My work combines agricultural engineering, hydrology, geophysics, remote sensing, and machine learning to improve irrigation efficiency, resource-use sustainability, and crop productivity.
At UC Davis, I lead and contribute to multidisciplinary projects that connect measurements from the root zone, plant, canopy, and atmosphere. These measurements are integrated with spatial observations and data-driven models to diagnose variability and support management decisions.
The work spans almonds, walnuts, pistachios, grapes, citrus, processing tomatoes, and other cropping systems, with emphasis on technologies that can move from research plots toward commercial agriculture.
Integrating plant water status, soil water storage, evapotranspiration, and irrigation records to understand crop response and improve scheduling. Research emphasizes perennial orchards and high-value crops where spatial variability and water limitation strongly influence management.
Designing and evaluating field monitoring systems that connect soil, plant, and atmospheric measurements. Instrumentation includes eddy covariance flux towers, soil moisture and water potential sensors, sap-flow systems, dendrometers, weather stations, and wireless sensor networks.
Using satellite and aerial imagery together with machine learning to map evapotranspiration, crop water stress, soil moisture, and field variability. The goal is to combine point measurements with scalable spatial information while maintaining interpretable and defensible models.
Applying electrical resistivity tomography, electromagnetic induction, soil-water balance modeling, and related methods to characterize infiltration, root-zone dynamics, soil heterogeneity, and water redistribution in agricultural soils.
Pressure chamber, sap flow, dendrometers, microtensiometers, canopy temperature
Volumetric water content, water potential, neutron probe, ERT, EM-38, salinity
Eddy covariance, weather stations, radiation, VPD, surface energy balance
Satellite, aerial imagery, multispectral/thermal data, geospatial processing
Machine learning, FAO-56 water balance, energy balance, spatial prediction
Data integration, visualization, irrigation analytics, web-based tools