MODELLING SEAWATER QUALITY OF RACH GIA BAY OF VIETNAM, USING SENTINEL-2 IMAGERY PROCESSED IN THE GOOGLE EARTH ENGINE

Nguyễn Hồng Quảng; Vũ Anh Tuân; Lê Thị Thu Hằng; Nguyễn Trần Điện; Lê Thanh Sơn; Nguyễn Nguyên Minh

doi:10.34238/tnu-jst.5402

MODELLING SEAWATER QUALITY OF RACH GIA BAY OF VIETNAM, USING SENTINEL-2 IMAGERY PROCESSED IN THE GOOGLE EARTH ENGINE

About this article

Received: 30/12/21 Revised: 25/01/22 Published: 24/02/22

Authors

1. Nguyen Hong Quang , Vietnam National Space Center (VNSC) – VAST
2. Vu Anh Tuan, Vietnam National Space Center (VNSC) – VAST
3. Le Thi Thu Hang, Vietnam National Space Center (VNSC) – VAST
4. Nguyen Tran Dien, Institute of Environmental Technology (IET) – VAST
5. Le Thanh Son, Institute of Environmental Technology (IET) – VAST
6. Nguyen Nguyen Minh, Commonwealth Scientific and Industrial Research Organization (CSIRO), Australia

Abstract

Vietnam has a 3260 km coastline where the seawater quality is affected by activities of agriculture, aquaculture, urbanization, industry, and tourism, raising concerns on coastal ecosystem and social-economic. Hence, monitoring the seawater environment near the coastline is urgently needed. This research exploits the abilities of the support vector machine model to extract surface seawater temperature, total suspended solids, chemical oxygen demand and chlorophyll-a, from Sentinel-2 images processed in the Google Earth Engine platform. The support vector machine model was trained and validated using in-situ measured data and chlorophyll-a data from the National Oceanic and Atmospheric Administration. Model evaluations showed a strong agreement between the modelled and the training data with a mean R² greater than 0.8. The water quality distributions presented poorer water quality near the Rach Gia city and river mouths but improved offshore. We strongly recommend this study method and Sentinel-2 data for water quality studies.

Keywords

Chemical oxygen demand; Chlorophyll-a; Google Earth Engine; Sentinel-2; Total Suspended Solid

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DOI: https://doi.org/10.34238/tnu-jst.5402

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