Another study conducted as part of SMACEX by Su et al. (2005) used the Surface
Energy Balance System (SEBS) to estimate land surface fluxes using remote sensing
and meteorological data. SEBS consists of several separate modules to estimate the
net radiation and soil heat flux and to partition the available energy into sensible and
latent heat fluxes. Results from using SEBS show that the model can predict ET with
accuracies approaching 10–15% of that of in situ measurements. To extend the fieldbased measurement of SEBS, information derived from Landsat ETM+ data and data
from the North American Land Data Assimilation System (NLDAS)
6 were combined
to determine regional surface energy fluxes for a clear day during the field experiment.
Results from this analysis indicate that prediction accuracy was strongly related to
crop type, with corn prediction showing improved estimates compared to those of
soybean. This research found that differences between the mean values of observations and the SEBS Landsat-based predictions at in situ data collection sites were
approximately 5%. Overall, results from their analysis indicate much potential toward
routine prediction of surface heat fluxes using remote sensing data in conjunction with
meteorological data.
In water-deficient areas, water resource management requires ET at high spatial
and temporal resolutions. The use of remote spaceborne sensing data to do so,
however, requires the assessment of trade-offs between spatial and temporal resolutions. The sharpening of remotely sensed data is one potential way to obviate the
limitations posed by data from satellite platforms to derive surface temperature and
NDVI at the spatiotemporal scales needed for water resource management applications. Yang et al. (2010) used the triangle algorithm to sharpen Landsat ETM+ data.
Sharpened surface temperatures and reference temperatures were compared at 60 and
240 m spatial resolutions. The reflectance measurements are used to calculate the
NDVI. The NDVI is then plotted as a function of surface temperature radiation (T r ) to
evaluate the relationship between these two variables as well as providing and
overlaying the index of moisture availability to establish a “warm edge” and a
“cold edge” index (Figure 3.1). [A good overview of the triangle method is presented
in Carlson (2007).]
It was found that RMSEs with the triangle algorithm are smaller than those with a
functional relationship between surface temperature and NDVI. The triangle method
combines measurements of T r and reflectance in portions of the electromagnetic
spectrum.
In another study focused on a water-deficient area, Landsat ETM+ data were used
as input to a remote sensing–based ET algorithm called METRIC (Mapping Evapotranspiration at High Resolution Using Internalized Calibration) to provide accurate
ET maps on actual crop water use over the Texas High Plains (THP) (Gowda et al.,
2008). The performance of the ET model was evaluated by comparing the predicted
daily ET with values derived from soil moisture budget at four commercial agricultural fields. Daily ET estimates resulted in a prediction error (RMSE) of 12.7 ± 8.1%
when compared with ET derived from measured soil moisture through the soil water
balance. Considering prevailing advection conditions in the THP, these results are
6 Information on the NLDAS can be found at http://ldas.gsfc.nasa.gov/.
EVAPORATION/EVAPOTRANSPIRATION/SOIL MOISTURE
41
Energy Balance System (SEBS) to estimate land surface fluxes using remote sensing
and meteorological data. SEBS consists of several separate modules to estimate the
net radiation and soil heat flux and to partition the available energy into sensible and
latent heat fluxes. Results from using SEBS show that the model can predict ET with
accuracies approaching 10–15% of that of in situ measurements. To extend the fieldbased measurement of SEBS, information derived from Landsat ETM+ data and data
from the North American Land Data Assimilation System (NLDAS)
6 were combined
to determine regional surface energy fluxes for a clear day during the field experiment.
Results from this analysis indicate that prediction accuracy was strongly related to
crop type, with corn prediction showing improved estimates compared to those of
soybean. This research found that differences between the mean values of observations and the SEBS Landsat-based predictions at in situ data collection sites were
approximately 5%. Overall, results from their analysis indicate much potential toward
routine prediction of surface heat fluxes using remote sensing data in conjunction with
meteorological data.
In water-deficient areas, water resource management requires ET at high spatial
and temporal resolutions. The use of remote spaceborne sensing data to do so,
however, requires the assessment of trade-offs between spatial and temporal resolutions. The sharpening of remotely sensed data is one potential way to obviate the
limitations posed by data from satellite platforms to derive surface temperature and
NDVI at the spatiotemporal scales needed for water resource management applications. Yang et al. (2010) used the triangle algorithm to sharpen Landsat ETM+ data.
Sharpened surface temperatures and reference temperatures were compared at 60 and
240 m spatial resolutions. The reflectance measurements are used to calculate the
NDVI. The NDVI is then plotted as a function of surface temperature radiation (T r ) to
evaluate the relationship between these two variables as well as providing and
overlaying the index of moisture availability to establish a “warm edge” and a
“cold edge” index (Figure 3.1). [A good overview of the triangle method is presented
in Carlson (2007).]
It was found that RMSEs with the triangle algorithm are smaller than those with a
functional relationship between surface temperature and NDVI. The triangle method
combines measurements of T r and reflectance in portions of the electromagnetic
spectrum.
In another study focused on a water-deficient area, Landsat ETM+ data were used
as input to a remote sensing–based ET algorithm called METRIC (Mapping Evapotranspiration at High Resolution Using Internalized Calibration) to provide accurate
ET maps on actual crop water use over the Texas High Plains (THP) (Gowda et al.,
2008). The performance of the ET model was evaluated by comparing the predicted
daily ET with values derived from soil moisture budget at four commercial agricultural fields. Daily ET estimates resulted in a prediction error (RMSE) of 12.7 ± 8.1%
when compared with ET derived from measured soil moisture through the soil water
balance. Considering prevailing advection conditions in the THP, these results are
6 Information on the NLDAS can be found at http://ldas.gsfc.nasa.gov/.
EVAPORATION/EVAPOTRANSPIRATION/SOIL MOISTURE
41