An Empirical Approach for Detecting Crop Water Stress Using Multispectral Airborne Sensors

Abstract

Irrigation scheduling can be improved by directly monitoring plant water status rather than depending solely on soil water content measurements or modeled evapotranspiration estimates. Plants receiving sufficient water through their roots have cooler leaves than those that are water stressed, leading to the development of the crop water stress index, which uses hand-held infrared thermometers as tools for scheduling irrigations. However, substantial error can occur in partial canopies when a downward-pointing infrared thermometer measures leaf temperature and the temperature of exposed, hot soil. To overcome this weakness, red and near-infrared images were combined mathematically as a vegetation index, which was used to provide a crop-specific measure of vegetative cover. Coupling the vegetation index with the paired radiant surface temperature from a thermal image, a trapezoidal two-dimensional index was empirically derived capable of detecting water stress even with a low percentage of canopy cover. Images acquired with airborne sensors over subsurface drip-irrigated muskmelon (Cucumis melo L.) fields demonstrated the method's ability to detect areas with clogged emitters, insufficient irrigation rate, and system water leaks. Although the procedure needs to be automated for faster image processing, the approach is an advance in irrigation scheduling and water stress detection technology.