Improving Water Quality Time-Series Prediction in Hong Kong using Sentinel-2 MSI Data and Google Earth Engine Cloud Computing
<|reference_start|>Improving Water Quality Time-Series Prediction in Hong Kong using Sentinel-2 MSI Data and Google Earth Engine Cloud Computing: 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.<|reference_end|>
@article{sood2024improving,
    title={Improving Water Quality Time-Series Prediction in Hong Kong using
  Sentinel-2 MSI Data and Google Earth Engine Cloud Computing},
    author={Rohin Sood, Kevin Zhu},
    journal={arXiv preprint arXiv:2408.14010},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14010},
    primaryClass={cs.LG}
}