Reference the seabed topographic depth observations based on the national mean dynamic topography model.

The mean sea surface in different regions is non-equipotential, rendering Vietnam's traditional approach, which relies on the Hon-Dau tide gauge station as a reference, not yet scientifically invalid. To overcome this, our study utilized the Vietnam national mean dynamic topography model (MDTVN22) for depth observations, particularly in the Gulf of Tonkin. Covering 3430 monitoring sites in Hai Phong and 813 sites in Quang Ninh, our experiments highlighted a 5 to 6 mm difference between the mean sea surface and MDTVN22 references. •Our research establishes a resilient methodology, integrating shore tide gauge station data and the MDTVN22 model, aimed at enhancing precision in depth observations.•Validation experiments in Hai Phong demonstrate a minimal discrepancy of ±0.006 m between measurements obtained from the traditional mean sea surface and the MDTVN22 model.•These findings underscore the significance of adopting the MDTVN22 model for improved accuracy in assessing Vietnam's seabed topography.

Assessing surface water pollution in Hanoi, Vietnam, using remote sensing and machine learning algorithms.

Rapid urbanization led to significant land-use changes and posed threats to surface water bodies worldwide, especially in the Global South. Hanoi, the capital city of Vietnam, has been facing chronic surface water pollution for more than a decade. Developing a methodology to better track and analyze pollutants using available technologies to manage the problem has been imperative. Advancement of machine learning and earth observation systems offers opportunities for tracking water quality indicators, especially the increasing pollutants in the surface water bodies. This study introduces machine learning with the cubist model (ML-CB), which combines optical and RADAR data, and a machine learning algorithm to estimate surface water pollutants including total suspended sediments (TSS), chemical oxygen demand (COD), and biological oxygen demand (BOD). The model was trained using optical (Sentinel-2A and Sentinel-1A) and RADAR satellite images. Results were compared with field survey data using regression models. Results show that the predictive estimates of pollutants based on ML-CB provide significant results. The study offers an alternative water quality monitoring method for managers and urban planners, which could be instrumental in protecting and sustaining the use of surface water resources in Hanoi and other cities of the Global South.