Submitted to: Journal of Irrigation and Drainage Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/16/2002
Publication Date: 2/1/2003
Citation: COLAIZZI, P.D., BARNES, E.M., CLARKE, T.R., CHOI, C.Y., WALLER, P.M., HABERLAND, J., KOSTRZEWSKI, M. 2003. WATER STRESS DETECTION UNDER HIGH FREQUENCY SPRINKLER IRRIGATION WITH WATER DEFICIT INDEX. JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERINg,. 129(1): 36-43. Interpretive Summary: New irrigation technologies now allow for more accurate control of irrigation water using traveling sprinkler systems that have the ability to spatially vary the amount of water applied. A management challenge to this type of irrigation system is the determination of variances in crop water needs across the field. Existing crop water stress indices derived from remotely sensed data have demonstrated the potential to determine when to irrigate, but these indices do not directly provided an estimate of how much water to apply. In this study, a new approach was developed that combines remotely sensed data and a more traditional method of predicting crop water use to infer soil moisture content in the root zone. The results of this study show promise to provide farmers and irrigation managers who wish to adopt variable rate irrigation systems with a cost effective management tool. Additionally, the ability to better determine crop water status will promote better water agricultural water conservation.
Technical Abstract: A remote sensing package aboard a linear move irrigation system was developed to simultaneously monitor water stress, nitrogen stress, and canopy density at one-meter spatial resolution. The system is called the Agricultural Irrigation Imaging System (AgIIS). The present study investigated the relationship between water stress detected by AgIIS and soil moisture for the 1999 cotton (Gossypium hirsutum, Delta Pine 90b) season in Maricopa, Arizona. Water stress was quantified by the Water Deficit Index (WDI), an expansion of the Crop Water Stress Index (CWSI) where the influence of soil temperature is accounted for through a linear mixing model of soil and vegetation temperature. The WDI was best correlated to soil moisture through the FAO 56 water stress coefficient (Ks) model; stability correction of aerodynamic resistance did not improve correlation. AgIIS could provide field images of the WDI that might aid irrigation scheduling and increase water use efficiency.