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https://doi.org/10.1...arrow_drop_down
https://doi.org/10.1007/978-94...
Part of book or chapter of book . 2003 . Peer-reviewed
Data sources: Crossref
https://doi.org/10.1007/978-1-...
Part of book or chapter of book . 2009 . Peer-reviewed
Data sources: Crossref
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Remote Sensing Phenology

Authors: Michael T. White; Bradley C. Reed; Jesslyn F. Brown;

Remote Sensing Phenology

Abstract

A number of approaches using a variety of satellite remote sensing products have been used to derive metrics related to the timing of biological events (or land surface phenology, LSP). The advantages of utilizing remote sensing for phenology applications are the ability to capture the continuous expression of phenology patterns across the landscape and the ability to retrospectively observe phenology from archived satellite data sets (e.g. Landsat and Advanced Very High Resolution Radiometer). However, LSP databases have not yet been satisfactorily validated due to the difficulty in obtaining sufficiently extensive ground observations throughout the growing season. A multi-level validation approach that uses ground observations, dedicated web cameras, and high, medium, and coarse spatial resolution satellite data is needed to give scientists an improved level of confidence in utilizing the data. Many of these shortcomings are being addressed by phenology networks across the globe such as the U.S. National Phenology Network. Even without extensive validation, a number of applications areas have employed LSP data successfully, including studies on ecosystems analysis, disasters, land use, and climate change. Land surface phenology promises to continue contributing to these types of applications, and will also likely serve as an important early indicator of environmental effects of climate change

  • BIP!
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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    181
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 1%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
Powered by OpenAIRE graph
Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
181
Top 1%
Top 1%
Top 10%
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