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8/11/2019 IMPACTS OF BIDIRECTIONAL REFLECTANCE ON THE ESTIMATION OF CROP BIOPHYSICAL PARAMETERS
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THE IMPACT OF BIDIRECTIONAL
REFLECTANCE ON THE ESTIMATION OF CROP
BIOPHYSICAL PARAMETERS
Aaron D. Mullin
B.Sc. University of Lethbridge, 2009
An Unfinished Thesis
Working towards a combined MASTER OF ARTS & SCIENCES
LETHBRIDGE, ALBERTA, CANADA
Aaron D. Mullin, 2013
Dedicated to my supervisors, mentors, and most importantly my children
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CHAPTER 11.1INTRODUCTION
Understanding variation in the spatial parameter is the essence of geography (Harvey,
1969). This fact makes Remote Sensing (RS) integral as it involves the collection and
analysis of geospatial data. RS permits frequent observations over large areas of the
Earths surface and allows for variation in a range of spatial, spectral, and temporal
scales. Global and regional Earth observations occur from spaceborne or airborne
platforms and are frequently used to monitor vegetation (Jensen, 1983; Rouse et al.,
1973;Rouseet al., 1974;Sellerset al., 1996;Tucker, 1978). Large coverage areas are
necessary for synoptic views of the Earths surface whereas focused studies deliver
essential details. Observing a vegetated surface via proximal sensing (distance between
the object and the sensor is within a few metres)provides a more thorough understanding
of the variations in spectral composition of the signals and can provide a relation to the
physical state and biophysical characteristics of the target (Gamonet al., 2006;Milton,
1987;Teilletet al., 2002).
Willsttter & Stoll (1915) discovered that leaves had an internal mechanism
responsible for reflectance and that the cellular structure was responsible for high
reflectance values in the near infrared (NIR) region of the electromagnetic spectrum
(EMS). During the same period advances in instrument design were providing an avenue
to better quantify reflected energy from objects (e.g. Ives, 1915). These successes spurred
on further development and expanding applications. By the 1950s, the advances made in
biophysical remote sensing were being coupled with airborne platforms; this permitted
the collection of spectral information from vegetation at synoptic views (Krinov, 1953;
Penndorf, 1956).
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It was found that pigments were the mechanism in the plant leaves responsible for
absorbing much of the energy in the visible range and reflecting much of the energy in
the near infrared (NIR) range of the spectrum (Coulson, 1966; Gates et al., 1965;
Knipling, 1970;Rabideauet al., 1946;Woolley, 1971). Around this time, instrumentation
was being designed to sense areas of the EMS with a significant biological response,
particularly between 400 to 1100 nm. Some of the instruments sampled broad ranges of
the spectrum (e.g. Adhav, 1963; Adhav & Murphy, 1963), while others focused on
specific areas within the spectrum known for strong spectral responses because of the
pigmentation (e.g. Birth & McVey, 1968).
Technological advances continued throughout the 1960s and these advances allowed
for more efficient and accurate quantification of biophysical characteristics. The
reflectance of incident energy from a Lambertian surface would appear uniformly diffuse
(Nicodemuset al., 1977)however, it was well known that many natural surfaces do not
reflect energy in a Lambertian manner (Middleton & Mungall, 1952). An anisotropic
surface diffuses the energy at many angles in an unequal manner making them more
difficult to characterize spectrally (Kimes, 1983; Kriebel, 1978).
Possibly the most challenging issue being faced in remote sensing is understanding
the angular distribution of radiance as it returns from a natural surface and is propagated
into the hemisphere above that surface (Schott, 2007). An early attempt to address
reflectance anisotropy was made by Arcybashev & Belov (1958) who flew a
multidirectional aerial flight line pattern over a forest stand while tilting the sensor to
different view angles. More recent approaches include NASAs Multi-angle Imaging
SpectroRadiometer (MISR). The MISR has been used to gather high resolution off-nadir
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imagery since its 1999 launch. Measuring the anisotropic reflectance from spaceborne
instruments with capability for multi-angular views can provide physical information not
otherwise available from traditional nadir only observations. These directional signatures
can be used to better resolve morphology and structural information of vegetation
canopies (Dineret al., 1999).
Spectral reflectance is defined as an intrinsic property of any object, independent of
illumination and sensor angle and therefore is a central concept to remote sensing science
(Nicodemuset al., 1977; Peddleet al., 2001). However, the method needs to be expanded
upon for applications where the target surface is an anisotropic scatterer (Chandrasekhar,
1960). Spectral reflectance (Equation 1.1) is broadly defined as the ratio of radiance to
irradiance and it is wavelength () specific.
() =
(1.1)
Where is the reflectance,M is the radiance returning from the surface, scattered back to
the sensor, and scattered from adjacent surfaces, E is the irradiance incident to the
surface.
The irradiance reaching the observed surface is a composite of direct and diffuse
energy. While some of the photons are scattered away from the target other photons are
scattered onto the target. Thus, the amount of diffuse energy reaching a vegetated canopy
is a function of the original electromagnetic radiation (EMR) leaving the surface of the
solar disc and the atmosphere, clouds, adjacent targets and topography that are in and
near the path of that collimated beam (Myneniet al., 1989).
The vegetation canopy further attenuates the irradiance. Some of the energy is
reflected by the vegetation, some is absorbed, some is transmitted through the canopy,
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some of the energy is scattered from aerosols and adjacent topography, and some energy
is scattered multiple times. Any of the irradiance that returns from the surface, regardless
of path, is radiance. The sensor collects spectroradiometric data from observations of the
Earths surface; the data are mainly a function of the scattered and multiply scattered
irradiance (Myneniet al., 1989).
1.1.1Canopy structure
Canopy structure is characterized by the vertical and spatial distribution, orientation,
and density of the vegetation (Myneni et al., 1995). Specific biophysical parameters
include species, leaf angle, canopy height, leaf area index (LAI), and spatial distribution.
Light attenuation by the canopy are regulated by the canopy structure; canopy structure,
through the attenuation of light, regulates photosynthesis, respiration, transpiration, and
nutrient cycling (Ross, 1981;Sellerset al., 1995; Sellers & Schimel, 1993;Widlowskiet
al., 2004).
The primary approach to characterizing canopy structure is either through direct or
indirect observations (Brenner et al., 1995; Gower et al., 1999;Rover & Koch, 1995).
Direct observations are made while in physical contact with canopy elements. Indirect
observations use sensors to collect radiance from the canopy surface (Welles, 1990).
After sampling the radiation, the data can be related back to the canopy using various
methods. RS can play an important role in the physical characterization of vegetation
canopies.
Canopy structure and radiative transfer (RT) are closely coupled (Welles, 1990). This
close relationship means that observations of radiation returning from the vegetation
surface can be used to model canopy structure. Advantages of the indirect approach
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include the fact that observations can be repeated, made synoptically from space, aerial,
or proximal platforms, and the target is observed remotely which ensures it is not
damaged. A key disadvantage of indirect sampling is that by introducing any distance
from a target, variations in illumination angle, sensor angle, and canopy geometry are
introduced. This angular dependence is responsible for reflectance anisotropy when
surfaces are not Lambertian. The bidirectional reflectance distribution function (BRDF)
was introduced to characterize this angular dependence (Nicodemuset al., 1977).
The remote sensing of agricultural canopies are reliant on the optical properties of the
canopy (Gausman, 1977; Myers & Allen, 1968; Thomas & Gausman, 1977; Woolley,
1971). The angular dependency in agriculture has been recognized for some time (Pinter
et al., 1985;Tremblayet al., 2009). The challenges include illumination and view angles,
row orientation, topography, weather, as they all strongly affect the optical properties of a
canopy (Jackson, 1984;Pinteret al., 1985; Qiet al., 1995). A primary objective of this
research is to deepen the understanding of angular dependence in various agricultural
crops.
Agricultural canopies and fields are not a spatially uniform, idealized media. There
are many productivity variations occurring within a single field. In precision farming, the
farmers job is to optimize yield by exploiting that variability (Pinteret al., 2003). For the
farmer to increase profitability refined data on plant and soil conditions throughout the
growth cycle are preferable. These data must cross temporal and spatial barriers and
incorporate that adaptability with other technology to match rapidly changing agricultural
practices, inputs, and irrigation practices (Pinteret al., 2003).
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A vegetated canopy consistently exhibits maximum anisotropy along the Solar
Principle Plane (SPP). Figure 1.1a illustrates higher anisotropy in the backscatter
direction and lower anisotropy in the forward scatter direction.Figure 1.1b is the two-
dimensional architecture of a BRF scan showing the SPP which always aligns along the
0-180 vector with the illumination originating from 180. The Perpendicular Plane (PP)
is also shown and the anisotropy is typically lower and symmetrical along this plane.
Figure 1.1: Solar illumination is scattered hemispherically. Along the SPP, the majority
of radiance is backscatter (A). The magnitude of the backscattered radiance is typicallymore than the radiance scattered in the direction of nadir or forward scattered. The SPP
described previously is aligned along the N-S axis in this schematic (B). This diagram is
representative of a goniometer scan with high angular resolution. For this style ofgraphical presentation, the illumination angle is always oriented from the south.
A healthy vegetated canopy is green because more energy is absorbed in the blue and
red parts of the visible spectrum than in the green; conversely, the green part of the
spectrum reflects more energy than the blue and red. Observations along the SPP from
the forward scatter side of the canopy contrasts with observations along the same plane
but from the backscatter side of the canopy. There are different results even though the
target is invariant (Coburnet al., 2010).
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If the target exhibited no anisotropic reflectance, the apparent brightness of the
surface would appear the same no matter what angle the sensor viewed the surface from.
This type of surface would be considered Lambertian. Spectral reflectance is defined
using idealized Lambertian surfaces. Reflectance from a Lambertian surface is calculated
by:
=0cos
where 0 is the intensity perpendicular to the surface and is the angle from
perpendicular to the surface. As gets further from perpendicular the 0decreases and
approaches zero at illumination angles near parallel to the surface but the surface does not
appear to change to the observer (Figure 1.2). There are several examples of near
Lambertian surfaces that are naturally occurring. Examples of naturally occurring
Lambertian surfaces include certain areas of playa and gypsum sand.
Figure 1.2: Distribution of reflected energy from a Lambertian surface.
The acquisition of aerial photos, imagery, and spectral data with the sensor angle
exclusively at nadir, can lessen the effects of anisotropic reflectance (Miltonet al., 2009;
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Qin et al., 2002). Off-nadir sensor angles can be used to measure the anisotropically
diffused EMR from vegetated surfaces (Qi et al., 1995). There are generally three
strategies to deal with the anisotropic effects:
Discount it (Miltonet al., 2009; Pinker & Stowe, 1990),
Correct it using a normalization routine (Bacouret al., 2006;Jacksonet al.,
1990), or
Use the angular data as a source of information (Barnsleyet al., 1994;Qiet
al., 1995).
This research focused on the third approach.
The bidirectional reflectance distribution function (BRDF) is the full spectral
characterization of a surface from a single illumination angle and all possible view angles
(Nicodemus et al., 1977). The BRDF encompasses all discrete solid angles and thus it
can never be directly measured because it is not finite (Nicodemuset al., 1977). While
sensors and instruments sample the BRDF to come up with estimates of its properties the
BRDF of a surface cannot be measured physically; the concept is important because it is
central to many measureable quantities (Figure 1.3).
A bidirectional reflectance factor (BRF) is the ratio of radiance reflected back to the
sensor from a single illumination angle at a single sensor look angle (Figure 1.3a). The
hemispherical conical reflectance factor (HCRF) is the practical analog to the theoretical
BRF (Schaepman-Strub et al., 2006). The left side or irradiance side of Figure 1.3b
represents a beam of EMR that is no longer fully collimated after it reaches the sphere or
atmosphere (represented by the arc), thus scattering the incoming irradiance in a diffuse
manner. The arc is also representative of adjacency effects. The energy reflects off the
Lambertian surface as a point source and it radiates outward in a conical pattern. The
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HCRF is a more realistic representation as to what happens in the field; however the
convention will be followed where the HCRF is referred to as the BRF.
Figure 1.3: Visual representation highlighting differences in directional reflectance
terminology: (A) bidirectional reflectance factor (BRF); (B) hemispherical conical
reflectance factor (HCRF).
Variability in reflectance of Earth surface targets with respect to a changing Sun or
sensor angle (or both) has been reviewed extensively over the past forty years (Barnsley
et al., 1994;Coburn & Peddle, 2006; Deeringet al., 1992;Jacksonet al., 1990;Kimes,
1983; Sandmeier et al., 1998a; Suits, 1972). The interaction of solar radiation with a
vegetated surface could be better resolved for improvements in interpretation of airborne
and spaceborne data (Knipling, 1970).
Most RS data is collected with the sensor oriented perpendicular to the target (nadir);
other view angles have been identified as important (Figure 1.4)such as along the SPP
where the maximum anisotropy typically occurs and along the PP which is typically
symmetrical across a vegetated surface (Miltonet al., 2009). Information gathered from
viewing a vegetated surface along the SPP can be used to extract structural information of
that canopy. For example, there is an anisotropic signature that could be used to
differentiate between crops with erect versus horizontal plant architecture.
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Figure 1.4: These images of a wheat canopy illustrate anisotropy; the arrows represent the
illumination origin. (A) the PP; (B) the SPP as viewed from the forward scatter side of
the surface; and (C) the SPP as viewed from the backscatter side of the surface.
Broadband multispectral data is effective for certain analytical tasks, global scale
vegetation studies for example. Hyperspectral data can provide more robust target
analysis because of the higher spectral resolution provided by narrow spectral bands
(Bannari et al., 2006; Thenkabail et al., 2002). More narrow spectral bands are more
effective at resolving spectral signatures for specific targets. Hyperspectral sensors have
proven advantageous over broadband sensors through extraction of optimal narrowband
spectra for characterizing vegetation canopies (Elvidge & Chen, 1995;Thenkabailet al.,
2000). However, hyperspectral data are more costly, more data intensive, and have more
redundancy compared to multispectral data.
Spaceborne sensors such as the EO-1 Hyperion and CHRIS-PROBA and airborne
sensors such as the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) are
hyperspectral. The additional spectral information provided by hyperspectral data has
been shown to improve image classification accuracy for a variety of Earth surface
features. The addition of multiple view angles with a hyperspectral sensor has been used
to more effectively extract information on vegetation structure in agriculture (Moran et
al., 1997;Smithet al., 2008).
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A necessary step toward understanding the airborne and spaceborne scale is through a
deeper investigation into near surface reflectance of the canopy at proximal distances
(Gamon et al., 2006). Investigating the angular and spectral variability from proximal
distances provides fundamental information on the biophysical characteristics of a
vegetated canopy (Milton, 1987;Sandmeieret al., 1998a;Teilletet al., 2007).To begin
that investigation, more background on the target is necessary.
1.1.2Vegetation characterization
Remote sensing in agriculture has applications in assessment of crop conditions,
species classification, and yield estimation. The accurate characterization of the volume,
distribution, and orientation of a vegetated canopy is necessary for biophysical and
biochemical parameter extraction (Pinty & Verstraete, 1991). Two of the most integral
components in the characterization of vegetation are foliage amount and foliage
orientation (Lang & Xiang, 1986;Langet al., 1985). The dominant parameter used for
characterizing the volume of canopy foliage is LAI (Weisset al., 2004). Two parameters
that are used to characterize the foliage orientation include the Leaf Inclination
Distribution Function (LIDF) and Mean Tilt Angle (MTA) (Weiss et al., 2004). The
mechanisms used to derive the parameters need to be defined prior to defining LAI,
LIDF, and MTA.
LAI is a central input for many canopy photosynthesis and evapotranspiration models
(Weiss et al., 2004). LAI is one half of the total leaf area per unit ground surface area
(Myneniet al., 1989). It is an estimate of the physical area of the leaves of a plant, from
the ground to the top of the canopy (Marshall, 1968). Methods used to physically and
empirically measure LAI have been reviewed extensively (Baret & Buis, 2008;Breda,
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2003;Chenet al., 1997; Darvishzadehet al., 2008;Ryuet al., 2010; Smithet al., 2006;
Weisset al., 2004; Yaoet al., 2008; Zheng & Moskal, 2009). The direct measurement of
LAI is destructive and time consuming (Lang et al., 1985). Thus, optical methods are
often used to estimate parameters such as gap fraction, which in turn allow for the
indirect derivation of canopy parameters.
Welles (1990) defined the gap fraction as the fractional view in a certain direction
from beneath the canopy of the visible sky. A gap fraction contains information that can
be used to derive structural information or more specifically mean foliage density. If
there is a distribution of foliage along a path and that path begins at the surface of the
canopy and ends at the location of the sensor there would be a minimal amount of
information provided about the foliage density. Many paths, together, begin to resolve
that foliage density and with enough of those paths mean foliage density can be
estimated. An estimate of LAI is extracted by inverting the gap fraction data and using
the logarithmic relationship between gap fraction and LAI to estimate LAI (Welles, 1990;
Wilson, 1959).
The only reason it is possible to use proxies such as gap fraction are because of the
close coupling between radiative transfer and canopy. Many vegetation structural
parameters can be inferred from the gap fraction because of the dependence between
radiation, absorption, and canopy structure. In the estimation of foliage character the
assumption of a random distribution of leaves has often been used (Lang & Xiang, 1986).
Optical methods provide the means to estimate variables like gap fraction while statistical
models convert those measurements into estimates of the canopy parameters discussed
earlier (Langet al., 1985).
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One of the drawbacks of optical methods is that they cannot differentiate between
living leaves, dead leaves, and woody material (Welles, 1990). Thus, an optical
instrument such as the LAI-2000 estimates the effective LAI (eLAI), which includes the
living and dead foliage and the plant structure. The eLAI estimate is calculated using the
probability of seeing sky from below the canopy in the blue region (
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method called the Mean Tilt Angle (MTA). Lang and Xiang (1986)used gap fraction to
derive the foliage orientation characteristics using a combination of trigonometry, linear
regression, and statistics. The major steps include comparison to an idealized canopy and
relating it to the mean inclination angle of the leaf (Welles & Norman, 1991). After
inverting the gap fraction the mean inclination angle is derived from a stochastic function
(Welles, 1990). MTA is estimated using simple field instruments like the LAI 2000.
1.1.3Leaf reflectance and absorbance
Spectral reflectance provides information on the absorption features of many
materials. Spectral signatures are representations of reflected, absorbed, and emitted
EMR as a function of wavelength and are used as unique identifiers (Gateset al., 1965).
The optical properties of foliage are controlled by tissue structure, water content, and
physiology (Gateset al., 1965;Thomas & Gausman, 1977).
Leaves exhibit both diffuse and specular reflectance properties (Grant, 1987)making
them particularly challenging RS targets. The diffuse Lambertian characteristics (Figure
1.2)are mainly caused by the multiple scattering that occurs at the cell wall-air interface
(Kumar & Silva, 1973;Woolley, 1971). The non-Lambertian reflectance typical of a leaf
is simply a result of reflection from the leaf surface (Grant, 1987).
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Figure 1.5: Hyperspectral reflectance signatures characterizing the properties of healthyleaves and soil. Absorption features are specified. The spectral range is from 400-1300
nm taken near Lethbridge, Alberta.
A healthy leaf will generally absorb most of the incoming radiation in the visible
range (400 700 nm) (Figure 1.5); for use in various plant functions, particularly
photosynthesis, and reflect very small amounts of radiation in this area (Gates et al.,
1965). Chlorophyll absorption is an important feature in the visible spectrum as the
concentration of chlorophyll is an indicator of nutritional stress, photosynthesis, and
phenological stage (Collins, 1978; Curran, 1989). In the near infrared (NIR, 750 1300
nm) much of the radiation is scattered and reflected by the water content and the inner
leaf physiology, particularly the spongy mesophyll cells (Gausman, 1977). The difference
in intensity between the NIR and visible (680 750 nm) is called the red edge shift
(Horleret al., 1983). The shift is caused by the high internal leaf scattering causing high
reflectance in the NIR contrasted with the low reflectance in the red caused by
chlorophyll absorption and is used as an indicator of plant health (Collins, 1978;Mutanga
& Skidmore, 2007).
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1.1.4Phenology
The study of phenology is the study of vegetation dynamics. Agricultural crops pass
through a number changes during a growth cycle. The day of seeding is the zero point
along a timeline that includes important growth stages such as emergence, flowering, and
senescence. For example, in a cereal canopy such as barley and wheat, growth stages
include a germination, tillering, stem elongation, heading, and ripening stage.
The canola passes through seedling, rosette, budding, flowering, and ripening stages.
Pea canopies have similar growth patterns as canola canopies. Peas go through four
principal stages: emergence, vegetative, reproductive, and senescence(Knott, 1987). The
later stages of development for all four canopies in this study were dewatering and
senescence. There is variability within each variety, species, and crop. However, within
that variability there are distinctive patterns that consistently arise (Loomis & Williams,
1969). These patterns include soil effects, row effects, closed homogenous canopy, open
canopy, shedding of lower canopy, fruiting, flowering, ripening, dewatering and
senescence.
Agronomic decisions are reliant on the recognition of growth stage and being able to
recognize those common patterns is imperative. Precision agriculture, the practice of
matching inputs to site-specific crop requirements, is a consumer of this type of
information (Goel et al., 2003). For example, knowledge of how many Days After
Planting (DAP) specific growth stages occur can provide an important baseline from
which to make key economic decisions, helping to reduce inputs (Pinteret al., 2003).
The efficient use of agricultural chemicals makes agricultural production more
profitable and more environmentally sound (Pinteret al., 2003). On some Idaho potato
fields, nitrogen is being applied at variable rates before row closure for improved yield
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Figure 1.6: Illustrating the structural differences between (A) erectophile and (B)
planophile architectures. Barley (A), exhibits a vertical leaf orientation while canola (B)exhibits a horizontal leaf orientation.
Leaf orientation in a typical erectophile structure produces a much lower spectral
response in the NIR region of the spectrum compared to the leaf orientation typical of a
planophile structure. The spectral similarities of the plants in the 400 700 nm range
makes it difficult to find spectral differences between the green plants in the visible
portion of the spectrum. However, from 750 900 nm the percentage of reflectance is
noticeably lower for the erectophile structures compared to the planophile structures
when estimated from a nadir sensor look angle (Figure 1.7).
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Figure 1.7: Typical spectral reflectance signatures for vegetation canopies with
planophile architectures (canola and pea) and erectophile architectures (wheat and barley)
taken near Lethbridge, Alberta. Sensor look angle was at nadir. Note differences inreflectance between 750 and 900 nm. The canola and pea exhibit ~40 and 50%
reflectance while the wheat and barley exhibit ~30% and sloping to ~35% in the same
spectral region.
1.1.6
Canopy reflectance and absorbance
Plant canopies reflect, absorb, and transmit EMR. While all regions in the visible
spectrum are strongly absorbed by healthy plants, their green colour is due to greater
absorption of blue and red wavelengths (Figure 1.7). The spectral response of a
vegetation canopy is caused not only by the biophysical and biochemical properties
within the leaves (Gates et al., 1965; Gausman, 1977), but also by the plant physical
structure (Knipling, 1970).
The path that light takes as it makes its way through an agricultural canopy is
complex. As the canopy grows and fills in gaps left by seeding plants in rows, reflectance
from the soil is often negated or minimized. As row crops transition from discrete
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structures into homogenous canopies the contribution from soil reflectance transitions
from an important component to insignificant within the overall reflectance for a
vegetated field of view (FOV). Plant architecture, such as leaf orientation, changes
significantly throughout the phenology and differs between plants. Further, the angle of
illumination in relation to the target determines the path that the direct beam of irradiance
makes through the canopy. This relationship has a large effect on the degree of multiple
scattering (Pinteret al., 1990).
Leaf orientation and plant structure are highly dependent on species, phenology, and
canopy health. Hapke et al. (1996)observed that the canopy structure and leaf orientation
affect shadow geometry, which in turn impacts the nature of reflectance variation in a
vegetated canopy. Variations in sensor view angle also impact reflectance variation
(Kimes, 1983). For all strong backscattering surfaces, a prominent hotspot in the
backscatter direction arises when the sensor geometry aligns with the illumination
geometry (Suits, 1972). When sensing a surface from this geometry the surface will have
the least amount of shadow.
The hotspot is an important reflectance feature of many surfaces. Some surfaces have
strong forward scattering properties (i.e. snow) while other surfaces have strong
backscattering properties (i.e. vegetation, soil). The hotspot, in vegetation or soil, has a
peak in reflectance occurring where the illumination and view directions coincide (Li &
Strahler, 1992). Coulson (1966)explains that the hotspot in a vegetated canopy coincides
with the minimum shadow.
The size, shape, density, orientation and spatial distribution of canopy foliage are all
known to influence the hotspot (Qin & Xiang, 1994). When the illumination geometry
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and canopy structure are at normal angles, the shadows are minimized (Qin & Xiang,
1994). Canopy height, leaf size, and foliage distribution are prime factors controlling
hotspot distribution and amplitude (Marshak, 1989).
Underlying controls on reflectance anisotropy are the shadow distribution and how
that distribution changes when the illumination and sensor look angle vary (Hapkeet al.,
1996; Jacksonet al., 1990;Kimes, 1983). As the leaf orientation moves from vertical to
horizontal (from erectophile to planophile) the reflectance anisotropy becomes more
asymmetrical in relation to nadir (Ross & Marshak, 1989). Canopy structure (i.e.
erectophile plant architecture or planophile plant architecture) helps determine the
shadowed versus illuminated portions of the canopy. The bidirectional reflectance of a
vegetated surface can be used to better understand the underlying physical mechanism
responsible for the anisotropy (Roujean & Breon, 1995;Verstraeteet al., 1990; Walthall
et al., 1985).
The spectral reflectance magnitude of the hotspot feature is dependent on canopy
parameters such as leaf size, LAI, and leaf inclination angle (Gerstl & Simmer, 1986;
Ross & Marshak, 1989; Strahler & Jupp, 1990). Thus, the hotspot information alone
could be used as a proxy to extract biophysical information of the canopy (Barnsley et
al., 1994;Lacazeet al., 2002).
1.1.7 Vegetation indices
RS data have been used for decades to extract and modelbiophysical parameters
(Teillet et al., 1994; Tucker, 1978). Plant physiologists, crop modellers, and other
stakeholders concerned with plant biophysical parameters use Vegetation Indices (VIs) as
inputs for various models. There is a well-defined relationship between plant growth,
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visible, and near infrared EMR (Bauer, 1985;Jackson, 1984). A VI derived from RS data
relies on spectral features found in a variety of important spectral bands. For example,
VIs have been effective at detecting variations in biomass and LAI in forests (Huete et
al., 2002;Justiceet al., 1985).
The first vegetation index, the simple ratio (SR; Equation 1.2), makes use of spectral
bands in the NIR and red regions of the EM spectrum. The normalized difference
vegetation index (NDVI; Equation 1.3), developed by Rouse et al.(1973), makes use of
the strong chlorophyll absorption feature found at 670 nm and the NIR vegetation
reflectance peak found at 800 nm. This popular VI measures the magnitude of the
infrared reflectance and is normalized by the red absorption feature, providing an index
of how vigorously the vegetation is growing. The generalized formula for SR and NDVI:
=
(1.2)
=()
(+) (1.3)
The relationshipbetween the NDVI and some biophysical parameters, such as LAI
and biomass, is well established. However, there is a saturation effect once the biomass
abundance reaches acertain threshold (Baret & Guyot, 1991). Saturation in a normalized
ratio occurs when for every increase in the measured parameter there is no increase in the
output value of the ratio. For the NDVI, this occurs when vegetation density (measured as
LAI) and vigour stop producing increases in the recorded values in the NIR (Knipling,
1970).
By using the unique physiology of the target feature it is possible to minimize canopy
background noise using ratios (Hui Qing & Huete, 1995). The polar plot (Figure 1.8)is
an NDVI; it is the ratio of two BRFs, one band in the red and one band in the NIR with
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Equation 1.3 applied to it. NDVI is normally measured from nadir. Recent studies
(Coburn & Noble, 2009)have demonstrated that NDVI and other ratios are variable due
to BRDF. These differences were not removed by the ratio.
Figure 1.8: NDVI plot of a wheat canopy at 87 DAP. The Sun is south of the target (at
the bottom of the figure) while north is at the top of the page. The north-south plane is the
SPP. The east-west plane is the PP. Each black dot is representative of discrete locationswhere the BRDF was sampled from. There are 217 data points in all. The dot in the
middle of the circle is nadir and each subsequent concentric ring is 10 further from
nadir. These discrete data are interpolated to create a continuous surface; a BRF. In this
format, each BRF is wavelength specific. A NIR BRF and a red BRF are substituted intoan NDVI formula (Equation 1.3) for the desired output seen above. The larger magnitude
NDVI values represent more vegetation and lower magnitude represents less vegetation.
1.1.8Bidirectional reflectance
The BRDF has been modelled at length (Gerstl & Simmer, 1986; Hapke, 1981;
Jacquemoud & Baret, 1990; Jacquemoudet al., 1995;Roujeanet al., 1992;Suits, 1972;
Verhoef, 1984)because it provides a foundation that connects the causative factors with
the remote sensing data (Suits, 1972). It has been extensively studied through
investigation of the BRDF effects of many natural surfaces, including other planets
(Hapke, 1981; Hapke & Wells, 1981), snow (Hudsonet al., 2006;Painteret al., 2003),
soil (Kimes, 1983;Wanget al., 2012;Wanget al., 2010), leaves (Bousquetet al., 2005;
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Gausman, 1977; Grant, 1987), trees (Chen et al., 2003; Lacaze et al., 2002), and
vegetation canopies (Coburn & Noble, 2009; Coburn & Peddle, 2006; Kriebel, 1978;
Sandmeier et al., 1999;Sandmeier et al., 1998a). Much of this broad scoping research
has assisted in the determination that the relationship between canopy structural change
and a unique BRDF signature is significant and can be used to assess change (Kimes,
1983; Kriebel, 1978;Martonchik, 1994;Ross & Marshak, 1989).
Bidirectional reflectance from natural surfaces is identified as one of the most vexing
issues in the characterization of Earth surface features (Deering & Eck, 1987;Sandmeier
& Itten, 1999). The term bidirectional refers to the geometry of the Sun and sensor. The
formal description of bidirectional reflectance (Equation 1.4) is the wavelength
dependent ratio of scattered radiance from an object or surface in the direction of ,
to the irradiance from the direction of,:
=(,,)
(,,)[1] (1.4)
BRDF is a four-dimensional wavelength () dependent function. Nadir (Z) is given,
providing general orientation. BRDF can be defined as a geometric function of the ratio
of upwelling radiance (L) scattered toward the sensor (Figure 1.9), at a view zenith (o)
and azimuth angle (o), over the downwelling irradiance (E) incident to the surface at an
illumination zenith (i) and azimuth angle (i) (Nicodemuset al., 1977).
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The study of bidirectional reflectance starts with the systematic measurement of
radiance from multiple angles over a hemispherical view at some controlled distance
from the target. In the field, sampling proximal BRDF must be quick so that changes in
illumination geometry are minimized. The instruments used to gather bidirectional data in
the field and laboratory are called field goniometer systems or field goniometric
radiometer devices, hereafter goniometers. These instruments are angular measuring
devices paired with a radiometer and have been effective for studying the BRDF of a
diversity of surfaces, in proximity (Coburn & Noble, 2009; Painter et al., 2003;
Sandmeier & Itten, 1999).
Current achievements in remote sensing have been preceded by fundamental research
into the BRDF. BRDF effects have been studied extensively by investigating the
interaction of directional reflectance with many different natural surfaces, including other
planets (Hapke, 1981; Hapke & Wells, 1981), snow (Hudsonet al., 2006;Painteret al.,
2003), soil (Kimes, 1983;Wanget al., 2012;Wanget al., 2010), leaves (Bousquetet al.,
2005;Gausman, 1977;Grant, 1987), trees (Chenet al., 2003;Lacazeet al., 2002), and
vegetation canopies (Coburn & Peddle, 2006; Kriebel, 1978; Sandmeier et al., 1999;
Sandmeier et al., 1998a). BRDF data or models are used for correction of view and
illumination angle effects (i.e. image standardization and mosaicking), accurate land
cover classification, atmospheric correction, and other applications.
Many instrument styles have been bulky, expensive, and often damaging to the target.
The University of Lethbridge Field Goniometer System II (ULGS-2) has an advantage
over some of these other instruments because its been designed as an efficient low-
impact, mobile device that can make consistent, repeated measurements over the same
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surface multiple times during the growing season (Coburn & Noble, 2009). The essentials
of this goniometer system are the pairing of two spectroradiometers with an angular
measuring device. The lightweight, mobile design minimizes adjacency effects caused by
the positioning apparatus. By automating the sensor the user(s) no longer have to trample
the adjacent vegetation to gather data nor do they have to be next to the target to gather
the data. The ULGS-2 has been paired with two hyperspectral, spectroradiometer as a
standard operating package.
Figure 1.10: The ULGS-2 goniometer system. (a) 1. Azimuth motor; 2. Sensor sled; 3.
Quarter arc with 2m radius; 4. Control computer; 5. Battery and inverter. (b) Close-up. 1.Azimuth motor and gear box; 2. Power distribution and downwelling spectroradiometer;
3. Sensor sled and motor drive carrying the upwelling spectrometer.
When viewing and comparing any dataset it is often useful to adjust the values to a
common denominator. For BRDF that common denominator is nadir. Sandmeier et al.
(1998a) used normalization and indexing for ease of analysis, visualization, and
comparison. Two indicies are: the Anisotropy Factor (ANIF; Equation 1.5) and the
Anisotropy Index (ANIX). The ANIF (e.g. Figure 1.11) is BRFs related to nadir
(Sandmeier et al., 1998b). It has also been called relative reflectance (Jackson et al.,
1990)
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ANIF (, , , ,) =(, ,, , )
(, , ) (1.5)
Where BRF is bidirectional reflectance factor, R0 is nadir reflectance factor, is
wavelength, is zenith angle, is azimuth angle, i is illumination direction, and r is
viewing direction.
Figure 1.11: ANIF of wheat at 61 DAP. Each line represents a VZA along the SPP. The
positive VZAs are forward scattering and the negative VZAs are backscattering. TheANIF is normalized to nadir, so nadir would be equal to. Below one would be lower
reflectance than nadir and above one would be more reflectance than nadir. The +10
VZA straddles nadir and is the closest VZA to nadir across the whole spectral range. The+30 VZA is also very close to nadir, more in the visible than in the NIR spectrum range.
The -10 VZA has moderately more reflectance than would be found at nadir. At -40,
this VZA is the most different from nadir when compared with the other three.
The anisotropic nature of a surface can also be characterized through indexing
(Sandmeier et al., 1998a). The ANIX was developed for this purpose. The ANIX
(Equation 1.6) is the ratio of maximum BRF to minimum BRF typically acquired along
the SPP per spectral band (); the PP has also been used to characterize the anisotropic
reflectance (Sandmeieret al., 1998a):
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ANIX () =()
() (1.6)
Where BRFmaxare the maximum and BRFminare the minimum bidirectional reflectance
factors. The ANIX (e.g.Figure 1.12)shows the variability of the BRF by quantifying the
magnitude change per wavelength along a plane (i.e. SPP, PP). Along the SPP, the
BRFmax estimate would (in theory) come from the hotspot (theoretical because the hotspot
is shadowed by the sensor), while the BRFmin would emanate from nadir or be in close
proximity to it (Sandmeieret al., 1998a).
Figure 1.12: ANIX of wheat at 43 DAP. Each line represents a certain amount of days
after planting along the PP. AS the ANIX increases from one, it represents more
reflectance anisotropy.
1.1.9
Proximal remote sensing
Primary and fundamental research in the field of remote sensing is done from
proximate distances (Milton, 1987). Proximal sensing is the acquisition of information
when the sensor and target are in close proximity (Teilletet al., 2002). Improvements in
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Characteristics of bidirectional reflectance as they relate to the structure of an
agricultural canopy
1.3 SUMMARY
Plant architecture, canopy structure and near-surface radiative transfer are closely
coupled (Qinet al., 2002; Ross, 1981). The transmission of photons through the canopy
and also the multiple scattering is partially controlled by the plant architecture and
canopy structure (Myneniet al., 1997), thus the spectral reflectance characteristics differ
when comparing unlike structures. The architecture of a plant plays an important role in
the attenuation of spectral reflectance. By following a plant through its life cycle and
recording the BRF for different stages, key information to assist in vegetation parameter
estimation is provided.
Phenological changes also affect the spectral reflectance. The microstructure of a
plant (i.e. canopy density, leaf orientation, etc.) and the macrostructure of a plant (i.e.
height, distance between rows, width of rows, etc.) underlie the changes in anisotropic
reflectance. The geometrical distributions of elements at the canopy scale and at the plant
scale affect the canopy-radiation interaction.
Further field research on bidirectional reflectance will allow for improvements in the
BRDF products that the airborne and spaceborne sensors, equipped with directional
capabilities, can provide to those interested in Earth observation. The proximal data
provided by goniometers is crucial in this research area.
RS technology is evolving rapidly and is now sometimes used to make relatively
accurate estimates of canopy parameters real-time thus optimizing management decisions
(Tremblay et al., 2009). Through researching the variability in anisotropic reflectance,
the biophysical characterization of a crop can be streamlined which will allow
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improvements in derived health indicators and crop productivity parameters. These
improvements will enable more effective decisions in agricultural modeling and
management and will benefit agricultural sustainability and, ultimately, food security.
The applications of this research include integration into future satellite sensor design,
precision farming, crop model optimization and algorithm development for improved
parameter extraction.
The research objectives were to compare the bidirectional reflectance, phenology, and
biophysical development of four crops. In preparation for more sustainable agricultural
production an improved understanding of the relationship between the structure,
physiology, and phenology of crops is important. This thesis focuses on the relationship
between the BRF, the canopy structure and the phenology of barley, wheat, canola, and
pea using multi-temporal, hyperspectral, bidirectional reflectance over a single growing
season.
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