Atmospheric Correction of Satellite Ocean Color Remote Sensing in the Presence of High Aerosol Loads

Abstract

The coverage of valid pixels of remote-sensing reflectance (Rrs) from ocean color imagery is relatively low due to the presence of clouds. In fact, it is also related to the presence of high aerosol optical depth (AOD) and other factors. In order to increase the valid coverage of satellite-retrieved products, a layer removal scheme for atmospheric correction (LRSAC) has been developed to process the ocean color data. The LRSAC used a five-layer structure including atmospheric absorption layer, Rayleigh scattering layer, aerosol scattering layer, sea surface reflection layer, and water-leaving reflectance layer to deal with the relationship of the components of the atmospheric correction. A nonlinear approach was used to solve the multiple reflections of the interface between two adjoining layers and a step-by-step procedure was used to remove effects of each layer. The LRSAC was used to process data from the sea-viewing wide field-of-view sensor (SeaWiFS) and the results were compared with standard products. The average of valid pixels of the global daily Rrs images of the standard products from 1997 to 2010 is only 11.5%, while it reaches up to 30.5% for the LRSAC. This indicates that the LRSAC recovers approximately 1.65 times of invalid pixels as compared with the standard products. Eight-day standard composite images exhibit many large areas with invalid values due to the presence of high AOD, whereas these areas are filled with valid pixels wusing the LRSAC. The ratio image of the mean valid pixel of the LRSAC to that of the standard products indicates that the number of valid pixels of the LRSAC increases with an increase of AOD. The LRSAC can increase the number of valid pixels by more than two times in about 33.8% of ocean areas with high AOD values. The accuracy of Rrs from the LRSAC was validated using the following two in situ datasets: the Marine Optical BuoY (MOBY) and the NASA bio-Optical Marine Algorithm Dataset (NOMAD). Most matchup pairs are distributed around the 1:1 line indicating that the systematic bias of the LRSAC is relatively small. The global mean relative error (MRE) of Rrs is 7.9% and the root mean square error (RMSE) is 0.00099 sr−1 for the MOBY matchups. Similarly, the MRE and RMSE are 2.1% and 0.0025 sr−1 for the NOMAD matchups, respectively. The accuracy of LRSAC was also evaluated by different groups of matchups according to the increase of AOD values, indicating that the errors of Rrs were little affected by the presence of high AOD values. Therefore, the LRSAC can significantly improve the coverage of valid pixels of Rrs with a similar accuracy in the presence of high AOD.