Abstract
Globally, phytoplankton concentrations have had a significant impact on the aquaculture sector and aquatic ecosystems. Therefore, chlorophyll-a (Chl-a) concentrations can serve as an indicator of phytoplankton biomass and abundance in coastal environments, thereby reflecting water quality. This study aims to utilize machine-learning (ML) algorithms through the Google Earth Engine (GEE) platform, in conjunction with Remote Sensing (RS), to examine the relationship between Chl-a concentrations and Sea Surface Temperature (SST) from 2018 to 2021 during three specific months (May, August, and November) on the coast of North Africa. Chl-a data were obtained from the CHLA/V2 product of the Global Change Observation Mission (GCOM-C), while SST data were retrieved from the NOAA Optimum Interpolation Sea Surface Temperature (OISST/V2) dataset. The results revealed an inverse correlation between SST and Chl-a, with a correlation coefficient (R) of 0.51, indicating that an increase in SST leads to a decrease in Chl-a concentrations, and vice versa. August exhibited the highest surface temperature, reaching 24.5 °C, while November experienced a decrease in surface temperature with the onset of winter, averaging 20.2 °C throughout the year. The coefficient of determination (R2 = 0.258) indicates that SST accounted for 25.8% of the variation in Chl-a concentration changes, while the remaining 74.2% was influenced by other factors. Additionally, seasonal variations were observed during the autumn season, displaying the highest average concentration of Chl-a (262.69 mg/m3), while the summer season exhibited the lowest concentration, which is approximately around 152.63 mg/m3. Furthermore, spatial distribution variations were noticed from the eastern to the western part, with an evident decrease of Chl-a concentration in the eastern part, particularly in the deltaic and western regions of the Mediterranean coastal area in Egypt and the Libyan waters. Accordingly, this study sheds light on the utility of the GEE platform for data analysis and monitoring of Chl-a concentration and SST in marine coastal areas over large spatial scales.