*Influence of Landsat Revisit Frequency on Time-Integration of Evapotranspiration… DOI: http://dx.doi.org/10.5772/intechopen.80946*

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, the cubic spline method resulted in the lowest standard error.

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. [11] and ET was calculated using SEBAL.

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 application of the METRIC model on Landsat images.
