instruments. They found that upscaled ASTER LSTs achieved an agreement of
−0.2 ± 1.87 K in comparison to the MODIS LSTs.
As part of Soil Moisture-Atmosphere Coupling Experiment (SMACEX) (Kustas
et al., 2005) conducted over Oklahoma, Kansas, and surrounding states, French et al.
(2005) used ASTER data to detect and discern variations in surface temperature,
emissivities, vegetation densities, and albedo for distinct land use types. They
combined ASTER observations with two physically based surface energy flux
models, the Two-Source Energy Balance (TSEB) and the Surface Energy Balance
Algorithm for Land (SEBAL) models, to retrieve estimates of instantaneous surface
energy fluxes. Intercomparison of results between all flux components indicated that
the two models operate similarly when provided identical ASTER data inputs. Further
assessment of a multiscale remote sensing model for disaggregating regional fluxes is
given by Anderson et al. (2004). Here TIR data from 6 remote sensing satellites
[including the NOAA Geostationary Operational Environmental Satellite (GOES)]
are used in conjunction with the Atmosphere-Land Exchange Inverse (ALEXI) model
and associated disaggregation technique (DisALEXI), in effecting regional to local
downscaling of these data. An excellent reference that provides an overview of
advances in thermal infrared-based land surface models is also provided by Kustas
and Anderson (2009) and Anderson et al. (2004).
3.5 EVAPORATION/EVAPOTRANSPIRATION/SOIL MOISTURE
A predominant application of TIR data has been in inferring evaporation, evapotranspiration (ET), and soil moisture. This is verified by the numerous references in
the literature relating to this application as we noted elsewhere (Quattrochi and Luvall,
1999, 2009). A good overview of remote sensing research in hydrometeorology and
evapotranspiration, with particular emphasis on the major contributions that have
been made by the U.S. Department of Agriculture’s, Agricultural Research Service
(ARS), is given by Kustas et al. (2003a). A review of surface temperature and
vegetation indices remote sensing–based methods for retrieval of land surface energy
fluxes and soil moisture is also proved by Petropoulos et al. (2009). An additional
overview of remote sensing of evapotranspiration is given in Kustas et al. (2003a).
Landsat ETM+, MODIS, and ASTER data have been successfully used to derive
parameters, such as surface temperature and emissivity, for input into soil moisture
and ET models (Jacob et al., 2004). Liu et al. (2007) used ETM+ and meteorological
data in a regional ET model for the Beijing, China, area. Comparisons of energy
balance components (net radiation, soil heat flux, sensible and latent heat flux) with
measured fluxes by the model were made, integrating the remotely sensed fluxes by
the model. Results show that latent heat flux estimates with errors of mean bias error
(MBE) ± root mean square error (RMSE) of −8.56 ± 23.79 W/m
2
, sensible heat flux
error of −8.56 ± 23.79 W/m
2 , net radiation error of 25.16 ± 50.87 W/m
2
, and soil
heat flux error of 10.68 ± 22.81 W/m
2 . The better agreement between the estimates
and the measurements indicates that the remote sensing model is appropriate for
estimating regional ET over heterogeneous surfaces.
40
THERMAL INFRARED REMOTE SENSING FOR ANALYSIS OF LANDSCAPE
−0.2 ± 1.87 K in comparison to the MODIS LSTs.
As part of Soil Moisture-Atmosphere Coupling Experiment (SMACEX) (Kustas
et al., 2005) conducted over Oklahoma, Kansas, and surrounding states, French et al.
(2005) used ASTER data to detect and discern variations in surface temperature,
emissivities, vegetation densities, and albedo for distinct land use types. They
combined ASTER observations with two physically based surface energy flux
models, the Two-Source Energy Balance (TSEB) and the Surface Energy Balance
Algorithm for Land (SEBAL) models, to retrieve estimates of instantaneous surface
energy fluxes. Intercomparison of results between all flux components indicated that
the two models operate similarly when provided identical ASTER data inputs. Further
assessment of a multiscale remote sensing model for disaggregating regional fluxes is
given by Anderson et al. (2004). Here TIR data from 6 remote sensing satellites
[including the NOAA Geostationary Operational Environmental Satellite (GOES)]
are used in conjunction with the Atmosphere-Land Exchange Inverse (ALEXI) model
and associated disaggregation technique (DisALEXI), in effecting regional to local
downscaling of these data. An excellent reference that provides an overview of
advances in thermal infrared-based land surface models is also provided by Kustas
and Anderson (2009) and Anderson et al. (2004).
3.5 EVAPORATION/EVAPOTRANSPIRATION/SOIL MOISTURE
A predominant application of TIR data has been in inferring evaporation, evapotranspiration (ET), and soil moisture. This is verified by the numerous references in
the literature relating to this application as we noted elsewhere (Quattrochi and Luvall,
1999, 2009). A good overview of remote sensing research in hydrometeorology and
evapotranspiration, with particular emphasis on the major contributions that have
been made by the U.S. Department of Agriculture’s, Agricultural Research Service
(ARS), is given by Kustas et al. (2003a). A review of surface temperature and
vegetation indices remote sensing–based methods for retrieval of land surface energy
fluxes and soil moisture is also proved by Petropoulos et al. (2009). An additional
overview of remote sensing of evapotranspiration is given in Kustas et al. (2003a).
Landsat ETM+, MODIS, and ASTER data have been successfully used to derive
parameters, such as surface temperature and emissivity, for input into soil moisture
and ET models (Jacob et al., 2004). Liu et al. (2007) used ETM+ and meteorological
data in a regional ET model for the Beijing, China, area. Comparisons of energy
balance components (net radiation, soil heat flux, sensible and latent heat flux) with
measured fluxes by the model were made, integrating the remotely sensed fluxes by
the model. Results show that latent heat flux estimates with errors of mean bias error
(MBE) ± root mean square error (RMSE) of −8.56 ± 23.79 W/m
2
, sensible heat flux
error of −8.56 ± 23.79 W/m
2 , net radiation error of 25.16 ± 50.87 W/m
2
, and soil
heat flux error of 10.68 ± 22.81 W/m
2 . The better agreement between the estimates
and the measurements indicates that the remote sensing model is appropriate for
estimating regional ET over heterogeneous surfaces.
40
THERMAL INFRARED REMOTE SENSING FOR ANALYSIS OF LANDSCAPE