A new framework for accessing and visualizing Ocean Color Data for Water quality parameter analysis

Abstract

This paper presents a framework for the remote processing and quantitative analysis of water quality parameters, specifically chlorophyll and sea surface temperature (SST). The framework is organized into three directories. The first directory accesses and examines water quality parameters, the second collocates in-situ observations with satellite data, and the third integrates Ocean Observatories Initiative (OOI) data via Machine to Machine (M2M) interfaces. The analysis of chlorophyll concentration is defined in correlation with SST, employing the Gradient of Mean and Mean of Gradient mathematical tools for change detection. The result is a system that leverages cloud technology to access, process, and analyze data. With a spatial gradient tolerance of 90 percentile, the framework enables accurate change detection from time-average data. This methodology contributes to the field of water quality analysis, offering new insights into environmental monitoring and marine science.

URN:NBN:sciencein.jist.2024.v12.707