4.1 Introduction
Vegetation phenology is the study of the timing of recurring plant life cycle events
that are driven by environmental factors (Morisette et al. 2009). The occurrence of
particular life cycle events, such as the emergence, growth, and senescence of
leaves, is driven predominantly by weather and climate (Hanes et al. 2013) and has
feedback effects on ecosystem processes (Baldocchi et al. 2005; Richardson et al.
2009b; Schwartz and Hanes 2010) and climate variables (Schwartz 1996; Hayden
1998; Fitzjarrald et al. 2001; Hanes 2012). The integrated nature of vegetation
phenology has motivated many to use it as an indicator of climate change, as
evidenced by the Intergovernmental Panel on Climate Change’s contention that
phenology ‘‘…is perhaps the simplest process in which to track changes in the
ecology of species in response to climate change.’’ (Rosenzweig et al. 2007).
The timing of phenological events has traditionally been documented using
in situ, visual observations of selected plants. While these observations have
proven useful, the lack of spatially and temporally-extensive in situ data inhibits
systematic assessments of vegetation phenology at large spatial scales (e.g., continental, global). Given this limitation of conventional phenology data, researchers
utilize satellite sensor-derived measurements of reflected electromagnetic radiation
from the land surface to study vegetation phenology over large geographic areas.
These measurements of land surface reflectance exhibit recurring changes that are
determined by vegetation phenology. The timing of these recurring changes in
reflectance is called land surface phenology (LSP).
During recent decades, a variety of methods has been used to derive metrics of
LSP from time series of satellite observations (White et al. 2009; Schwartz and
Hanes 2010). Although LSP metrics do facilitate large-scale assessments of seasonal vegetation dynamics, they are different than conventional phenology data. In
contrast to conventional phenology data, which typically include the timing of
specific phenophases for individual plants, metrics of LSP represent the timing of
reflectance changes that are driven by the aggregate activity of vegetation within
the areal unit measured by satellite sensors, such as the Advanced Very High
Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) (see Table 1 in Reed et al. 2009 for a list of satellite
sensors used commonly in LSP studies and the spatial resolution of their measurements). Therefore, these satellite-derived LSP metrics do not provide specific
information about the phenology of individual plants, species, or their phenophases (e.g., buds open, leaf emergence, leaf unfolding). Despite the generalized
nature of satellite sensor-derived measurements, they have proven useful for
studying changes in LSP associated with various phenomena, including climate
(Myneni et al. 1997; Hanes and Schwartz 2011), institutional changes (de Beurs
and Henebry 2004), and urban heat islands (White et al. 2002; Zhang et al. 2004;
Fisher et al. 2006).
This chapter provides a detailed overview of the use of satellite remote sensing
to monitor LSP. First, the theoretical basis for the application of satellite remote
100
J. M. Hanes et al.
Vegetation phenology is the study of the timing of recurring plant life cycle events
that are driven by environmental factors (Morisette et al. 2009). The occurrence of
particular life cycle events, such as the emergence, growth, and senescence of
leaves, is driven predominantly by weather and climate (Hanes et al. 2013) and has
feedback effects on ecosystem processes (Baldocchi et al. 2005; Richardson et al.
2009b; Schwartz and Hanes 2010) and climate variables (Schwartz 1996; Hayden
1998; Fitzjarrald et al. 2001; Hanes 2012). The integrated nature of vegetation
phenology has motivated many to use it as an indicator of climate change, as
evidenced by the Intergovernmental Panel on Climate Change’s contention that
phenology ‘‘…is perhaps the simplest process in which to track changes in the
ecology of species in response to climate change.’’ (Rosenzweig et al. 2007).
The timing of phenological events has traditionally been documented using
in situ, visual observations of selected plants. While these observations have
proven useful, the lack of spatially and temporally-extensive in situ data inhibits
systematic assessments of vegetation phenology at large spatial scales (e.g., continental, global). Given this limitation of conventional phenology data, researchers
utilize satellite sensor-derived measurements of reflected electromagnetic radiation
from the land surface to study vegetation phenology over large geographic areas.
These measurements of land surface reflectance exhibit recurring changes that are
determined by vegetation phenology. The timing of these recurring changes in
reflectance is called land surface phenology (LSP).
During recent decades, a variety of methods has been used to derive metrics of
LSP from time series of satellite observations (White et al. 2009; Schwartz and
Hanes 2010). Although LSP metrics do facilitate large-scale assessments of seasonal vegetation dynamics, they are different than conventional phenology data. In
contrast to conventional phenology data, which typically include the timing of
specific phenophases for individual plants, metrics of LSP represent the timing of
reflectance changes that are driven by the aggregate activity of vegetation within
the areal unit measured by satellite sensors, such as the Advanced Very High
Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) (see Table 1 in Reed et al. 2009 for a list of satellite
sensors used commonly in LSP studies and the spatial resolution of their measurements). Therefore, these satellite-derived LSP metrics do not provide specific
information about the phenology of individual plants, species, or their phenophases (e.g., buds open, leaf emergence, leaf unfolding). Despite the generalized
nature of satellite sensor-derived measurements, they have proven useful for
studying changes in LSP associated with various phenomena, including climate
(Myneni et al. 1997; Hanes and Schwartz 2011), institutional changes (de Beurs
and Henebry 2004), and urban heat islands (White et al. 2002; Zhang et al. 2004;
Fisher et al. 2006).
This chapter provides a detailed overview of the use of satellite remote sensing
to monitor LSP. First, the theoretical basis for the application of satellite remote
100
J. M. Hanes et al.