IMPACTS OF BIDIRECTIONAL REFLECTANCE ON THE ESTIMATION OF CROP BIOPHYSICAL PARAMETERS

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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IMPACTS OF BIDIRECTIONAL REFLECTANCE ON THE ESTIMATION OF CROP BIOPHYSICAL PARAMETERS

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