temperatures and energy fluxes over a wide area repetitively for multiple temporal
periods (i.e., hours, days, weeks) for the same geographic area on Earth. This facilitates
the modeling of surface energy fluxes for different land covers across the heterogeneous
land surface to develop an understanding of how individual land covers with different
thermal characteristics force energy exchanges between the land and atmosphere. There
are numerous references that explain TIR theory and how it can be used to derive surface
thermal energy balances using remote sensing data (see, e.g., Quattrochi and Luvall,
2009; Quattrochi et al., 2009) and this will not be explained here.
3.4 ESTIMATING LAND SURFACE ENERGY BUDGETS
USING REMOTE SENSING DATA
Satellite remote sensing provides an excellent opportunity to study land–atmosphere
energy exchanges at the regional scale. Many algorithms have been developed and
tested using satellite TIR data to measure regional distributions of land surface
temperature (LST), land surface reflectance, particularly that from vegetation, using
the normalized vegetation index (NDVI), and fluxes of net radiation, soil heat, and
sensible and latent heat flux. The NDVI has been used extensively to measure canopy
density (or biomass content) to develop better regional estimates of energy fluxes for
vegetation at the regional scale (see Quattrochi and Luvall, 1999, 2004). The NDVI
has also been used to compare the energy fluxes of vegetation with other types of land
covers (e.g., nonnatural surfaces) and to assess how energy dynamics of vegetation,
especially evapotranspiration, affects surrounding land covers (NASA, 2013; Quattrochi et al., 2009).
Landsat TM and ETM data have been used extensively to derive land surface
temperatures in conjunction with NDVIs. Fan et al. (2007) used ETM+ data to derive
regional distribution of surface energy fluxes in conjunction with NDVI over a
watershed in Inner Mongolia China. Distribution maps revealed strong contrasts in
thermal energy responses of surface characteristics as a function of landscape features.
Southworth (2004) investigated the utility of integrating Landsat data for differentiation between successional stages of forest growth in the Yucatan, Mexico. He found
that the Landsat ETM+ thermal data contain considerable information for the
discrimination of land cover classes in the dry tropical forest ecosystem. Li et al.
(2004) used Landsat TM and ETM+ data to derive land surface temperatures as part
of the Soil Moisture Experiments in 2002 (SMEX02) in central Iowa. Results from the
study show that it is possible to extract accurate LSTs that vary from 0.98 to 1.47°C
from Landsat 5 TM and Landsat 7 ETM+ data, respectively. Yves et al. (2006) used
LST algorithms and NDVI values to estimate changes in vegetation in the European
continent between 1982 and 1999 from the Pathfinder AVHRR (NOAA AVHRR)
NDVI data set.
3 These data show a well-confirmed trend of increased NDVI values
over Europe, with southern Europe seeing a decrease over the whole continent except
3 The Pathfinder AVHRR NDVI data set is available from the NASA Goddard Earth Science Data and
Information Services Center (GES DISC) at http://disc.sci.gsfc.nasa.gov/about-us.
38
THERMAL INFRARED REMOTE SENSING FOR ANALYSIS OF LANDSCAPE
periods (i.e., hours, days, weeks) for the same geographic area on Earth. This facilitates
the modeling of surface energy fluxes for different land covers across the heterogeneous
land surface to develop an understanding of how individual land covers with different
thermal characteristics force energy exchanges between the land and atmosphere. There
are numerous references that explain TIR theory and how it can be used to derive surface
thermal energy balances using remote sensing data (see, e.g., Quattrochi and Luvall,
2009; Quattrochi et al., 2009) and this will not be explained here.
3.4 ESTIMATING LAND SURFACE ENERGY BUDGETS
USING REMOTE SENSING DATA
Satellite remote sensing provides an excellent opportunity to study land–atmosphere
energy exchanges at the regional scale. Many algorithms have been developed and
tested using satellite TIR data to measure regional distributions of land surface
temperature (LST), land surface reflectance, particularly that from vegetation, using
the normalized vegetation index (NDVI), and fluxes of net radiation, soil heat, and
sensible and latent heat flux. The NDVI has been used extensively to measure canopy
density (or biomass content) to develop better regional estimates of energy fluxes for
vegetation at the regional scale (see Quattrochi and Luvall, 1999, 2004). The NDVI
has also been used to compare the energy fluxes of vegetation with other types of land
covers (e.g., nonnatural surfaces) and to assess how energy dynamics of vegetation,
especially evapotranspiration, affects surrounding land covers (NASA, 2013; Quattrochi et al., 2009).
Landsat TM and ETM data have been used extensively to derive land surface
temperatures in conjunction with NDVIs. Fan et al. (2007) used ETM+ data to derive
regional distribution of surface energy fluxes in conjunction with NDVI over a
watershed in Inner Mongolia China. Distribution maps revealed strong contrasts in
thermal energy responses of surface characteristics as a function of landscape features.
Southworth (2004) investigated the utility of integrating Landsat data for differentiation between successional stages of forest growth in the Yucatan, Mexico. He found
that the Landsat ETM+ thermal data contain considerable information for the
discrimination of land cover classes in the dry tropical forest ecosystem. Li et al.
(2004) used Landsat TM and ETM+ data to derive land surface temperatures as part
of the Soil Moisture Experiments in 2002 (SMEX02) in central Iowa. Results from the
study show that it is possible to extract accurate LSTs that vary from 0.98 to 1.47°C
from Landsat 5 TM and Landsat 7 ETM+ data, respectively. Yves et al. (2006) used
LST algorithms and NDVI values to estimate changes in vegetation in the European
continent between 1982 and 1999 from the Pathfinder AVHRR (NOAA AVHRR)
NDVI data set.
3 These data show a well-confirmed trend of increased NDVI values
over Europe, with southern Europe seeing a decrease over the whole continent except
3 The Pathfinder AVHRR NDVI data set is available from the NASA Goddard Earth Science Data and
Information Services Center (GES DISC) at http://disc.sci.gsfc.nasa.gov/about-us.
38
THERMAL INFRARED REMOTE SENSING FOR ANALYSIS OF LANDSCAPE