Chlorophyll-a Concentration Assessment Using Remotely Sensed Data over Multiple Years along the Coasts of the United Arab Emirates

Abstract

The United Arab Emirates (UAE) recently witnessed algal/phytoplankton blooms attributed to the high concentrations of Chlorophyll-a associated with the spread and accumulation of a wide range of organisms with toxic effects that influence ecological and fishing economic activities and water desalination along coastal areas.  This research explores the UAE coasts as a case study for the framework presented here. In this research, we argue that advances in satellite remote sensing and imaging of spatial and temporal data offer sufficient information to find the best-fit regression method and relationship between Chlorophyll-a concentration and a set of climatic and biological explanatory variables over time. Three functional forms of regression models were tested and analysed to reveal that the Log-Linear Model found to be the best fit providing the most statistically robust model compared to the Linear and the Generalised Least Square models.  Besides, it is useful to identify the factors Sea Surface temperature, Calcite Concentration, Instantaneous Photosynthetically Available Radiation, Normalized Fluorescence Line Height, and Wind Speed that significantly influence Chlorophyll-a concentration. Research results can be beneficial to aid decision-makers in building a best-fit statistical system and models of algal blooms in the study area. The study found results to be sensitive to the study’s temporal time-period length and the explanatory variables selected for the analysis.