Global trends of transparency and color based on merged multi-sensor satellite data

Jaime Pitarch, Royal Netherlands Institute for Sea Research and Utrecht University, Department of Coastal Systems, Texel, Netherlands, Dr. Marco Bellacicco, PhD, ENEA National Agency for New Technologies, Energy and Sustainable Economic Development, Frascati, Italy and Salvatore Marullo, ENEA National Agency for New Technologies, Energy and Sustainable Economic Development, Rome, Italy
This contribution analyzes color and transparency data from the remote-sensing ESA-OC-CCI dataset. Such dataset was obtained after merging all mid-resolution sensors data available since SeaWiFS until present, and was conceived to provide data for climate studies, for which long time series are needed, in order to derive statistically-significant trends and temporal oscillations. The sensor merging took care of the inter-sensor bias removal so that artificial trends were not introduced. In addition, the last version of the CCI dataset includes the latest NASA reprocessing, R2018.0, which corrects for the aging of the AQUA sensor. Remote-sensing algorithms are applied to monthly-binned global remote-sensing data to derive transparency in terms of the Secchi disk depth and color in terms of the hue angle. Both parameters, while being first-order dependent on phytoplankton concentration, they do have different optical characteristics, and their joint study can reveal insights that their separate analysis would not. Pixel-wise time series at monthly time resolution are derived globally and a trend analysis is performed after a proper seasonal decomposition. Particular focus is put on the most oligotrophic areas of the Ocean such as the subtropical gyres, which previous analyses showed to be expading.