Integrating Sentinel 2A data for selecting the best methods in observation of biological pigments in coastal water

Abstract

Coastal waters serve as critical ecosystems, supporting diverse marine life and offering invaluable resources to human communities. Understanding the distribution and dynamics of biological pigments, such as chlorophyll and phytoplankton, is essential for assessing the health and productivity of these ecosystems. Remote sensing technology has revolutionized our ability to monitor these pigments, providing a synoptic view of coastal waters. Sentinel-2A, a European Space Agency satellite, offers high-resolution multispectral data, making it a valuable resource for coastal studies. This paper presents a comprehensive analysis of Sentinel-2A data integration techniques for the observation of biological pigments in coastal waters along the coasts of Mediterranean Sea (Abu-Qir Area) from seven different sampling sites for the presence of different chlorophyll forms. Water samples were collected and physico-chemical characteristics like dissolved oxygen, pH, conductivity and chlorophyll were measured. In chlorophyll estimation, different forms of were calculated viz. chl a, b, c and carotenoids using different methodologies. Images from the Sentinel Multispectral Imager (MSI) sensor were taken on the same sample date. The phyto-planktonic forms, carotenoids, and their ratios in the sampling sites were also determined analytically. With the integration of satellite images (Sentinel 2A), results indicated types of chlorophyll, carotenoids, and ratios' distributions in the Abu-Qir Area of the Mediterranean Sea, Egypt. Our results demonstrate that the kind of species, illumination levels, and nutrient concentrations may all affect how much chlorophyll is absorbed by phytoplankton. Additionally, the band ratios of the different pigment types were found to be connected, such as b3/b8, or the same band might interact with other bands like b5, b4, b6, and b7, or b8a. Additionally, it was noticed that the green band and near infrared bands were useful for detecting pigments integrated with sampling datasets.