Submitted to: Journal of Irrigation and Drainage Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/16/2011
Publication Date: N/A
Citation: Interpretive Summary: Conserving and using water effectively on irrigated lands is important for coping with scarcity and competitive demands from urban users. To do this, farmers need to know how much water is needed by their crops and how much is lost through evapotranspiration. In recent years techniques have been developed to monitor water use through remote sensing. The techniques can be accurate and help schedule irrigations, but they often cannot be used because not enough satellite data are available. Reasons include poor resolution, cloudy skies, and high cost. A method is described that reduces this problem by combining satellite data, as they become available, with inexpensive ground observations of weather and land surface temperatures. The method used is known as data assimilation. The study showed that evapotranspiration can be forecast with good reliability. The results from this research will be useful for water managers and irrigation engineers seeking ways to conserve and map water use over crops.
Technical Abstract: Estimation of spatially distributed evapotranspiration (ET) with remote sensing could be especially valuable for developing water management tools in arid lands. For decision support over irrigated crops, these spatial ET estimates also depend upon good spatial resolution ($<$30 m)at timely intervals, which for practical operations means no less frequent than approximately five days. For a variety of reasons, current remote sensing platforms usually cannot meet these needs. Commonly overpass frequencies are no better than 16 days, and sometimes are much worse considering cloudy skies. One way to reduce this problem is to develop an ET estimation approach that utilizes both remotely sensed data and ground-based observations. By combining episodic spatially distributed data with temporally continuous point observations, it could be feasible to provide continuous ET estimates that are better than can be achieved with either technique alone. Using data from a remote sensing irrigation scheduling experiment over cotton, conducted in 2003 at Maricopa, Arizona, an ET modeling approach was developed that used airborne images of vegetation indices (NDVI) and land surface temperatures (LST) along with ground-based thermal infrared radiometry and meteorology. Fractional vegetative cover were forecast from NDVI at daily time steps using a linear Kalman filter consisting of prior data, cumulative heat units, and spatially oriented Beta distribution functions. LST were forecast hourly using a diurnal temperature model and a linear cover/LST estimator.ET accuracies derived from using these data as inputs to a surface energy balance model showed good agreement with independent ET estimates determined from 5-day soil depletion observations. Increased ET due to increased crop water use and to irrigation applications were reflected in model outputs, and sometimes agreement was within 10% of independently observed soil moisture depletion data sets. These results indicated that combining remote sensing and ground-based datasets could be a feasible way to estimate ET at field-scales at daily time steps.