170 
and Thomas, 1976; Jackson, 1983). The 
reason Eq. [3] is location specific is 
that efficiencies of conversion and 
harvest indices are climate-dependent, 
a fact not much acknowledged or 
discussed yet in the crop science 
literature. 
The VI used can be calculated either 
from reflectance factors (units of %) 
or from digital counts (DC) which are 
proportional to radiance 
(W/m2/ym/sr). Because DC are 
proportional to radiance, they will 
change seasonally with sun zenith 
angle, even if leaf area index or 
amount of photosynthetically active 
tissue is constant. Thus we speculate 
that cumulations of VI based on DC will 
take variations in incident PAR into 
account, making comparisons of IVI 
versus yield for a given sensor more 
comparable among geographically 
separated sites than if based on 
reflectance factors. However, the 
calibrations will differ among 
sensors. On the other hand, 
calibrations from observations 
converted to exoatmospheric 
reflectances should agree among 
observation systems, ignoring 
date-specific atmospheric effects on 
the observations. For sites in 
different parts of the world that are 
climate analogs of each other, the 
yield efficiencies in terms of VI as 
well as the slopes of the right side 
terms of Eqs. [3] and [5] should be 
closely similar. The inverse should 
also be true; for carefully taken data, 
the difference in the slopes should be 
a measure of the climatic effect if 
yield potentials are genetically 
alike. The hypotheses stated are 
difficult to test because of 
measurement errors in available data 
sets, or incomplete data sets. 
Because rainfed agricultural areas 
dominate the production of crops 
important in world trade and because VI 
take time to respond to current 
conditions, it would be highly 
desirable to augment the VI with canopy 
temperature observations which do 
respond to current plant-available 
water conditions. The thematic mapper 
has a thermal band (10.4 to 12.5ym) but 
the observations have to be corrected 
for amount of water vapor in the 
atmosphere and have not been used very 
much in conjunction with the NIR and 
Red bands; the ground resolution is 
also larger than for the shorter 
bands. Wiegand et al. (1983) have 
reviewed drought detection and 
quantification by reflectance and 
thermal observations, but at that time 
Eq. [5] and SCA did not exist. 
Maas et al. (1989) have incorporated a 
crop water stress index based on canopy 
temperature into a crop simulation 
model in which initial conditions and 
parameter values are adjusted to make 
simulated growth agree with remotely 
sensed observations (Maas, 1988). In 
the growth simulation model, daily dry 
matter production is calculated from 
daily PAR absorbed and an efficiency of 
conversion, and during grain filling a 
constant fraction is allocated to the 
increase in grain dry mass. In 
addition, the simulation model requires 
weather data (ambient maximum and 
minimum daily temperature, dewpoint 
temperature, and solar radiation) that 
is not always available. In contrast, 
the pure spectral approach used here 
assumes the same physiological 
principles as the simulation model but 
relates the spectral observations 
directly to yield. Thus it is 
simpler. The two approaches are best 
viewed as complementary rather than 
competing because when applied to 
particular situations the yields 
estimated should agree (in direction 
and magnitude) with each other and 
available ground truth and strengthen 
confidence in both. 
Another appealing feature of SCA is 
that the functional relations of all 
the left side terms of Eqs. [1], [2], 
[3], and [5] are linear. This makes 
them easy to use because slopes and 
intercepts are easy to interpret and 
have biophysical meaning. The 
equations are linear because there is a 
proportionality between photosynthetic 
size of the canopies and each of the 
dependent variables, APAR, DM, and 
yield through the common process, 
photosynthesis. Evapotranspiration is 
also a function of the 
photosynthetically active tissue and 
insolation so that the relation 
between ZET and ZVI is nearly linear. 
In applying spectral components 
analysis there are many pertinent 
questions to ask in order to tailor the 
required procedures to the specific 
objectives of the application. These 
include: Is information needed about 
specific ecological communities (crops, 
pastures, forests) or is synoptic 
information sufficient? If information 
is needed about specific ecological 
communities, is enough known about the 
annual calendars of the plant community 
categories involved (planting and 
green-up dates, relative rates that 
ground cover develops or greening 
occurs, growing season duration, 
senescence rate or date differences) to 
develop classifications algorithms that 
distinguish the ecological categories 
accurately enough to estimate the 
hectarage of each? Are locations of 
the perennial categories well enough 
known that masks of the areas they 
occupy can be prepared? Are indicator 
fields or sites needed that can be 
located repetitively, or will a random 
sample from within the crop category or 
the scene be adequate? What magnitude 
of difference in VI or in yield is