8 Evaporation
163
This equation underlies most of the significant research effort dedicated to the development of a remote sensing approach to determine areal evaporation.
Imaging spectrometers onboard aircraft and satellites measure top-ofatmosphere radiances which are related to bottom-of-atmosphere radiances but
cannot measure turbulent heat fluxes at the land atmosphere interface. Literature
tells us another story, however, given the large numbers of scientific papers and
technical reports dealing with the use of measurements of reflectance and emittance to obtain heat fluxes and particularly latent heat flux. Much of this tradition
was initiated in the early 70-s by agricultural scientists studying the physical climate of crops, with special reference to irrigation.
The dependence of canopy temperature on solar radiation was studied by Stone
et al. (1975) and early studies on the estimation of evaporation were presented by
Heilman et al. (1976). The interrelation of heat balance with crop yield and soil
water balance was addressed by Hatfield et al. (1978). The Heat Capacity Mapping Mission (HCMM), a small, low cost and focussed mission (1978-1980)
boosted the interest of the scientific community for heat balance studies in relation
with land surface hydrology. A detailed overview of the results of HCMM- and
evaporation related results was presented by Reiniger and Seguin (1986). The
relatively low spatial resolution of the HCMM radiometer led to consider large
heterogeneous areas and the first conceptual and practical difficulties with the application of the early algorithms intended for homogeneous agricultural patches
appeared. Moran and Jackson (1991) reviewed methods to estimate the spatial
distribution of evaporation using remote measurements of surface temperature in
combination with meteorological observations. The method assumes that observations of e.g. air temperature and humidity are available at the proper areal density,
which is a relatively safe assumption when dealing with agricultural areas. In many
other cases such as Alaska (Gurney and Hall, 1983) or the Libyan desert (Menenti,
1984) feasible approaches had to be based on variables observable with airborne
or spaceborne radiometers only. In another review Menenti (1993) addressed the
issue of the reference air temperature and of the inherent correlations in the spatial
variability of land surface properties. Air temperature at some higher elevation in
the atmospheric boundary layer has a limited spatial variability because of mixing
and, therefore, is a suitable reference temperature for heat balance studies of heterogeneous land surfaces. This concept was demonstrated by Brutsaert et al.
(1992) and Menenti and Choudhury (1993).
Water balance- soil. The water balance equation of a soil column reads:
P + I + Q + 15m + Ow + E = 0
(kg m- 2 (1)
(8.5)
where P is precipitation, I is inflow (capillary rise and lateral infiltration), Q is
runoff, om is change in soil water storage, ow is change in snow water equivalent
and E is actual evaporation. It appears that the terms ofEq. (8.5) are hardly accessible with sensors on board satellites. Choudhury and DiGirolamo (1998) computed global evaporation at low spatial resolution (2.5° x 2.5°) with a biophysical
process model based on Eq.(8.1) and a set of process- sub-models. Satellite obser-
163
This equation underlies most of the significant research effort dedicated to the development of a remote sensing approach to determine areal evaporation.
Imaging spectrometers onboard aircraft and satellites measure top-ofatmosphere radiances which are related to bottom-of-atmosphere radiances but
cannot measure turbulent heat fluxes at the land atmosphere interface. Literature
tells us another story, however, given the large numbers of scientific papers and
technical reports dealing with the use of measurements of reflectance and emittance to obtain heat fluxes and particularly latent heat flux. Much of this tradition
was initiated in the early 70-s by agricultural scientists studying the physical climate of crops, with special reference to irrigation.
The dependence of canopy temperature on solar radiation was studied by Stone
et al. (1975) and early studies on the estimation of evaporation were presented by
Heilman et al. (1976). The interrelation of heat balance with crop yield and soil
water balance was addressed by Hatfield et al. (1978). The Heat Capacity Mapping Mission (HCMM), a small, low cost and focussed mission (1978-1980)
boosted the interest of the scientific community for heat balance studies in relation
with land surface hydrology. A detailed overview of the results of HCMM- and
evaporation related results was presented by Reiniger and Seguin (1986). The
relatively low spatial resolution of the HCMM radiometer led to consider large
heterogeneous areas and the first conceptual and practical difficulties with the application of the early algorithms intended for homogeneous agricultural patches
appeared. Moran and Jackson (1991) reviewed methods to estimate the spatial
distribution of evaporation using remote measurements of surface temperature in
combination with meteorological observations. The method assumes that observations of e.g. air temperature and humidity are available at the proper areal density,
which is a relatively safe assumption when dealing with agricultural areas. In many
other cases such as Alaska (Gurney and Hall, 1983) or the Libyan desert (Menenti,
1984) feasible approaches had to be based on variables observable with airborne
or spaceborne radiometers only. In another review Menenti (1993) addressed the
issue of the reference air temperature and of the inherent correlations in the spatial
variability of land surface properties. Air temperature at some higher elevation in
the atmospheric boundary layer has a limited spatial variability because of mixing
and, therefore, is a suitable reference temperature for heat balance studies of heterogeneous land surfaces. This concept was demonstrated by Brutsaert et al.
(1992) and Menenti and Choudhury (1993).
Water balance- soil. The water balance equation of a soil column reads:
P + I + Q + 15m + Ow + E = 0
(kg m- 2 (1)
(8.5)
where P is precipitation, I is inflow (capillary rise and lateral infiltration), Q is
runoff, om is change in soil water storage, ow is change in snow water equivalent
and E is actual evaporation. It appears that the terms ofEq. (8.5) are hardly accessible with sensors on board satellites. Choudhury and DiGirolamo (1998) computed global evaporation at low spatial resolution (2.5° x 2.5°) with a biophysical
process model based on Eq.(8.1) and a set of process- sub-models. Satellite obser-