**1.1 Objective**

*Advanced Evapotranspiration Methods and Applications*

areas and within irrigated projects is important for solving water right disputes, hydrologic water balances, and water resources planning. Estimation of actual ET at relatively high spatial resolutions is of interest to agriculture, water resources

With the availability of free satellite imagery, especially Landsat, there has been substantial investigation to retrieve actual evapotranspiration (ET) over large areas from remotely sensed data. The major advantage of applying remote sensing is that ET can be computed directly without the need for quantifying other complex hydrological processes. A detailed review of remote sensing algorithms to estimate ET are presented in Kustas and Norman [1], Bastiaanssen [2], Courault et al. [3], and Kalma et al. [4]. There are two general approaches to estimate ET via remote sensing: (a) scaling ET based on a vegetation index [5, 6] and (b) using thermal information to drive a surface energy balance [7, 8] or to more simply scale the ET values [9]. The thermal approach is the only one that can effectively estimate ET from waterstressed vegetation as well as evaporation from wet soil when using a surface energy balance [10]. The estimation of ET implies the use of remotely sensed spectral data, thermal imagery, and ground-based meteorological inputs to evaluate net radiation (Rn), sensible heat (H), and soil heat flux (G) components of the surface energy balance to obtain latent heat flux (LE) as the residual from the energy balance. Some

Many applications in water resources planning, hydrological modeling, and

One approach for estimating monthly and seasonal ET from a given number of satellite-derived ET maps is based on the construction of a crop coefficient curve, for every pixel, similar to the proposed by FAO-56 [11]. In this approach, satellite-derived ET is converted to alfalfa reference ET fraction (ETrF = ET/ETr) or grass reference ET fraction (EToF = ET/ETo) by dividing ET to alfalfa reference evapotranspiration (ETr) or grass reference evapotranspiration (ETo), respectively. Basically, each ET image would provide one point of the ETrF or EToF curve. The rest of the curve is later completed by interpolation (linear, spline, or other method), providing ETrF (or EToF) for every day during the growing season. Finally, daily ETrF (or EToF) is multiplied by daily ETr (or ETo) to produce daily ET,

Allen et al. [12] used METRIC [13] and interpolation of daily alfalfa reference ET fraction (ETrF) for computing seasonal ET in Southern Idaho. This approach resulted is less than 3% difference on seasonal ET when compared to lysimeter data [11]. The authors attributed this good estimation of seasonal ET to the random distribution of daily ET from the METRIC model. Chavez et al. [14] used interpolation of grass reference ET fraction (EToF) to estimate ET in between

Singh et al. [15] employed three different methods of ETrF interpolation to compute seasonal ET for 6 months (July–December) and compare these values with daily ET measurements collected with eddy covariance in Nebraska. The first method assumed that ETrF on each acquired image date was constant during a representative period for daily ET computation. The second method involved linear

agricultural water management require seasonal/annual ET estimates. The determination of seasonal ET based on remote sensing data is very challenging when daily ET is not available due to temporal resolution of satellites (revisiting) and/or gaps in imagine acquisition due to cloud cover. The methods discussed in the previous paragraph are useful to estimate ET for the days when cloud-free satellite imagery is available, which generally represents just a small portion of the total number of days during the growing season. For that reason, methods are needed to extrapolate and/or interpolate those ET snapshots to represent the

management, and can serve as an indicator of crop water deficits.

information is commonly supplied by a soil water balance [10].

which can be summarized into monthly and seasonal values.

**48**

whole growing season.

satellite overpasses.

The objective of this study was to explore the improvement in accuracy of estimates for ET over complete growing seasons and for monthly periods, when more frequent Landsat imagery is made available.

The study was implemented by conducting a series of METRIC applications for a Landsat WRS path overlap area in southern Idaho (paths 39 and 40) during a period (year 2000) when two fully functioning satellites, Landsat 5 and Landsat 7, were in orbit. During that year, Landsat 5 (L5) and Landsat 7 (L7) passed over the overlap area twice, each, per 16 day period, providing four imaging opportunities every 16 days. Monthly and growing season ET was integrated using all available cloud-free imagery during the April–October growing period to provide a baseline representing our most accurate estimate. The frequency of imagery was then sparsened by removing imagery from one path or the other and by removing imagery from one satellite or the other. Monthly and seasonal ETs were then recomputed with the sparsened image series and compared with the baseline data.
