3
THEORY
SRS Sensors
quantum yield, maximum photosynthesis rate, electron transport un-
der saturating light, non-photochemical quenching, and chlorophyll
to carotenoid content ratio (Sims & Gamon, 2002; Garrity et al.,
2011; Garbulsky et al., 2011; Porcar-Castell et al., 2012). Garbul-
sky et al. (2011) and Porcar-Castell et al. (2012) provide excellent
overviews of what has been done with PRI including analyses of PRI
correlations with several of these variables at the leaf, canopy, and
ecosystem levels. We encourage our customers to use these references
as a starting resource.
3.6
Sun-Sensor-Surface Geometry Considerations
It is not uncommon for a time series of NDVI or PRI to contain high
amounts of variability due to changing environmental and observa-
tion conditions. Spectral reflectance measurements are inherently
variable due to radiation source, reflecting surface, and sun-sensor-
surface geometry. Sometimes NDVI and/or PRI values exhibit er-
ratic behavior due to changing environmental conditions. Some level
of data filtering (e.g., visual inspection for short time series or au-
tomated despiking and smoothing algorithms for longer time series)
may be required to remove spurious data points, including cleaning
up points that result in indeterminate or undefined results from the
NDVI or PRI calculation. Consider the NDVI time series shown in
Figure 6a. These data were collected from a corn canopy planted
in June. The data sampling interval was five minutes. There are
several things to notice:
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