drawbacks of different ET models. They also provide insight into the limitations and
promising aspects of the estimation of ET-based remotely sensed data and groundbased measurements.
3.6 DROUGHT MONITORING
In addition to using TIR data for ET and soil moisture analysis over vegetated
surfaces, there is also a need for using these data for assessment of drought conditions.
Anderson and Kustas (2008) illustrate that the ALEXI model can successfully be used
with TIR data to model ET and drought at local to continental scales. They
demonstrate this using GOES AVHRR data to produce ET soil moisture stress
estimates at 10 km grid resolution over the coterminous United States. They also
indicate that ALEXI run in a disaggregation mode (DisALEXI) can generate
moderate- to high-resolution (10
0
–10
3 -m) ET flux maps using data satellite platforms
such as Landsat and MODIS. This methodology is examined more extensively and
reported on by Anderson et al. (2011. Min and Minghu (2010) show that combining a
spectral vegetation index (NDVI) with TIR data (T s ) in a T s /NDVI triangle model can
provide a promising measure for drought monitoring. They use the T s /NDVI triangle
method using MODIS NDVI and LST data to explore dryness monitoring in
Heilongjiang Province, China. The spatial pattern observed using this method
demonstrates that the summer dryness is characterized with extensive and longduration droughty conditions. They find that the T s /NDVI method can provide nearreal-time drought monitoring in the study area. Another study conducted in China by
Wu et al. (2008) used MODIS TIR data within a GIS format to generate a soil
moisture map based on the relationship between thermal inertia and soil moisture.
Results indicate that thermal inertia derived from MODIS data is consistent with the
actual dryness characteristics that occurred as verified with meteorological data.
7
Karniell et al. (2010) provide an insightful analysis of the merits and limitations of
the use of NDVI and LST for drought assessment. Their work investigates the
generality of the LST–NDVI relationship over a wide range of moisture and climatic/
radiation regimes encountered over the North American continent (up to 60° N)
during the summer growing season. Information on LST and NDVI was obtained
from long-term (21-year) data sets acquired with the AVHRR sensor. It was found
that when water is the limiting factor for vegetation growth (which is the typical
situation for low latitudes of the study area during the midseason), the LST–NDVI
correlation is negative. However, when energy is the limiting factor for vegetation
growth (in higher latitudes and elevations, particularly at the beginning of the growing
season), a positive correlation exists between LST and NDVI. Multiple regression
analysis revealed that during the beginning and end of the growing season solar
7 Thermal inertia is the ability of a landscape to resist change in temperature. Because thermal inertia is
related to surface composition or to near-surface moisture, remote sensing can be used to measure this
property. We explain the utility of thermal inertia measurements in landscape analysis in our 1999
Landscape Ecology article on pages 583–584.
DROUGHT MONITORING
45
promising aspects of the estimation of ET-based remotely sensed data and groundbased measurements.
3.6 DROUGHT MONITORING
In addition to using TIR data for ET and soil moisture analysis over vegetated
surfaces, there is also a need for using these data for assessment of drought conditions.
Anderson and Kustas (2008) illustrate that the ALEXI model can successfully be used
with TIR data to model ET and drought at local to continental scales. They
demonstrate this using GOES AVHRR data to produce ET soil moisture stress
estimates at 10 km grid resolution over the coterminous United States. They also
indicate that ALEXI run in a disaggregation mode (DisALEXI) can generate
moderate- to high-resolution (10
0
–10
3 -m) ET flux maps using data satellite platforms
such as Landsat and MODIS. This methodology is examined more extensively and
reported on by Anderson et al. (2011. Min and Minghu (2010) show that combining a
spectral vegetation index (NDVI) with TIR data (T s ) in a T s /NDVI triangle model can
provide a promising measure for drought monitoring. They use the T s /NDVI triangle
method using MODIS NDVI and LST data to explore dryness monitoring in
Heilongjiang Province, China. The spatial pattern observed using this method
demonstrates that the summer dryness is characterized with extensive and longduration droughty conditions. They find that the T s /NDVI method can provide nearreal-time drought monitoring in the study area. Another study conducted in China by
Wu et al. (2008) used MODIS TIR data within a GIS format to generate a soil
moisture map based on the relationship between thermal inertia and soil moisture.
Results indicate that thermal inertia derived from MODIS data is consistent with the
actual dryness characteristics that occurred as verified with meteorological data.
7
Karniell et al. (2010) provide an insightful analysis of the merits and limitations of
the use of NDVI and LST for drought assessment. Their work investigates the
generality of the LST–NDVI relationship over a wide range of moisture and climatic/
radiation regimes encountered over the North American continent (up to 60° N)
during the summer growing season. Information on LST and NDVI was obtained
from long-term (21-year) data sets acquired with the AVHRR sensor. It was found
that when water is the limiting factor for vegetation growth (which is the typical
situation for low latitudes of the study area during the midseason), the LST–NDVI
correlation is negative. However, when energy is the limiting factor for vegetation
growth (in higher latitudes and elevations, particularly at the beginning of the growing
season), a positive correlation exists between LST and NDVI. Multiple regression
analysis revealed that during the beginning and end of the growing season solar
7 Thermal inertia is the ability of a landscape to resist change in temperature. Because thermal inertia is
related to surface composition or to near-surface moisture, remote sensing can be used to measure this
property. We explain the utility of thermal inertia measurements in landscape analysis in our 1999
Landscape Ecology article on pages 583–584.
DROUGHT MONITORING
45