The validation of VIs with specific in situ biophysical quantities across a vegetation, phenologic growing season is made more complicated by the large number of
co-varying canopy properties that make it difficult to explicitly quantify one variable
from the others without constructing generalizations and assumptions about the
canopy. Phenologic canopy development often involve simultaneous changes in
LAI, specific leaf area, chlorophyll content, fractional cover, leaf angle, leaf phenology, litterfall, and canopy shadows (Hilker et al. 2008). Each of these variables
result in unique spectral variations and the retrieval of specific biophysical details
from the integrative ‘greenness’ signal would require the use of radiative transfer
(RT) models, productivity models, or for local site conditions, empirical models.
1.5 Findings
Vegetation indices have been remarkably successful in providing coherent data
sets with large spatial coverage for mapping and characterization of landscape
vegetation dynamics. Satellite VI products are seamlessly computed across all
pixels and at high temporal frequencies. A wide range of the earth science,
modeling, and applications user group community are using VI time-series data in
natural resource management, agriculture, public health, and hydrology and biogeochemical models. In this section we highlight some recent examples of
important findings involving the use of vegetation indices within the earth science
research and applications communities.
1.5.1 Phenology Studies
High temporal frequency vegetation index time series data from coarse resolution
sensors, including MODIS, AVHRR, SPOT-VGT, MERIS are now widely used to
trace and characterize land surface seasonal dynamics and phenology with quantifiable metrics, such as the onset date of greening, peak greenness date, browning,
and growing season length, all critical to understanding ecosystem functioning
(Fig. 1.12) (Zhang et al. 2006; Reed et al. 2003). Phenology is the study of
recurring biological events, such as the timing of leaf emergence and development,
senescence, and litterfall (Schwartz and Hanes 2010). It is an important integrative
science for quantifying vegetation responses and feedbacks to climate variability
(Penuelas et al. 2009).
Using AVHRR-NDVI time series data, Myneni et al. (1997) showed evidence
of a lengthening of the plant growing season at northern latitudes in response to
global temperature increases. Vegetation phenologies in high latitude environments are difficult to interpret due to the short growing seasons, long periods of
darkness, and persistent snow cover in winter. More recently, Beck et al. (2006)
were able to estimate biophysical parameters related to the timing of spring and
20
A. Huete et al.
co-varying canopy properties that make it difficult to explicitly quantify one variable
from the others without constructing generalizations and assumptions about the
canopy. Phenologic canopy development often involve simultaneous changes in
LAI, specific leaf area, chlorophyll content, fractional cover, leaf angle, leaf phenology, litterfall, and canopy shadows (Hilker et al. 2008). Each of these variables
result in unique spectral variations and the retrieval of specific biophysical details
from the integrative ‘greenness’ signal would require the use of radiative transfer
(RT) models, productivity models, or for local site conditions, empirical models.
1.5 Findings
Vegetation indices have been remarkably successful in providing coherent data
sets with large spatial coverage for mapping and characterization of landscape
vegetation dynamics. Satellite VI products are seamlessly computed across all
pixels and at high temporal frequencies. A wide range of the earth science,
modeling, and applications user group community are using VI time-series data in
natural resource management, agriculture, public health, and hydrology and biogeochemical models. In this section we highlight some recent examples of
important findings involving the use of vegetation indices within the earth science
research and applications communities.
1.5.1 Phenology Studies
High temporal frequency vegetation index time series data from coarse resolution
sensors, including MODIS, AVHRR, SPOT-VGT, MERIS are now widely used to
trace and characterize land surface seasonal dynamics and phenology with quantifiable metrics, such as the onset date of greening, peak greenness date, browning,
and growing season length, all critical to understanding ecosystem functioning
(Fig. 1.12) (Zhang et al. 2006; Reed et al. 2003). Phenology is the study of
recurring biological events, such as the timing of leaf emergence and development,
senescence, and litterfall (Schwartz and Hanes 2010). It is an important integrative
science for quantifying vegetation responses and feedbacks to climate variability
(Penuelas et al. 2009).
Using AVHRR-NDVI time series data, Myneni et al. (1997) showed evidence
of a lengthening of the plant growing season at northern latitudes in response to
global temperature increases. Vegetation phenologies in high latitude environments are difficult to interpret due to the short growing seasons, long periods of
darkness, and persistent snow cover in winter. More recently, Beck et al. (2006)
were able to estimate biophysical parameters related to the timing of spring and
20
A. Huete et al.