This study proposed a comprehensive approach that utilized PlanetScope imagery for classifying tropical-marine benthic habitats after retrieving bathymetry from ICESat-2 data and water-column correction for areas around Lyson Islands, Vietnam. Exact bathymetry derivation and water column correction were applied to the PlanetScope imagery, making it an effective method for mapping marine benthic habitats. Water column correction was achieved by applying Depth Invariant Index (DII) and Bottom Reflectance Index (BRI). Moreover, two conventional machine learning algorithms, including Random Forest and Support Vector Machine, and a current deep Convolutional Neural Network (CNN) was employed to classify the benthic features. The overall accuracy of these classifiers are 80.74%, 84.19%, and 89.80% with the BRI, 80.17%, 82.75%, and 87.85% with the DII compared to 37.64%, 42.5%, and 47.2% of without corrected water columns respectively. The CNN model demonstrated that the approach significantly maximizes the improvement in benthic classification results in coastal region.

Monitoring the drought progression over time is a critical responsibility to identify the changing trends of this phenomenon. This study aims to assess drought hazard based on the Standardized Precipitation Index (SPI) using satellite-derived precipitation combined with ground station data on the Google Earth Engine cloud computing platform. The study employs the Mann-Kendall test and Sen's slope method to evaluate the reliability of the SPI 1, 3, 6, 9, and SPI12 indices. The results show that SPI12 values have the highest reliability, 12/12 months having p values <0.05. In addition, the study also combines rainfall data collected from the Center for Hydrometeorology and Remote Sensing (CHRS) and field observational data to assess the correlation with SPI calculated from CHIRPS (Climate Hazards Group InfraRed Precipitation). Results demonstrate that the SPI12 derived from CHIRPS correlates well with SPI12 from CHRS and observational data, as evidenced by the statistical coefficients. Furthermore, the study simulated the characteristics of droughts across the Hoa Vang district based on the Inverse Distance Weighting interpolation to assess the fluctuations of SPI values. The research findings facilitate understanding the impacts of droughts on the natural environment and socio-economics, especially in the agricultural sector in Hoa Vang district, Da Nang City.