To enhance the global applicability of the SEN12-WATER dataset and the proposed framework, several strategies can be implemented. First, expanding the geographical coverage to include diverse regions such as arid, semi-arid, tropical, and temperate climates would provide a more comprehensive understanding of water dynamics across different environmental conditions. This could involve collecting satellite data from regions prone to droughts, floods, or other hydrological extremes, thereby enriching the dataset with varied hydrological patterns.
Second, integrating data from additional satellite missions, such as Landsat, MODIS, or PlanetScope, could provide complementary information that enhances the dataset's richness. These satellites offer different spectral resolutions and revisit times, which can be particularly useful for capturing rapid changes in water bodies due to seasonal variations or extreme weather events.
Third, incorporating ground-based observations and local hydrological data can improve the dataset's accuracy and reliability. Collaborating with local agencies and research institutions to gather in-situ measurements of water levels, quality, and usage can provide valuable ground truth data for validating the satellite-derived information.
Lastly, employing machine learning techniques to synthesize data from various sources can help fill gaps in the dataset. For instance, using generative adversarial networks (GANs) to create synthetic data for underrepresented regions can enhance the dataset's diversity and applicability, making it a more powerful tool for global water resource management.