Mapping Phytoplankton and Algal Blooms With a Novel Multisensor Water Index

Multispectral satellite imageries (MSIs) are strong enough to delineate features of interest by suppressing others. The water index is a method applied to delineate the water features by combining the multispectral band images. This research proposes a novel index for extracting water features from multisensor MSIs named multisensor water index. The proposed index, along with existing indices, is applied on Landsat-8 OLI imageries, a comparative analysis is done, and it is found that the proposed index is outperforming with 99.77% accuracy. The proposed index was validated on other sensors, such as a series of Landsat (5, 7, 8, and 9), Sentinel-2, Resourcesat, and Modis satellite imagery, by mapping water features successfully. Furthermore, the proposed index maps marine pollution by discriminating phytoplankton and algal bloom from the water feature. It is observed that the increase in phytoplankton or algal bloom is the result of eutrophication. The blooming of phytoplankton and algae is on the surface of the water. Thus, it causes shading at the bottom of the marine ecosystem and negatively impacts the growth of seagrasses, dissolved oxygen levels, fish suffocation, water properties, and other bottom habitats. The three test sites, viz., lake Villarrica, lake Okeechobee, and the Atlantic Ocean, have been considered to study increasing phytoplankton and algal bloom. The proposed index successfully discriminates the phytoplankton and algal bloom from the water features.