Environmental Engineering Reference
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pointed out that the variation of greenness onset is not only dependent on latitude
but also associated with elevation and ecoregions. When the elevation is higher than
1 km, mountain area tends to green up latter than other ecoregions.
17.3.4 Applications of Other Sensors
The SPOT Vegetation optical instrument launched in March 1998 operates in four
spectral bands: blue, red, NIR, and shortwave infrared (SWIR). Delbarta et al. ( 2005 )
have provided an accurate and precise determination of the dates of greenness onset
based on the Normalized Difference Water Index (NDWI) which generated from
SPOT Vegetation NIR and SWIR bands. Their detection algorithm relies on the fact
that NDWI first decreases with snowmelt and then increases during the vegetation
greening. The Medium Resolution Image Spectrometer Instrument (MERIS) is one
of the sensors on Environmental Satellite (ENVISAT) which was launched by the
European Space Agency (ESA) in 2002. Although MERIS was primarily dedicated
to ocean color, its band configuration broadens its application to vegetation monitor-
ing(Denteetal. 2008 ). The MERIS collect global data every 3 days in 15 wavebands
at 300-m spatial resolution. The Terrestrial Chlorophyll Index (MTCI), one of the
MERIS products, has enabled researchers to provide temporally continuous pheno-
logical variables at a much finer spatial resolution more accurately and precisely
(Lankester et al. 2010 ). The SeaWiFS carried on SeaStar spacecraft was launched in
1997. The sensor records information in eight optical bands with 1.1-km spatial
resolution for Local Area Coverage (LAC) and 4.5-km resolution for Global Area
Coverage (GAC). Verstraete et al. ( 2008 ) have described a method to define the start,
end, and length of growing season based on the statistical analysis of time series of
the biogeophysical quantity known as the fraction of absorbed photosythetically
active radiation (FAPAR) derived from SeaWiFS data for various biomes.
17.4 Summary
Satellite measurements have gained the insights about phenological behavior both on
the methods and applications. The method for satellite-derived vegetation phenology
has been developed from empirically, simple threshold of vegetation index to
automated, elaborate logistic model. Some methods are carried on specific satellite
data or specific land cover types, while some methods are independent of data set and
can be utilized on different land cover types. Each method provides certain
advantages and helps lay the way open for the success of satellite-derived vegetation
phenology. A variety of satellite sensors have been used to detect vegetation phenol-
ogy varying from instruments designed for land application purpose such as Landsat,
AVHRR, MODIS, and SPOT Vegetation to instruments not for land application such
as MERIS and SeaWiFS. The application of satellite-derived vegetation phenology
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