A REVIEW OF METHODS AND REGRESSION MODELS USING SATELLITE IMAGERIES ON PHYTOPLANKTON'S WATER QUALITY PARAMETERS ESTIMATION

• Published in: GEOINFORMATION WEEK 2022, VOL. 48-4
• Main Authors: Muhamad, Hasmida; Mokhtar, Ernieza Suhana; Roslani, M. Akmal
• Format: Proceedings Paper
• Language: English
• Published: COPERNICUS GESELLSCHAFT MBH 2023
• Subjects: Computer Science; Remote Sensing
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001185691400033

Water quality monitoring is compulsory to maintain and preserve aquatic ecosystem health, especially for the phytoplankton community. Phytoplankton abundance relies greatly on the condition of water, it is important to assess the water quality parameter (WQP) to estimate the abundance of PP. However, obtaining WQP using conventional methods (water sampling and laboratory assessment) requires more time and cost of operation. Therefore, Geographical Information System (GIS) and remote sensing (RS) approaches are becoming popular methods of measuring water quality parameters (turbidity, total suspended solids (TSS), temperature, pH, etc). This paper aims to review the assessment of WQP in relation to PP abundance and other interchangeable factors from the recent studies and efforts on WQP assessment using geospatial technologies approaches. Methods, algorithms, and accuracies established from the GIS and RS techniques are discussed. From ten (10) extended review research articles, it is revealed that most WQP has an indirect and direct effect on human activities, seasonal changes, fish production, water pollution, and especially PP abundance. In addition, about nine (9) previous research articles revealed the use of various satellite image sensors to estimate WQP from Landsat 8 is the most common, to Landsat 7 ETM+, 5, Sentinel MSI, and the least used is RapidEye. Further research also finds that the three most common types of WQP estimated via the geospatial analysis method are turbidity, pH, and Secchi depth with the highest R-2 value equal to 0.810,0.947, and 0.990 respectively.