A Secchi Depth Algorithm Considering the Residual Error in Satellite Remote Sensing Reflectance Data

A scheme to semi-analytically derive waters' Secchi depth (Z(sd)) from remote sensing reflectance (R-rs) considering the effects of the residual errors in satellite R-rs was developed for the China Eastern Coastal Zone (CECZ). This approach was evaluated and compared against three existing algorithms using field measurements. As it was challenging to provide the accurately inherent optical properties data for running the three existing algorithms in the extremely turbid waters, the new developed algorithm worked more effective than the latter. Moreover, with both synthetic and match-up data, the results indicated that the proposed algorithm was able to minimize some residual errors in R-rs, and thus could generate inter-mission consistent Z(sd) results from two ocean color missions. Finally, after application of new model to satellite images, we presented the spatial and temporal variations of Secchi depth and trophic state in the CECZ during 2002-2014. The study led to several findings: Firstly, the Z(sd)-based trophic state index (TSI) in the East China Sea first increased since 2002, and then gradually dropped during 2008-2014. Secondly, more and more waters within 30-35 m and 20-25 m isobaths were deteriorating from oligotrophic to mesotrophic type and from mesotrophic to eutrophic water, respectively, during 2002-2014. Lastly, the TSI increased on average 0.091 and 0.286 m per year respectively in Bohai Sea and Yellow Sea since 2002, and it might only take 14 and 67 years for Bohai Sea and Yellow Sea to deteriorate from mesotrophic to eutrophic water, following their current yearly deterioration rate and trophic trend. These results highlighted the importance to make some strict regulations for protecting the aquatic environment in the CECZ.