Improving Water Quality Time-Series Prediction in Hong Kong using Sentinel-2 MSI Data and Google Earth Engine Cloud Computing

TL;DR

This study develops LSTM-based models using Sentinel-2 satellite data and Google Earth Engine to improve water quality prediction accuracy in Hong Kong's coastal regions, enabling better monitoring of pollutants like chlorophyll-a, suspended solids, and turbidity.

Contribution

It introduces a novel approach combining Sentinel-2 spectral data, GEE cloud computing, and LSTM models for enhanced water quality time-series prediction in coastal areas.

Findings

Improved prediction accuracy over previous methods

Effective use of Sentinel-2 spectral data for water quality parameters

Demonstrated potential for remote sensing in continuous water monitoring

Abstract

Effective water quality monitoring in coastal regions is crucial due to the progressive deterioration caused by pollution and human activities. To address this, this study develops time-series models to predict chlorophyll-a (Chl-a), suspended solids (SS), and turbidity using Sentinel-2 satellite data and Google Earth Engine (GEE) in the coastal regions of Hong Kong. Leveraging Long Short-Term Memory (LSTM) Recurrent Neural Networks, the study incorporates extensive temporal datasets to enhance prediction accuracy. The models utilize spectral data from Sentinel-2, focusing on optically active components, and demonstrate that selected variables closely align with the spectral characteristics of Chl-a and SS. The results indicate improved predictive performance over previous methods, highlighting the potential for remote sensing technology in continuous and comprehensive water quality assessment.