Ground based remote sensing for irrigation management in precision agriculture

Abstract

The relationship between remotely sensed canopy temperature and soil moisture was studied. The objectives were to relate two remotely sensed canopy temperature-based indices, the Crop Water Stress Index (CWSI) and the Water Deficit Index (WDI), to soil moisture through the water stress coefficient, to estimate soil moisture depletion with the CWSI and the WDI, and to develop a remote sensing system aboard a linear move irrigation system that would provide field images of the WDI at one-meter spatial resolution. Studies were conducted in Maricopa, Arizona during the 1998 and 1999 seasons with cotton (Gossypium hirsutum, Delta Pine 90b). In 1998, the field was surface irrigated (low frequency irrigation), and the CWSI was calculated from canopy temperature measurements using stationary infrared thermometers. In 1999, the field was irrigated with a linear move system (high frequency irrigation), and the WDI was calculated using measurements made by the on board remote sensing system. Both the CWSI and the WDI were correlated to soil moisture through the water stress coefficient. Soil moisture depletion could be estimated using the CWSI under low frequency irrigation, but could not be estimated using the WDI under high frequency irrigation. These differences were attributed to the range of soil moisture resulting from infrequent surface irrigation vs. frequent irrigation using the linear move. High spatial resolution images of the WDI could nonetheless monitor water stress throughout the field from partial to full canopy cover, which demonstrated that ground-based remote sensing is feasible for irrigation management in precision agriculture. This application of remote sensing provides an opportunity to improve water use efficiency.