Dynamic Variable Rate Irrigation Management Using Soil Moisture and Canopy Temperature Sensors in the Southeastern USA.

Currently variable rate irrigation (VRI) prescription maps used to apply water differentially to irrigation management zones (IMZs) are static. They are developed once and used thereafter and thus do not respond to environmental variables which affect soil moisture and crop conditions. To date our approach for creating dynamic VRI prescription maps which respond to environmental conditions has been to use soil moisture sensor networks to estimate the amount of irrigation water needed to return each IMZ to an ideal soil moisture condition. We developed and commercialized the UGA Smart Sensor Array (UGA SSA) which is an inexpensive wireless soil moisture sensing network which allows for a high density of sensor probes. Each probe includes three Watermark sensors. We use a modified van Genuchten model and soil matric potential data from each probe to estimate the volume of irrigation water needed to bring the soil profile of each IMZ back to 75% of field capacity. These estimates are converted into daily prescription maps which we downloaded remotely to a VRI controller thus creating a dynamic VRI control system. However, this system does not address the temporal changes in IMZ boundaries and shapes associated with changing environmental conditions. We have recently demonstrated a strong relationship between canopy temperature-derived crop water stress index (CWSI) and leaf water potential (LWP) for cotton and peanut in the humid southeastern USA. This provides the potential for using remotely-sensed thermal imagery of crop canopies to develop dynamic IMZs. We are now developing methods for combining our soil moisture sensing network and remotely-sensed thermal imagery of crop canopies to create a truly dynamic VRI system. This presentation describes our work.