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drought by losing leaves and going dormant, which is detectable usingNDVI and other
red/near-infrared vegetation indices (see Sect. 20.3 ). However, plants that tolerate
drought will continue to lose water at a slow rate through the leaf cuticle. Therefore,
can repeated estimates of LWC or CWC be used to detect plant water stress?
Lenz et al. ( 2006 ) determined the pressure-volume curves for different species of
trees from different ecosystems in Australia. When the turgor potential was 0 MPa,
the mean RWC was 85.7 and 76.9% for species from high- and low-rainfall areas,
respectively (Lenz et al. 2006 ). Using the median LWC value from F´ret et al.
( 2011 ) of 0.11 kg m 2 , the LWC difference required to detect water stress is about
0.016-0.025 kg m 2 for repeated measurements on a single leaf. Because of
multiplicative effect of LAI, the difference in CWC between stressed and non-
stressed plants is larger, which leads to a somewhat ironic result that it may be
easier to detect water stress from satellites than it would be in a laboratory.
There have been several studies examining the effect of water stress on leaf and
canopy reflectances, but the data reported were either LWC, FMC, or LWC/ W f
(Table 20.1 ), all of which cannot be related unambiguously to the amount of plant
water stress. Early studies suggested that water stress could not be detected using
reflectances at about 1650-nm wavelength (Jackson et al. 1986 ; Bowman 1989 ;
Hunt and Rock 1989 ; Pierce et al. 1990 ; Riggs and Running 1991 , but see Collier
1989 ). Later studies used more spectral channels and found significant correlations
between
(or measures of soil water content) and vegetation water indices
(Serrano et al. 2000 ; Fensholt and Sandholt 2003 ; Stimson et al. 2005 ; Elsayed
et al. 2011 ). Rodr ´ guez-P ´ rez et al. ( 2007 ) found that vegetation water indices could
not be used to detect stress in grape vineyards, but spectroscopic methods were
significant.
During the night, plants draw water from the soil and reach the highest
Ψ
Ψ
just
before dawn. During the day, water loss from the foliage lowers
. In 2010, Cheng
et al. ( 2011a ) used MODIS/ASTER Airborne Simulator (MASTER) morning and
afternoon flights over almond and pistachio orchards in the southern San Joaquin
Valley, California. Changes in NDII were related to the small changes in CWC
(mean difference ¼ 0.021 kg m 2 ) between the morning and afternoon MASTER
overflights, and the RMSE of an NDII-LWC regression was 0.035 kg m 2 . Several
areas in the orchards had LWC differences greater than 0.04 kg m 2 (Cheng et al.
2011b ). Therefore, it is possible that water stress may be detected with remote
sensing if the LAI of the canopy is sufficiently large. From the numerous studies
above, however, detection of water stress using foliar water indices may be
problematic for routine applications.
Ψ
20.8 Estimating Fuel Moisture Content
Another major application for monitoring CWC is to estimate FMC, because it is
directly correlated with risk of wildfire. Field crews regularly sample FMC in order
to estimate fire danger ratings, which are assumed to be representative over large
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