Chlorophyll-a concentration in the hailing bay using remote sensing assisted sparse statistical modelling. (12th March 2021)

• Record Type: Journal Article
• Authors: Liu, Guangping; Wei, Jingmei; Muthu, BalaAnand; Jackson Samuel, R. Dinesh
• Is Part Of: European journal of remote sensing. Volume 54(2021)Supplement 2
• Page Start: 284
• Page End: 295
• Publication Date: 2021-03-12
• Subjects: Remote sensing -- carbon flow -- coastal system -- computational modeling; Remote sensing -- Periodicals; Remote sensing; Electronic journals; Periodicals; 621.3678
• DOI: 10.1080/22797254.2020.1771774
• Languages: English
• ISSNs: 2279-7254

Abstract

ABSTRACT: In the recent past, the Satellite authenticated synoptic instrument has been used to retrieve the water quality variables like chlorophyll, suspended materials and the pigmented dissolved organic matter. However, the use of chlorophyll phytoplankton endeavors acts as a proxy and strongly overestimates the contribution to the annual pelagic carbon flows from spring production. Further, Remote Sensing assisted Sparse Statistical Modelling (RSSSM)has been proposed to determine the chlorophyll-a concentration seasonal variations and spatial/temporal structure in the Hailing Bay. It provides high correlation information between the water surface environment and organic matter. Besides, it provides the highest possible correlation coefficient value and gives a more practical representation at a clear water reference site using a lab-scale simulation setup. Thus in considering the coastal system, the seasonal variation in chlorophyll ratios has been reviewed and outcomes has been analyzed using effective experimental validation at lab scale.