Evapotranspiration

In general, remote-sensing techniques cannot measure evaporation or évapotranspiration directly. However, remote sensing does have two potentially very important roles in estimating évapotranspiration. First, remotely sensed measurements offer methods for extending point measurements or empirical relationships, such as the Thornthwaite (1948), Penman (1948), and Jensen and Haise (1963) methods, to much larger areas, including those areas where measured meteorological data may be sparse. Secondly, remotely sensed measurements may be used to measure variables in the energy and moisture balance models of évapotranspiration. Although there has been progress made in the direct remote sensing of the atmospheric parameters that affect évapotranspiration, such as the Rahman LIDAR, this is essentially a ground-based, point measurement and will not be covered in this report.

The question of how to use the spatial nature of remote-sensing data to extrapolate point évapotranspiration measurements to a more regional scale has been addressed in several ways. Using the temperature sounders on the meteorological satellites in a linear regression model, Davis and Tarpley (1983) estimated shelter temperatures with an error of about 2 K for clear or partly cloudy conditions. Price (1982) used thermal data from the Heat Capacity Mapping Mission (HCMM) to estimate regional-scale évapotranspiration rates, which were found to be comparable to pan evaporation data. Jackson (1985) and Gash (1987) have proposed an analytical framework for relating the horizontal changes in evaporation to horizontal changes in surface temperature. Kustas et al. (1990) demonstrated these concepts for an agricultural area under clear sky conditions. Humes et al. (1994) has proposed a simple model using remotely sensed surface temperatures and reflectances for extrapolating energy fluxes from a point to a regional scale; however, other than for clear sky conditions, variations in incoming solar radiation, meteorological conditions, and surface roughness limit this approach.

Several variables related to the energy balance equation can be measured by remote sensing and simple meteorological measurements. Generally, the latent heat term is determined as the residual of the other terms in the energy balance. Incoming solar radiation can be estimated from satellite observations of cloud cover, primarily from geosynchronous satellites (Brakke and Kanemasu, 1981; Tarpley, 1979). Pinker and Laszlo (1992) have proposed a model that infers incoming short-wave fluxes and surface albedos from GOES data. Pinker et al. (1994) used this model to demonstrate that incoming shortwave radiation can be measured quite accurately, even under variable cloud conditions, at the basin scale.

For clear sky conditions, the surface albedo may be estimated by measurements covering the entire visible and near-infrared waveband, while empirical relations using narrow spectral bands can be used to determine albedo over heterogeneous surfaces (Jackson, 1985; Brest and Goward, 1987). Although albedo estimated this way is not the true hemispherical albedo, lack of directional data or simple models make this correction not feasible under most applications.

Surface temperature can be estimated from measurements in thermal infrared wavelengths, that is, the 10.5- to 12.5-nm waveband, either assuming a surface emissivity (close to unity for natural surfaces) or having measured values of the surface emissivity. Surface temperatures can be used to estimate the outgoing longwave radiation term in the net radiation equation (Kustas et al., 1994).

The soil heat flux term can be estimated with remote-sensing measurements. A simplified approach defines the ratio of soil heat flux to net radiation in terms of vegetation cover, which, in turn, is determined from visible and near-infrared measurements (Clothier et al., 1986; Choudhury et al., 1987; Kustas and Daughtry, 1990). The diurnal effects (Owe and van de Grind, 1990) and influence of soil moisture (Brutsaert, 1982) are assumed to be secondary for large areas (Kustas et al., 1994).

The sensible heat flux can be estimated using several approaches, including the bulk resistance approach proposed by Monteith (1973) and similarity principles for the unstable boundary layer (Brutsaert and Sugita, 1992), where the surface temperatures are measured by remote sensing. These approaches have met with varying degrees of success (Hall et al., 1992; Brutsaert and Sugita, 1992; Brutsaert et al., 1993; Kustas et al., 1994).

Additional approaches for estimating ET from remote sensing data are being explored. Ottle et al. (1989) have shown how satellite-derived surface temperatures can be used to estimate ET and soil moisture in a model that has been modified to use these data. Mauser (1990) has shown how multitemporal Système Probatoire pour l'Observation de la Terre (SPOT) and Thematic Mapper (TM) data to derive plant parameters for estimating ET in a GIS-based model. Later, Mauser (1996) used Advanced Very High Resolution Radiometer (AVHRR) thermal data to validate an actual ET mesoscale model by comparing them to the surface temperature distributions. Soares et al. (1988) demonstrated how thermal infrared and C-band radar could be used to estimate bare soil evaporation. Choudhury et al. (1994) have shown strong relationships between evaporation coefficients and vegetative indices.

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