Agricultural Drought Indices - US Department of Agriculture

Figure 2 represents the regression lines LSTNDVI max and LSTNDVI min , which define the “dry edge”
and “wet edge” of the triangle, respectively. The dry edge represents the rate of minimum
evapotranspiration (dry areas), while the wet edge gives the maximum value (areas without water
restrictions).
Then, the Vegetation Temperature Condition Index proposed by Wang et al. (2001), and widely
applied by Wan et al. (2004), for monitoring droughts, is defined as
VTCI
LST
NDVIi.max
NDVIi
= (4)
LST
NDVIi.max
+ LST
− LST
NDVIi.min
Finally, the Water Deficit Index (WDI), proposed by Moran et al. (1994), is considered. The WDI is
derived from the interpretation of the trapezoid based on the relationship between the difference of
surface temperature and air temperature and from the vegetation index. In the present work, the
WDI approach proposed by Verstraeten (2001; in Ranjan 2006) is applied, since it only requires Ta
(air temperature) as input. This method can be applied at a regional scale, in near to real time. It
is useful for surfaces completely covered by vegetation, and can also be applied to mixed surfaces
(covered by vegetation and with bare land). WDI quantifies the relative rate of latent heat flux (or
evapotranspiration), presenting a value of 0 for surfaces completely wet (evapotranspiration only
limited by atmospheric demand) and 1 for dry surfaces where there is no latent heat flux.
Following Verstraeten (2001; in Ranjan 2006),
WDI
∆LST
NDVIi.min
NDVIi
= (5)
∆LST
NDVIi.min
− ∆LST
− ∆LST
NDVIi.max
where the equations of the edges of the trapezoid (or space) are defined as the regression lines
that identify the minimum and maximum actual evapotranspiration rates:
Min ET real (dry edge):
Max ET real (wet edge):
∆LST NDVIi max
= a + bNDVIi
∆LST NDVIi
= a′
+ b′
NDVIi
. (6)
. min
(7)
Processing of Remote Sensing and Meteorological Data
The methodology for the proposed monitoring of drought conditions is based on surface
reflectance data and thermal properties derived from the TERRA MODIS (Moderate Resolution
Imaging Spectroradiometer) sensor, and from meteorological information (air temperature). The
processing was carried out by applying the computational system SORPRESA (García et al. 2007),
developed below GRASS GIS.
Compositions of 8 days of satellite images for surface temperature, and 16 days for NDVI, both
with 1-km spatial resolution (products MOD11A2 and MOD13A2, respectively), were used. In the
generation of LST vs. NDVI and (LST-Ta) vs. NDVI spaces, on the basis of 16-day compositions,
maximum values of LST were considered. Several authors demonstrated that maximum values of
LST represent the surface conditions in a realistic form (Cihlar et al. 1991). The temperature data
corresponded to the 42 stations of the Agriculture Information Service (IMIDA, Murcia Region).
Analysis of Results
The analysis of LST-NDVI spaces, generated for a sufficiently long and representative time period,
permits the detection of changes in soil moisture conditions for the whole analysis zone (Figure 3).
In general, the dry edge always describes a negative correlation with NDVI, while the wet edge can
present positive and negative correlations, which could be related to dry conditions.
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