About Water Data Models and Decision (WaterDMD)

WaterDMD builds digital twins of complex water systems — virtual models that integrate natural and human components. Using AI, drone monitoring, hyperspectral imaging, and satellite Earth observations, we develop models that simulate and predict the behavior of interconnected water, agriculture, and urban systems.

Current Research Themes

• Continental Scale Crop Land Cover Land Use Classification (CLCLU) Deep learning and high-resolution satellite data to classify crop types and land use at continental scale — supporting food security and resource management.
• Global Soil Carbon Models Integrating satellite and ground measurements to model current and attainable soil carbon stocks globally, improving estimates of sequestration potential.
• Hyperspectral Imaging for Crop Stress Detecting early crop stress from drought, pests, and nutrient deficiency using hyperspectral sensors that capture changes invisible to the naked eye.
• AI Models for Streamflow Predictions Machine learning models for streamflow prediction using hydrological, meteorological, and geospatial data — supporting flood forecasting and drought preparedness.
• Assimilation of Aerial and SpaceBorne Earth Observations Assimilating aerial and satellite observations (soil moisture, ET, vegetation indices) into crop and water models to improve forecast accuracy.
• Stakeholder Mental Models Understanding how stakeholders perceive complex water systems, and integrating those perspectives into our models to improve real-world relevance.