Application of Google Earth Engine to automated surface water quality monitoring in the Red River and Duong River basins

Abstract

Purpose. To develop an automated cloud-based application to monitor surface water quality using Google Earth Engine and multi-source satellite imagery for major river systems, with application to the Red River and Duong River in Vietnam. Methodology. The study was conducted using multi-source satellite data from Sentinel-2 MSI and Landsat 8/9 within the Google Earth Engine environment. Sentinel-2 MSI imagery was used to estimate total suspended solids (TSS) and water turbidity, whereas the thermal bands of Landsat 8/9 were used to calculate water surface temperature. Data processing included filtering satellite imagery by study area and acquisition period, spectral analysis, calculation of the relevant water quality parameters, and cartographic visualization of their spatial distribution. The satellite-derived values were compared with field measurements collected along the Red River and Duong River sections within Hanoi, Vietnam. Findings. The results showed distinct spatial and seasonal patterns of TSS, turbidity, and water surface temperature. Comparisons with field measurements demonstrated acceptable agreement between satellite-derived estimates and observed data, supporting the applicability of the proposed approach for river-basin-scale water quality assessment. Originality. This study presents an integrated Google Earth Engine-based framework that combines multi-source remote sensing data with field observations to support automated, scalable surface water quality monitoring in Vietnam. Practical value. The developed application can be applied efficiently for rapid, scalable, and cost-effective water quality monitoring, particularly in data-scarce regions and large river basins where conventional monitoring networks are insufficient.