**1. Introduction**

Evapotranspiration (ET) transfers large volume of water from soil and vegetation into the atmosphere. Quantifying the consumption of water over large

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

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

Many applications in water resources planning, hydrological modeling, and 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 whole growing season.

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, which can be summarized into monthly and seasonal values.

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 satellite overpasses.

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

**49**

**1.1 Objective**

*Influence of Landsat Revisit Frequency on Time-Integration of Evapotranspiration…*

the cubic spline method resulted in the lowest standard error.

interpolation of ETrF in between two consecutive images; the hypothesis here was that the errors caused by underestimation or overestimation of daily ET are canceled out while computing seasonal ET. These methods are convenient if satellite images are available at regular intervals. The third interpolation method used was a cubic spline of the ETrF values. The spline method is the procedure that better mimic the natural behavior of the crop coefficient curve. The results indicated that there was no statistically significant difference among the three methods; overall,

Mohamed et al. [16] used SEBAL [17] to describe the temporal variability of ET in swamps of the upper Nile. The authors estimate ET during days with no satellite image by assuming that the daily ratio of daily evaporation and reference evapotranspiration (Kc = ET/ETo) could be kept constant during the month. ETo represents the grass-based reference evapotranspiration calculated using Allen et al.

Bashir et al. [18] used LANDSAT and MODIS imagery to estimate the spatial distribution of daily, monthly, and seasonal ET for irrigated Sorghum in the Gezira scheme, Sudan. The authors used SEBAL to estimate daily ET. The monthly and seasonal ET was computed by linearly interpolating the ratio of ET and grass reference ETo (EToF) in between two consecutive images; the estimation of seasonal ET by SEBAL and EToF interpolation was within 8% of an estimation of seasonal ET from water balance. A second approach that is implemented to generate seasonal or annual ET utilized soil-vegetation-atmosphere transfer (SVAT) models to estimate ET in between satellite dates. Olioso et al. [19] combined remote sensing inputs and a SVAT model to estimate ET and photosynthesis. The authors indicate that is useful to assimilate remote sensing data into SVAT models, which are able to give access to a detailed description of soil and vegetation canopy processes. SVAT models are capable of simulating intermediary variables linked to hydrological and physiological processes. Various remote sensing data may be used to drive those SVAT models. Spectral reflectance in the visible and near infrared portions of the spectrum can provide information on the structure and characteristics of the vegetation canopy, such as LAI and albedo. Thermal remote sensing data can be used as indirect indicators of moisture in the soil or vegetative surface. Dhungel et al. [20] proposed a surface energy balance model that uses gridded weather data to interpolate ET between two consecutive satellite dates; bulk surface resistance for satellite dates was obtained by inversion of the Penman-Monteith equation, where ET came from

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

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.

*DOI: http://dx.doi.org/10.5772/intechopen.80946*

[11] and ET was calculated using SEBAL.

application of the METRIC model on Landsat images.

frequent Landsat imagery is made available.
