Referring back to Fig. 4.4, the increasing number of publications on LSP is
likely to be connected to enhanced computational power and storage capacity,
improved LSP product quality, and easier access to satellite imagery. However, the
increase is likely also tied to the increasing relevance of LSP studies as the
community is looking to explore the connection between climate variability and
changes or trends in vegetation seasonality as well as the connection between
observed LSP and ecosystem functions.
The future likely will bring more satellite imagery and a longer time series of
data from which to construct ever-increasing LSP time series. This longer time
series will help LSP distinguish between temporal variability, actual trends, and
correlation with climate forcings. Perhaps the most pressing need for this line of
research will be for common LSP products derived from multiple sensors. The
works of Tucker et al. (2005) and Cao et al. (2008) have demonstrated the ability
to extend the time series through a multi-sensor approach (see also the Long Term
Data Record project at NASA
4 ). Future work with LSP will need to build on such
work as well as utilize the validation techniques described above to ensure that
LSP derived from a multi-sensor time series are free from artifacts from the
different data streams. Also, with the entire archive of Landsat data now available
and efforts to mosaic and composite those data (Roy et al. 2010), there is the
opportunity to consider kilo-, hecto-, and deca-resolution imagery (Morisette
2010) LSP products (Kovalskyy et al. 2012) for regional, continental, and even
global LSP studies.
In addition to longer time series, the validation techniques described above will
be able to take advantage of an increasing amount of coordinated phenologyrelated ground-based observations and modeling techniques that can integrate
these observations with satellite data. In May 2012, the archive of observations
within the USA National Phenology Network recorded its one millionth observation.
5 These offer an unprecedented archive of information with which to
compare LSP products. Furthermore, there is now an expanding network of towerbased near-surface cameras to complement field-based phenology observations
and help scale from the individual plant to a wider area representing a CO 2 flux
tower footprint (Richardson et al. 2009a). The integration of plant phenology and
LSP products with CO 2 monitoring and modeling has led the community to call for
improved understanding of the environmental controls on vegetation phenology
and incorporation of this knowledge into better phenological models (Richardson
et al. 2012). Some work is being done toward this objective through data assimilation of empirical phenology models and remote sensing observations (Stockli
et al. 2008, 2011). More connection and integration between the carbon modeling
and LSP communities are likely to improve our understanding of the global carbon
cycle and its impact on climate and feedback to phenological processes (Morisette
et al. 2009).
4 http://ltdr.nascom.nasa.gov
5 http://www.usgs.gov/newsroom/article.asp?ID=3195#.UAQsGvXNkxE
118
J. M. Hanes et al.
likely to be connected to enhanced computational power and storage capacity,
improved LSP product quality, and easier access to satellite imagery. However, the
increase is likely also tied to the increasing relevance of LSP studies as the
community is looking to explore the connection between climate variability and
changes or trends in vegetation seasonality as well as the connection between
observed LSP and ecosystem functions.
The future likely will bring more satellite imagery and a longer time series of
data from which to construct ever-increasing LSP time series. This longer time
series will help LSP distinguish between temporal variability, actual trends, and
correlation with climate forcings. Perhaps the most pressing need for this line of
research will be for common LSP products derived from multiple sensors. The
works of Tucker et al. (2005) and Cao et al. (2008) have demonstrated the ability
to extend the time series through a multi-sensor approach (see also the Long Term
Data Record project at NASA
4 ). Future work with LSP will need to build on such
work as well as utilize the validation techniques described above to ensure that
LSP derived from a multi-sensor time series are free from artifacts from the
different data streams. Also, with the entire archive of Landsat data now available
and efforts to mosaic and composite those data (Roy et al. 2010), there is the
opportunity to consider kilo-, hecto-, and deca-resolution imagery (Morisette
2010) LSP products (Kovalskyy et al. 2012) for regional, continental, and even
global LSP studies.
In addition to longer time series, the validation techniques described above will
be able to take advantage of an increasing amount of coordinated phenologyrelated ground-based observations and modeling techniques that can integrate
these observations with satellite data. In May 2012, the archive of observations
within the USA National Phenology Network recorded its one millionth observation.
5 These offer an unprecedented archive of information with which to
compare LSP products. Furthermore, there is now an expanding network of towerbased near-surface cameras to complement field-based phenology observations
and help scale from the individual plant to a wider area representing a CO 2 flux
tower footprint (Richardson et al. 2009a). The integration of plant phenology and
LSP products with CO 2 monitoring and modeling has led the community to call for
improved understanding of the environmental controls on vegetation phenology
and incorporation of this knowledge into better phenological models (Richardson
et al. 2012). Some work is being done toward this objective through data assimilation of empirical phenology models and remote sensing observations (Stockli
et al. 2008, 2011). More connection and integration between the carbon modeling
and LSP communities are likely to improve our understanding of the global carbon
cycle and its impact on climate and feedback to phenological processes (Morisette
et al. 2009).
4 http://ltdr.nascom.nasa.gov
5 http://www.usgs.gov/newsroom/article.asp?ID=3195#.UAQsGvXNkxE
118
J. M. Hanes et al.