AquaVolt-AI: Autonomous Multi-Modal Satellite Fusion and Physics-Informed Machine Learning for Hydrological Downscaling

Traditional precision agriculture and basin-scale hydrological tracking rely on localized, highly expensive physical IoT sensor networks (e.g., Eddy Covariance towers), often proving cost-prohibitive for global deployment. This technical report presents AquaVolt-AI, an autonomous Python-based Artificial Intelligence architecture that programmatically fuses diverse multi-modal satellite layers.

By employing a Physics-Informed Machine Learning (PIML) ensemble model secured by zero-touch cloud automation, the model successfully identifies hyper-local evapotranspiration (ETcETc) dynamics cross-validated with ground-level meteorology.

A subsequent V2.0 upgrade integrates Sentinel-1 SAR (Radar), NASA GRACE (Gravity Anomalies), and Sentinel-5P (Methane) downscaling to isolate deep-earth aquifer depletion and micro-climate stressors across the UC Davis Russell Ranch validation site. The system achieves world-class sub-millimeter accuracy (0.30 mm/day RMSE) at a fundamentally zero-hardware cost.