A research team from the Hong Kong University of Science and Technology (HKUST) has developed an innovative AI-powered tool called STIMP, designed to reconstruct missing data and predict the health of coastal ocean ecosystems. The tool leverages machine learning to fill in gaps in environmental monitoring data, enabling more accurate assessments of coastal ocean productivity and ecological conditions. Coastal regions are among the most dynamic and vulnerable marine environments, yet they often suffer from inconsistent or incomplete data due to logistical challenges in monitoring. STIMP addresses this issue by using advanced algorithms to infer missing information based on available observations, historical patterns, and spatial-temporal correlations across oceanic variables such as temperature, salinity, chlorophyll levels, and nutrient concentrations. By integrating satellite data, in-situ measurements, and oceanographic models, STIMP enhances the resolution and reliability of coastal ocean assessments. The system can identify early signs of ecosystem stress, such as algal blooms, hypoxia, or shifts in species distribution, allowing for timely interventions to protect marine biodiversity and support sustainable fisheries. The tool is particularly valuable for policymakers, environmental agencies, and conservationists who rely on accurate, real-time data to make informed decisions. Its ability to operate in data-scarce regions makes it a powerful asset for monitoring coastal zones in developing nations and remote areas where traditional monitoring infrastructure is limited. The HKUST team believes STIMP could play a key role in global efforts to safeguard ocean health amid growing pressures from climate change, pollution, and overfishing. Ongoing improvements aim to expand its capabilities to include real-time forecasting and integration with broader Earth observation systems.