Submitted to: International Evapotranspiration Irrigation Scheduling Conference
Publication Type: Proceedings
Publication Acceptance Date: August 5, 1996
Publication Date: N/A
Interpretive Summary: Knowledge of crop water loss (evaporation) is essential for making reasonable crop irrigation decisions. However, crop evaporation rates are rarely measured because conventional instrumentation is very expensive and difficult to install and maintain. As an alternative, crop evaporation rates could be measured indirectly using airborne cameras to provide images of crop reflectance, temperature, and response to active radar beams. A study was conducted to develop procedures based on such images to map evaporation rates of alfalfa and cotton crops in Arizona. Preliminary results showed that evaporation rates could be estimated based on either images of crop reflectance and temperature or images of radar. The radar-based approach had the advantage of applications during both cloudy and clear-sky conditions, unlike the approach based on measurements of surface reflectance and temperature, which can be obtained only under clear-sky conditions. Both methods have potential for use by farmers, crop consultants, researchers, and agricultural support agencies for monitoring of crop evaporation rates for improving water use efficiency and crop yield.
In 1994, Cemagref-ENGREF Remote Sensing Laboratory and U.S. Water Conservation Laboratory conducted an experiment called "MAC VII" on Maricopa Agricultural Center, to demonstrate the ability of using combined optical and microwave remote sensing data for estimating the evapotranspiration of irrigated crops. Two approaches were developed on alfalfa and cotton crops. One is based on the combination of remotely sensed surface temperature and vegetation index representing both vegetation water stress and fractional cover, and providing a water deficit index (WDI), which has been demonstrated to be a reliable extension of the Crop Water Stress Index (CWSI) concept to partial coverage canopies. The estimation of WDI was improved here by using surface soil moisture derived from ERS-1 radar satellite C-Band data, which provided the ability to discriminate canopy transpiration from soil evaporation. The other approach is based on the combination of airborne Ku- and C-Band radar imagery, which represent both vegetation fractional cover and water stress, respectively. This new tool is useful for estimating plant water stress in partial coverage conditions and has the advantage of being independent of atmospheric and cloudy conditions, unlike approaches based on optical remote sensing data.