photosynthetic capacity (i.e., Gitelson et al., 2006), and estimate vegetation productivity (i.e., Peng and Gitelson, 2011).
Methods for predicting and estimating Chl a + b at the leaf scale are based on
light–foliar interactions that are recorded by remote sensing data and have been in
development for several decades. Spectral data in the visible and near-infrared regions
of the spectrum were originally used to develop a number of spectral indices [e.g., the
normalized difference vegetation index (NDVI)] (Rouse et al., 1974) for estimating
vegetation chlorophyll content. However, these indices have been found to be
insensitive to medium and high chlorophyll concentrations (Gitelson and Merzlyak,
1994). Recent studies have developed several new spectral indices based on other
visible wavelength bands instead of the red wavelengths around the region of
670–680 nm. For example, studies have found that the region near 700 nm is highly
sensitive to chlorophyll concentrations (e.g., Gitelson and Merzlyak, 1996; Gitelson
et al., 2005; Ciganda et al., 2009), and according to that finding, the narrow-bandbased indices simple ratio (SR [700,750] ) (Gitelson and Merzlyak, 1996) and
NDVI [705,750] (Datt, 1999) were found to be well correlated with total chlorophyll
content of different types of leaves. Further, Sims and Gamon (2002) examined
hundreds of leaves of nonrelated plant species and proved that reflectance in the
spectral channel around 700 nm was the most sensitive indicator of chlorophyll and
that indices SR [700,750] and NDVI [705,750] could be used as a measure of chlorophyll
content. In the tall grassland, a previous study further demonstrated that the index
SR [700,750] is better than NDVI [705,750] in estimating vegetation pigment
(Wong, 2012).
At the leaf scale, strong relationships have been reported between spectral
indices and chlorophyll measurements in many leaf types originating from a wide
range of ecosystems. However, the predictive capability of spectral indices in
estimating chlorophyll content remains uncertain at the canopy and landscape
scales. At the canopy scale, remote sensing estimation of pigments has been
performed through different empirical methods (Johnson et al., 1994; Curran
et al., 1997; Daughtry et al., 2000; Zarco-Tejada et al., 1999, 2000, 2002,
2004). One method is to correlate leaf-level pigment content directly to the
canopy-level reflectance spectral data measured in the field or by airborne or
satellite sensors. This method, however, may not produce reliable results as canopy
pigment composition depends strongly on plant species as well as on the canopy
structure (Zhang et al., 2008). The other commonly used method includes correlating the canopy-integrated pigment content to the canopy-level reflectance spectral
data (Yoder and Pettigrew-Crosby, 1995; Daughtry et al., 2000). The canopyintegrated pigment content is obtained by multiplying the leaf pigment content by
the corresponding canopy biophysical parameters such as leaf area index (LAI) or
biomass. For example, Jago et al. (1999) defined canopy chlorophyll content as
chlorophyll concentration ´ biomass within an area covered by a pixel. Similarly,
Ciganda et al. (2009) estimated total chlorophyll in maize canopies using LAI ´
leaf chlorophyll content. The canopy-integrated approach markedly improved
current techniques proposed for pigment quantification in the canopy. However,
the major assumption of the canopy-integrated method is that all leaves in the plant
INTRODUCTION
127
Methods for predicting and estimating Chl a + b at the leaf scale are based on
light–foliar interactions that are recorded by remote sensing data and have been in
development for several decades. Spectral data in the visible and near-infrared regions
of the spectrum were originally used to develop a number of spectral indices [e.g., the
normalized difference vegetation index (NDVI)] (Rouse et al., 1974) for estimating
vegetation chlorophyll content. However, these indices have been found to be
insensitive to medium and high chlorophyll concentrations (Gitelson and Merzlyak,
1994). Recent studies have developed several new spectral indices based on other
visible wavelength bands instead of the red wavelengths around the region of
670–680 nm. For example, studies have found that the region near 700 nm is highly
sensitive to chlorophyll concentrations (e.g., Gitelson and Merzlyak, 1996; Gitelson
et al., 2005; Ciganda et al., 2009), and according to that finding, the narrow-bandbased indices simple ratio (SR [700,750] ) (Gitelson and Merzlyak, 1996) and
NDVI [705,750] (Datt, 1999) were found to be well correlated with total chlorophyll
content of different types of leaves. Further, Sims and Gamon (2002) examined
hundreds of leaves of nonrelated plant species and proved that reflectance in the
spectral channel around 700 nm was the most sensitive indicator of chlorophyll and
that indices SR [700,750] and NDVI [705,750] could be used as a measure of chlorophyll
content. In the tall grassland, a previous study further demonstrated that the index
SR [700,750] is better than NDVI [705,750] in estimating vegetation pigment
(Wong, 2012).
At the leaf scale, strong relationships have been reported between spectral
indices and chlorophyll measurements in many leaf types originating from a wide
range of ecosystems. However, the predictive capability of spectral indices in
estimating chlorophyll content remains uncertain at the canopy and landscape
scales. At the canopy scale, remote sensing estimation of pigments has been
performed through different empirical methods (Johnson et al., 1994; Curran
et al., 1997; Daughtry et al., 2000; Zarco-Tejada et al., 1999, 2000, 2002,
2004). One method is to correlate leaf-level pigment content directly to the
canopy-level reflectance spectral data measured in the field or by airborne or
satellite sensors. This method, however, may not produce reliable results as canopy
pigment composition depends strongly on plant species as well as on the canopy
structure (Zhang et al., 2008). The other commonly used method includes correlating the canopy-integrated pigment content to the canopy-level reflectance spectral
data (Yoder and Pettigrew-Crosby, 1995; Daughtry et al., 2000). The canopyintegrated pigment content is obtained by multiplying the leaf pigment content by
the corresponding canopy biophysical parameters such as leaf area index (LAI) or
biomass. For example, Jago et al. (1999) defined canopy chlorophyll content as
chlorophyll concentration ´ biomass within an area covered by a pixel. Similarly,
Ciganda et al. (2009) estimated total chlorophyll in maize canopies using LAI ´
leaf chlorophyll content. The canopy-integrated approach markedly improved
current techniques proposed for pigment quantification in the canopy. However,
the major assumption of the canopy-integrated method is that all leaves in the plant
INTRODUCTION
127