Investigating the use of satellite, aerial, and ground-based remotely sensed data to improve agricultural efficiency and protect natural resources.

Studying the impacts of spatial variability on our ability to measure and understand properties which impact crop development, runoff, soil erosion, and water quality.

Using crop development models to understand the effects of spatially variable properties, and to explore linkages between agricultural production and environmental impacts.

Studying ways in which site-specific farming can optimize production and efficiency while minimizing negative effects on soil and water quality.

Using hydrologic models, in combination with field measurements, to understand the hydrological behavior of water management systems, and to study the influence of alternative management scenarios.

Dr. Amy Kaleita, Laboratory Director

Spatial and temporal variability of field-scale soil moisture

Sensor development for networked moisture and salinity data collection

Environmental impacts and economic tradeoffs of crop residue harvest

Assimilation of sensor network data into agricultural decision support systems

Use of remote sensing for tillage and crop detection and integration into a land management database

Integration of remote sensing and crop growth modeling for nitrogen management in corn

Viability and impact on crop yield of various irrigation schemes in a central Iowa corn field

Analysis of the influence of soil roughness and crusting on soil moisture estimation

Integration of DRAINMOD and DSSAT family of crop models