Environmental Engineering Reference
In-Depth Information
20.1
Introduction
The long time series of Normalized Difference Vegetation Index (NDVI) acquired
from the Advanced Very High Resolution Radiometer (AVHRR), the Moderate
Resolution Imaging Spectroradiometer (MODIS), and the System Pour l'Observation
de la Terre (SPOT) Vegetation sensors enabled the development of environmental
data records for the study of climatic change (NRC 2004 ; Tucker et al. 2005 ). Liquid
water has absorption features at near-infrared and shortwave-infrared wavelengths
(Curcio and Petty 1951 ; Palmer and Williams 1974 ) which are readily identified in
leaf spectral reflectance (Gates et al. 1965 ;Tucker 1980 ). MODIS, SPOT Vegetation,
and AVHRR/3 data cover a relatively short time period for climate data records; with
the recently launched Visible Infrared Imaging Radiometer Suite (VIIRS), global
measurements will continue into the foreseeable future. Standardized data products
of canopy water content could be the start of a satellite environmental data record that
would provide important information for assessing global climatic change.
Landsat Thematic Mapper, MODIS, AVHRR/3, SPOT Vegetation, and other
sensors have bands at about 1650-nm wavelength, which is at a local absorption
minimum for liquid water (Fig. 20.1 ). Because the water absorption coefficient at
this wavelength is relatively small, differences in the amount of leaf water are
detectable by changes in leaf reflectance (Olsen 1967 ; Tucker 1980 ; Hunt and Rock
1989 ). Imaging spectrometers have many more bands and potentially have better
algorithms for the retrieval of water content (Ustin et al. 1998 , 2004 , 2012 ).
One of the large uncertainties about future climatic change is about changes in
precipitation frequency and amount (Christensen et al. 2007 ;AllanandSoden 2008 ).
Drought will cause changes in leaf water content for many vegetation types, so
remotely sensed data products are important for assessing the impact and helping
to mitigate the effects of climatic change on vegetation. Furthermore, climate change
is expected to increase the potential for wildfire (Liu et al. 2010 ); thus, a water content
data product may help manage efforts for disaster prevention and recovery. The
objective of this chapter is to review remote sensing for the retrieval of water content
in vegetation canopies for remotely sensed data products and to show how these data
products could be used for various applications.
20.2 Quantifying the Amount of Water in Vegetation
Different quantities and methods are used to measure the amount of water in foliage
(Table 20.1 ). The basic measurements are leaf fresh and dry weights; the difference
is the amount of water in a leaf. However, these basic data have little value because
of variation in leaf size, dry weight, and morphology. Plant physiological responses
to drought and water stress usually depend on relative water content (RWC) and
water potential (
), which are defined in thermodynamics by reference to the
maximum amount of water a plant cell may hold (Nobel 2009 ). Cells with the
maximum amount of water (i.e., RWC
Ψ
¼
100%) also have the maximum turgor
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