Télédétection et ressources en eau/Remote sensing and water resources
325
and the canopy. However other formulations for deriving soil under the shrubs temperature were
also tested. Wind speed was measured at 1.25 and 2.25 m above the soil surface using photochopper anemometers with a threshold of 0.2 ms
-1
. These sensors were samples at 10 seconds
intervals and the data averaged over 20 minute periods. For this study, all the data were averaged
over one hour periods.
Additionally, ground-based surface reflectances were measured from a height of 2 m above
the ground surface, using yoke-based radiometers (Exotech with spectral filters covering bleu,
green, red and NIR) and a calibrated reflectance panel (See Moran et al., 1994a,b). These data
were taken over a ground target of approximately 120 m by 120 m in size, during several day of
the experiment. For this study red and NIR reflectances were used to compute short wave albedo,
and the vegetation index .
RESULTS
Data described above has been used to estimate surface energy balance components during 13
days (DOY 209 to DOY 221). Comparison between observed and remotely sensed net radiation
is presented in Figure 1. In general, net radiation estimation compared well with the observations
( RMSE of about 50 Wm
-2 ). However, there is a slight discrepancy which may be due to the
limitation of the expression used to estimate incoming long wave radiation under cloudy sky
conditions (Brutsaert, 1975).
Figure 2, is a comparison of the data-derived and the model- derived soil heat flux. The
RMSE was about 40 Wm
-2 for values ranged between 0 and 250 Wm
-2
. It must be emphasized
however that the expression between Rn and G used here is developed for clear sky conditions
only. Furthermore, this expression does not take into account the time lag between G and Rn
(Moran et al., 1994). additionally, it may be possible that the relationship between net radiation
and soil heat flux does not depend only surface type (bare versus vegetated surface) but also
depends on the distribution of the vegetation within the surface (Chehbouni et al., 1996c). These
reasons may explain the scatter between measured and remotely sensed soil heat flux.
In Figure 3, eddy correlation based sensible heat flux is compared to that estimated using
remotely sensed surface temperature and vegetation index. The model tends to underestimate H
when measured values ranged from 50 to 150 Wm
-2
. This may be due to the error associated with
the formulation of β coefficient or to that associated with the estimation of LAI from MSAVI
which does not take into account the effect of solar angle variation. However, the root mean
square error (RMSE) between observed and simulated sensible heat was about 44 Wm
-2 for
measured values ranged between 0 to 300 Wm
-2
. Additional studies are needed to test the
universality of β parameterization, and to investigate how the L parameter changes with
vegetation type and structure.
Figure 4 presents a comparison between measured latent heat flux and that obtained as the
residual term of the energy balance equation. The model estimates of latent heat flux are
reasonable, the RMSE is about 54 Wm
-2
.
325
and the canopy. However other formulations for deriving soil under the shrubs temperature were
also tested. Wind speed was measured at 1.25 and 2.25 m above the soil surface using photochopper anemometers with a threshold of 0.2 ms
-1
. These sensors were samples at 10 seconds
intervals and the data averaged over 20 minute periods. For this study, all the data were averaged
over one hour periods.
Additionally, ground-based surface reflectances were measured from a height of 2 m above
the ground surface, using yoke-based radiometers (Exotech with spectral filters covering bleu,
green, red and NIR) and a calibrated reflectance panel (See Moran et al., 1994a,b). These data
were taken over a ground target of approximately 120 m by 120 m in size, during several day of
the experiment. For this study red and NIR reflectances were used to compute short wave albedo,
and the vegetation index .
RESULTS
Data described above has been used to estimate surface energy balance components during 13
days (DOY 209 to DOY 221). Comparison between observed and remotely sensed net radiation
is presented in Figure 1. In general, net radiation estimation compared well with the observations
( RMSE of about 50 Wm
-2 ). However, there is a slight discrepancy which may be due to the
limitation of the expression used to estimate incoming long wave radiation under cloudy sky
conditions (Brutsaert, 1975).
Figure 2, is a comparison of the data-derived and the model- derived soil heat flux. The
RMSE was about 40 Wm
-2 for values ranged between 0 and 250 Wm
-2
. It must be emphasized
however that the expression between Rn and G used here is developed for clear sky conditions
only. Furthermore, this expression does not take into account the time lag between G and Rn
(Moran et al., 1994). additionally, it may be possible that the relationship between net radiation
and soil heat flux does not depend only surface type (bare versus vegetated surface) but also
depends on the distribution of the vegetation within the surface (Chehbouni et al., 1996c). These
reasons may explain the scatter between measured and remotely sensed soil heat flux.
In Figure 3, eddy correlation based sensible heat flux is compared to that estimated using
remotely sensed surface temperature and vegetation index. The model tends to underestimate H
when measured values ranged from 50 to 150 Wm
-2
. This may be due to the error associated with
the formulation of β coefficient or to that associated with the estimation of LAI from MSAVI
which does not take into account the effect of solar angle variation. However, the root mean
square error (RMSE) between observed and simulated sensible heat was about 44 Wm
-2 for
measured values ranged between 0 to 300 Wm
-2
. Additional studies are needed to test the
universality of β parameterization, and to investigate how the L parameter changes with
vegetation type and structure.
Figure 4 presents a comparison between measured latent heat flux and that obtained as the
residual term of the energy balance equation. The model estimates of latent heat flux are
reasonable, the RMSE is about 54 Wm
-2
.