Detection of Phytoplankton Temporal Anomalies Based on Satellite Inherent Optical Properties: A Tool for Monitoring Phytoplankton Blooms

Abstract

[EN] The baseline of a specific variable defines the average behavior of that variable and it must be built from long data series that represent its spatial and temporal variability. In coastal and marine waters, phytoplankton can produce blooms characterized by a wide range of total cells number or chlorophyll a concentration. Classifying a phytoplankton abundance increase as a bloom depends on the species, the study area and the season. The objective of this study was to define the baseline of satellite absorption coefficients in Todos Santos Bay (Baja California, Mexico) to determine the presence of phytoplankton blooms based on the satellite inherent optical properties index (satellite IOP index). Two field points were selected according to historical bloom reports. To build the baseline, the data of phytoplankton absorption coefficients (aphy,GIOP ) and detritus plus colored dissolved organic matter (CDOM) (adCDOM,GIOP ) from the generalized inherent optical property (GIOP) satellite model of the NASA moderate resolution imaging spectroradiometer (MODIS-Aqua) sensor was studied for the period 2003 to 2016. Field data taken during a phytoplankton bloom event on June 2017 was used to validate the use of satellite products. The association between field and satellite data had a significant positive correlation. The satellite baseline detected a trend change from high values to low values of the satellite IOP index since 2010. Improved wastewater treatment to waters discharged into the Bay, and increased aquaculture of filter-feeding mollusks could have been the cause. The methodology proposed in this study can be a supplementary tool for permanent in situ monitoring programs. This methodology offers several advantages: A complete spatial coverage of the specific coastal area under study, appropriate temporal resolution and a tool for building an objective baseline to detect deviation from average conditions during phytoplankton bloom events.