Agricultural Modeling, Remote Sensing and Agriculture
Amor Ines is an agro-hydrologist working on integrations of advanced modeling techniques, remote sensing (RS) and seasonal climate forecasts for decision support in agriculture and water management. He holds a BS in agricultural engineering, magna cum laude, and an MS and PhD in water engineering and management with a focus on irrigation engineering and management, and integrated water resources management.
Linking climate information in agriculture and water management; remote sensing in agriculture and water management; climate-based crop forecasting methods; statistical downscaling of climate (seasonal; climate change time scale); data assimilation applied to soil-water-atmosphere-plant models from field to regional scale; development and applications of evolutionary-biological algorithms (genetic algorithms, etc.) to agriculture and water management; agro-hydrological modeling/vadose zone hydrology (variably saturated flows); high-performance computing for RS-agro-hydrological modeling; stochastic/deterministic water resources systems analyses (optimization and operational management).
Role at the IRI
At the IRI Ines’ work involves development, testing, and evaluation of methodologies to advance climate risk management in agriculture using advanced climate information, remote sensing (RS), crop modeling, data assimilation and soil hydrologic modeling.
Some Useful Tools
How to use video demo. A collaboration with Chubu U., AIT and ListenField Co.
VACC – A vulnerability assessment tool for agro-forestry crops under climate change (Fortran)
H20_Balance_Polygon_V01a – A polygon-based regional crop water balance model (Fortran)
predictWTD – A tool for predicting crop yields at different lead-times in the growing season (Fortran)