Integration of SWOT and GRACE Satellite data for Hydro-climatic analysis using Advanced Machine Learning Techniques

SWOT monitors surface water bodies such as oceans, rivers, and lakes, offering precise measurements of water levels and flow dynamics, while GRACE tracks changes in Earth's gravity field to monitor terrestrial water storage variations, encompassing groundwater depletion and ice mass loss. However, the integration of multiple satellite data products, particularly SWOT and GRACE, remains relatively unexplored in addressing hydrologic challenges, representing an emerging frontier in both hydrological research and satellite remote sensing. By assimilating SWOT and GRACE datasets, we aim to provide accurate assessments of different hydrologic extremes (e.g., floods, droughts), water availability, and distribution, providing valuable insights for water resource management and climate change adaptation strategies. This could be achieved by harnessing the advantages of advanced machine and deep learning techniques such as transformer-based models. These architectures can effectively capture the spatial and temporal dependencies in multi-source satellite data, extracting relevant features and making accurate predictions about future hydrological conditions.