast Colour (here called C2RCC), Case 2 Regional Coast Colour for Complex waters (here called C2RCCCX), Image correction for atmospheric effects (iCOR), Polynomial-based algorithm applied to MERIS (Polymer) and Sen2Cor or Sentinel 2 Correction) are compared against in situ reflectance measured in lakes and reservoirs in the Valencia region (Spain). Polymer and C2RCC are the processors that give back the best statistics, with coefficients of determination higher than 0.83 and mean average errors less than 0.01. An evaluation of the performance based on water types and single bands–classification based on ranges of in situ chlorophyll-a concentration and Secchi disk depth values- showed that performance of these set of processors is better for relatively complex waters. ACOLITE, iCOR and Sen2Cor had a better performance when applied to meso- and hyper-eutrophic waters, compare with oligotrophic. However, other considerations should also be taken into account, like the elevation of the lakes above sea level, their distance from the sea and their morphology.
Highlights
The radiance over water measured by satellite sensors has two important contributors: (1) the water itself, due to the interaction of sunlight with the optically active constituents (OACs), namely pure water, phytoplankton, Colored Dissolved Organic Matter and (CDOM) and Non-algal Particulates (NAP) or suspended sediments; and (2) the atmosphere, made up of atmospheric gases and aerosols
Retrievals obtained from different atmospheric correction processors (i.e., Atmospheric correction for OLI ‘lite’ (ACOLITE), Case 2 Regional Coast Colour, Case 2 Regional Coast Colour for Complex waters, Image correction for atmospheric effects, Polynomial-based algorithm applied to MERIS (Polymer) and Sen2Cor or Sentinel 2 Correction) are compared against in situ reflectance measured in lakes and reservoirs in the Valencia region (Spain)
Two common patterns are shown in these AC processors: Case 2 Regional Coast Colour (C2RCC), C2RCCCX and Polymer show an overestimation of some data in the visible bands (443, 490 and 560 nm), especially for Type 1 waters while the bulk of the data is below the 1:1 line
Summary
The radiance over water measured by satellite sensors has two important contributors: (1) the water itself, due to the interaction of sunlight with the optically active constituents (OACs), namely pure water, phytoplankton, Colored Dissolved Organic Matter and (CDOM) and Non-algal Particulates (NAP) or suspended sediments; and (2) the atmosphere, made up of atmospheric gases and aerosols. The latter represents about 90% of the signal measured by the satellite sensor [1]. These OACs have a very distinct effect, with Total Suspended Matter (TSM) producing a reflectance increase in the green and red bands, while CDOM increases absorption (reducing reflectance) in the blue bands
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