Algorithm for Additional Correction of Remote Sensing Reflectance in the Presence of Absorbing Aerosol: Case Study

Purpose. The main goal of this work is to develop an algorithm for additional correction of Level 2 remote sensing reflectance ocean color satellite data, taking into account the presence of absorbing aerosol over the Black Sea, where a large number of dust transfers from the Sahara are observed annually. Methods and Results. The research method is based on the comparison of satellite data about remote sensing reflectance from MODIS-Aqua/Terra scanner and in situ measurements from AErosol ROboties NETwork Ocean Color (AERONET-OC) stations. The Python mathematical package was used for the data processing: analysis and visualization of satellite images were made in SeaDAS. As a basis for an additional correction algorithm, theoretical calculations were provided to take into account aerosol stratification in the radiative transfer equation, it is shown that for absorbing aerosol the atmospheric correction error is proportional to λ−4. The analytical conclusions were confirmed during the validation of the satellite and the in situ measurements using principal component analysis (PCA). The new algorithm is based on the constancy of the color index value, characteristic of the selected region. For the Black Sea, the average value of color index at 412 and 443 nm (CI(412/443)) is approximately equal to 0.80 ± 0.08, a small standard deviation indicates that the sample is slightly variable and considered as the reference value. Conclusions. The model values of the remote sensing reflectance (Rrs) had a better agreement with the in situ values than the satellite Rrs(λ) at Level 2. In the case of the dust aerosol presence, the developed model increases the coefficient of determination between the satellite and the in situ values of Rrs(λ) by more than twice at 412 nm, the difference is also noticeable at 443 and 488 nm. The color indices calculated from the model values of Rrs(λ), which are necessary for calculating chlorophyll a, are also in better agreement with the AERONET data (an increase in correlation by 20 %).