510
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
21.2.2 dRought indiceS
On the basis of the defined drought criteria, the intensity and duration of drought,
a number of different indices have been developed to detect, monitor, and evaluate drought, each with its own strengths and weaknesses. Drought indices integrate
various hydrological and meteorological parameters such as rainfall, temperature,
evapotranspiration, runoff, and other water supply indicators into a single number
and gives a comprehensive picture for decision making (Narasimhan and Srinivasan
2005). Drought conditions are monitored constantly using these indices to provide
current drought-impact information in the domains of meteorology, hydrology, agriculture, and water resources management.
Among various drought indices, PDSI (Palmer 1965) has been most commonly
used for drought monitoring and forecasting, which was developed by Palmer to
measure the departure of the moisture supply. Palmer based his index on the supplyand-demand concept of the water balance equation, incorporating antecedent precipitation, moisture supply, and moisture demand. PDSI has gained the widest
acceptance, because the index relies on a simple lumped parameter water balance
model and is easy to apply.
Despite the widespread acceptance of PDSI, various limitations have been
observed by different studies (Akinremi and McGinn 1996; Alley 1984; Guttman
1998; Narasimhan 2004), such as the large spatial resolution, poor performing of
potential evapotranspiration calculation, and incomplete knowledge of water balance
model physics. The large spatial resolution of model parameters, which is often common to most of the drought indices generated from hydrological and meteorological
data, hampers their effective applicability for drought monitoring, since the recent
developments of drought monitoring prefer specific indices that consider the spatial
variability and can describe best the local and regional drought conditions.
21.2.3 Satellite ReMote SenSing foR MonitoRing dRought
Satellite remote sensing has offered a means for more effective drought monitoring
across a much wider spatial and temporal scale and, at the same time, at a much
higher spatial and temporal resolution. Since the 1970s, a lot of studies have used
satellite land observation data to monitor a variety of dynamic land surface processes
(Anderson et al. 1976; Reed et al. 1994; Yang et al. 1998; Peters et al. 2002). Numerous
satellite-derived indices were developed to describe the land surface, mainly of vegetation, with the potential for detecting and monitoring anomalies such as drought
(Niemeyer 2008). In early 1990, Gutman (1990) presented an overview on the first
generation of remote sensing–based drought monitoring, while Kogan (1997) provided an update in 1997. Most recent reviews were provided by Bayarjargal et al.
(2006) and Niemeyer (2008).
The normalized difference vegetation index (NDVI) (Rouse et al. 1974; Tucker
1979), which certainly is the most common vegetation index, has been extensively
used in ecosystem and drought monitoring (Tucker and Choudhury 1987; Kogan
1991; Kogan 1995; Yang et al. 1998; McVicar and Bierwirth 2001; Ji and Peters
2003; Wan et al. 2004). By using the normalized reflectance difference between
Multiscale Hydrologic Remote Sensing: Perspectives and Applications
21.2.2 dRought indiceS
On the basis of the defined drought criteria, the intensity and duration of drought,
a number of different indices have been developed to detect, monitor, and evaluate drought, each with its own strengths and weaknesses. Drought indices integrate
various hydrological and meteorological parameters such as rainfall, temperature,
evapotranspiration, runoff, and other water supply indicators into a single number
and gives a comprehensive picture for decision making (Narasimhan and Srinivasan
2005). Drought conditions are monitored constantly using these indices to provide
current drought-impact information in the domains of meteorology, hydrology, agriculture, and water resources management.
Among various drought indices, PDSI (Palmer 1965) has been most commonly
used for drought monitoring and forecasting, which was developed by Palmer to
measure the departure of the moisture supply. Palmer based his index on the supplyand-demand concept of the water balance equation, incorporating antecedent precipitation, moisture supply, and moisture demand. PDSI has gained the widest
acceptance, because the index relies on a simple lumped parameter water balance
model and is easy to apply.
Despite the widespread acceptance of PDSI, various limitations have been
observed by different studies (Akinremi and McGinn 1996; Alley 1984; Guttman
1998; Narasimhan 2004), such as the large spatial resolution, poor performing of
potential evapotranspiration calculation, and incomplete knowledge of water balance
model physics. The large spatial resolution of model parameters, which is often common to most of the drought indices generated from hydrological and meteorological
data, hampers their effective applicability for drought monitoring, since the recent
developments of drought monitoring prefer specific indices that consider the spatial
variability and can describe best the local and regional drought conditions.
21.2.3 Satellite ReMote SenSing foR MonitoRing dRought
Satellite remote sensing has offered a means for more effective drought monitoring
across a much wider spatial and temporal scale and, at the same time, at a much
higher spatial and temporal resolution. Since the 1970s, a lot of studies have used
satellite land observation data to monitor a variety of dynamic land surface processes
(Anderson et al. 1976; Reed et al. 1994; Yang et al. 1998; Peters et al. 2002). Numerous
satellite-derived indices were developed to describe the land surface, mainly of vegetation, with the potential for detecting and monitoring anomalies such as drought
(Niemeyer 2008). In early 1990, Gutman (1990) presented an overview on the first
generation of remote sensing–based drought monitoring, while Kogan (1997) provided an update in 1997. Most recent reviews were provided by Bayarjargal et al.
(2006) and Niemeyer (2008).
The normalized difference vegetation index (NDVI) (Rouse et al. 1974; Tucker
1979), which certainly is the most common vegetation index, has been extensively
used in ecosystem and drought monitoring (Tucker and Choudhury 1987; Kogan
1991; Kogan 1995; Yang et al. 1998; McVicar and Bierwirth 2001; Ji and Peters
2003; Wan et al. 2004). By using the normalized reflectance difference between