Research
Integrating hydrological modeling, remote sensing, and AI to advance sustainable water management in agricultural systems.
Evapotranspiration & Crop Water Use
Multi-site ET monitoring using eddy covariance, weighing lysimeters, and remote sensing across Oregon Branch Experiment Stations. We develop Oregon-specific crop coefficients (Kc) and crop water production functions under deficit irrigation for seed potato, Kentucky bluegrass, hazelnut, mint, and other specialty crops.
CascadeFlux ET Network — A ORBWI-funded multi-site framework at COAREC, Malheur Experiment Station, and Klamath Basin Research & Extension Center providing real-time ET data and crop coefficient validation for Oregon irrigated agriculture.
Eddy Covariance
Flux tower deployment and data processing for actual ET measurement at field scale.
OpenET Validation
Regional-scale ET validation using satellite-based OpenET platform and ground-truth data.
Crop Coefficients
Oregon-specific Kc development for seed potato, bluegrass, hazelnut, and mint.
Precision Irrigation & Decision Support
Variable rate irrigation (VRI) design, sensor-based scheduling, and deficit irrigation strategy development for semi-arid cropping systems. We develop farmer-facing decision support tools (DSS) that bridge complex models with real-time field management decisions.
VRI Design
Variable rate irrigation system design and prescription mapping for center pivot systems.
Soil Moisture Sensing
METER equipment deployment for real-time soil water monitoring and irrigation scheduling.
DSS Tools
Web-based decision support systems for irrigation scheduling accessible to producers and extension agents.
Water–Nitrogen Dynamics
Integrated water and nutrient management targeting nitrate leaching reduction and groundwater quality protection in irrigated landscapes. Our P-RDI (Partial Root-zone Drying Irrigation) module and DNA algorithm enable simultaneous optimization of water productivity and nitrogen use efficiency.
AI, Modeling & Decision Intelligence
Physics-informed neural networks (PINN), LSTM, Random Forest, and surrogate models for computationally intensive APEX, SWAT+, DSSAT, HYDRUS-1D, and MODFLOW simulations. We integrate machine learning with process-based models to enable real-time agricultural decision support at farm to watershed scale.
APEX / SWAT+
Field-to-watershed scale modeling of water balance, nutrient cycling, and crop production.
DSSAT & HYDRUS
Crop growth modeling and vadose zone water flow simulation for field-scale studies.
Transfer Learning
Cross-site model transfer for data-scarce environments including Haiti and Vietnam.
Remote Sensing & Geospatial Analysis
Drone-based thermal and multispectral imagery (Sentera 6X Thermal Pro, Inspired Flight IF800 RTK/PPK), satellite remote sensing, and GIS for crop stress detection, ET mapping, and regional water accounting in irrigated agricultural landscapes.
Climate Adaptation & International Work
Assessment of climate change impacts on crop yield and water demand using DSSAT, APEX, and SWAT+ across contrasting environments. Current collaborations with Nong Lam University (Vietnam) on AI-GIS for climate-resilient agriculture, and ongoing connections to Haiti's Artibonite Valley rice systems through the FAMV alumni network.