Retrieving leaf chlorophyll content in wheat and corn using Landsat-8 imagery
by
Joyce Arabian
A thesis submitted in conformity with the requirements for the degree of Master of Science
Department of Geography University of Toronto
© Copyright by Joyce Arabian 2015
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Retrieving leaf chlorophyll in crops using Landsat-8 images
Joyce Arabian
Master of Science
Department of Geography
University of Toronto
2015
Abstract
The purpose of this study is to develop a method of modeling crop leaf chlorophyll content
(Chlab) using remote sensing data with a physically-based modelling approach. During the 2013
growing season, ground data were collected at 28 corn and wheat sites near Stratford, Ontario.
Effective leaf area index, hyperspectral leaf reflectance and transmittance, and chemically
extracted Chlab were acquired at each site. A two-step inversion process was developed to model
crop Chlab using Landsat-8. In this process, a look-up-table (LUT) was developed using SAIL, a
bidirectional radiative-transfer model, to simulate canopy reflectance. The LUT was then utilized
to calculate leaf-level reflectance and input into PROSPECT, a leaf-level radiative transfer
model, to estimate Chlab. Validation of PROSPECT with ground-based Chlab using simulated
Landsat-8 bands shows an R²= 0.83 and RMSE=8.48 μg/cm². Validation using the LUT shows
an R²=0.64 at the leaf level and R²= 0.87 at the canopy level.
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Acknowledgments
The completion of this paper would not have been possible without the support and guidance of
my advisor, Dr. Jing Chen. I would like to express my gratitude to Dr. Chen for providing me
with this research opportunity, research independence, insight, immense knowledge, and his
patience throughout the process. I would also like to thank Dr. Holly Croft whose knowledge
throughout this processes was invaluable. I would like to express my deepest appreciation for her
patience and availability, and also for giving me opportunities to participate in research outside
the scope of my project. Holly – thank you for guiding me through every step.
My thesis work would not have been enjoyable without the new friends and research partners I
made along the way. For this, I would like to thank Alemu Gonsamo, Remy Luo, Rong Wang,
Ting Zheng, Bin Chen, Min Zhang, Nadine Nesbitt, and my permanent study partner, Jenny
Jung.
Besides my advisors, I would like to thank my committee members, Dr. Yuhong He and Dr.
Sean Thomas for their presence on the committee, questions, comments and feedback. . I would
also like to thank Agriculture Canada for providing me with the means and financial support to
conduct the field work.
I would like to extend to gratitude to my family and friends who have supported and encouraged
me throughout the process. Specifically to my parents, Hagop and Raymonda Arabian, and my
sister Cynthia. I would like to thank Jamie Diamond for his continuous support and patience with
me when I was most frustrated. Finally, I would like to thank my sister Grace, who always took
the time to proofread my work, aid me with Python scripting, help me with decisions, and
encouraged me to succeed.
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Table of Contents
Acknowledgments.......................................................................................................................... iii
Table of Contents ........................................................................................................................... iv
List of Tables ................................................................................................................................ vii
List of Figures .............................................................................................................................. viii
Chapter 1 Introduction & Background ............................................................................................1
1 Introduction & Objectives ...........................................................................................................1
1.1 Background ..........................................................................................................................2
1.1.1 Leaf Optical Properties ............................................................................................2
1.1.2 Plant biophysical and biochemical parameters ........................................................3
1.1.3 Canopy and leaf reflectance modelling ....................................................................5
1.2 Objectives ............................................................................................................................6
Chapter 2 Literature Review ............................................................................................................8
2 Literature Review ........................................................................................................................8
2.1 Remote sensing in vegetation ..............................................................................................8
2.2 Vegetation & chlorophyll indices ........................................................................................9
2.3 Recent studies with radiative transfer models ...................................................................11
2.3.1 The PROSPECT model..........................................................................................11
2.3.2 The SAIL model ....................................................................................................13
2.3.3 Past studies on radiative transfer models to estimate chlorophyll .........................17
Chapter 3 Methods .........................................................................................................................20
3 Methods .....................................................................................................................................20
3.1 Introduction ........................................................................................................................20
3.2 Study sites and background ...............................................................................................22
3.3 Field data collection ...........................................................................................................24
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3.3.1 Ground-based effective LAI measurements ..........................................................24
3.3.2 Measurement of crop biophysical parameters .......................................................25
3.3.3 Leaf reflectance and transmittance measurements ................................................26
3.4 Remote sensing data ..........................................................................................................27
3.5 Chlorophyll indices to retrieve chlorophyll .......................................................................29
3.6 PROSPECT Model validation ...........................................................................................31
3.6.1 Landsat simulation and hyperspectral spectral comparison ...................................31
3.6.2 Fixing the leaf structural parameter .......................................................................32
3.7 Two-step model inversion using SAIL and PROSPECT ..................................................33
3.7.1 Creating a LUT using altering LAI and SZA ........................................................34
3.7.2 Modelling leaf reflectance using the SAIL canopy model ....................................36
3.7.3 Estimating LAI using RSR ....................................................................................37
3.7.4 SAIL modelled canopy reflectance ........................................................................37
3.7.5 Bilinear Interpolation and Inverse Distance Weighting.........................................40
3.7.6 Using modelled leaf reflectance to calculate transmittance ...................................41
3.8 Summary of Methods .........................................................................................................41
Chapter 4 Results ...........................................................................................................................43
4 Results .......................................................................................................................................43
4.1 Seasonal trends in LAI and chlorophyll.............................................................................43
4.2 Chlorophyll indices to estimate crop chlorophyll ..............................................................48
4.3 LAI maps using RSR .........................................................................................................51
4.4 Model Validation ...............................................................................................................54
4.4.1 PROSPECT Model Validation ..............................................................................54
4.4.2 SAIL Model Validation .........................................................................................57
4.5 Model Inversion .................................................................................................................58
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Chapter 5 Limitations ....................................................................................................................61
5 Limitations ................................................................................................................................61
Chapter 6 Discussion and Conclusion ...........................................................................................64
6 Discussion and Conclusion .......................................................................................................64
6.1 Significance........................................................................................................................65
6.2 Future Research .................................................................................................................66
References ......................................................................................................................................67
Appendix A ....................................................................................................................................73
Appendix B ....................................................................................................................................75
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List of Tables
Table 1 Common vegetation indices based on the NIR and red bands ........................................ 10
Table 2 Inputs into the PROSPECT model .................................................................................. 12
Table 3 Inputs into the SAIL model ............................................................................................. 14
Table 4 Four study sites used to study chlorophyll content. Zero N sites refer to sites where no
fertilizer was applied while N sites refer to sites that received fertilizer. ..................................... 22
Table 5 Summary of field data collected between May and September. Wheat (W) data was
collected between May and July while corn (C) was collected between June and September. ... 24
Table 6 Summary of Landsat imagery, and the field dates of collection to be compared. The
solar zenith (𝜃𝑠) and solar azimuth (𝜑𝑠) are included for the dates............................................. 28
Table 7 A list of tested spectral indices in this study. The formula contains the calculations
conducted with Landsat bands. ..................................................................................................... 30
Table 8 Inputs in the SAIL model to create a LUT. Two LUTs were created with the same
parameters but with different inputs of leaf reflectance; one healthy and one unhealthy leaf. .... 36
Table 9 Differences between chlorophyll in the study plots throughout the growing season. P-
values<0.05, shown in bold, show statistically significant differences between the nitrogen and
no nitrogen areas. .......................................................................................................................... 46
Table 10 Differences between LAI in the study plots throughout the growing season. P-
values<0.05, shown in bold, show statistically significant differences between the nitrogen and
no nitrogen areas. .......................................................................................................................... 47
Table 11 A summary of the results from spectral indices. Relationships between the index and
empirical chlorophyll measurements are displayed in the table. Linear regressions were used for
analysis. ......................................................................................................................................... 49
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List of Figures
Figure 1 A typical leaf reflectance curve of green vegetation adapted from Hoffer (1978) .......... 3
Figure 2 An example of a satellite sensor capturing radiance from a target; Ɵ𝒔 represents the
solar zenith angle, Ɵ𝒗 represents the view angle, ɸ𝒔 represents the solar azimuth angle, and ɸ𝒗
represents the view azimuth angle. Adapted from Ranson, Daughtry, Biehl, & Bauer (1985). ..... 9
Figure 3 A diagram showing the leaf inclination and azimuth angle for a single leaf. ............... 15
Figure 4 Three LIDF curves available in SAIL. The LIDF shows the cumulative frequency of
leaves at different angles. .............................................................................................................. 16
Figure 5 Model inversion process. The gray boxes represent inputs while the black boxes
represent outputs. The dashed lines represent the inversion process and the double arrow lines
represent comparison to empirical data. This model flowchart is modified from a past study
conducted by Zhang et al. (2008) ................................................................................................. 21
Figure 6 Images of the two wheat plots (WE1 & WE2) are shown in the top two images while
images of the corn plots (CE1 &CE2) are seen in the bottom image. .......................................... 23
Figure 7 CE1_07 on June 18. Measured Cab was 42.31. The image on the left demonstrates the
errors with the integrating sphere, and the image on the right shows the correction. .................. 27
Figure 8 Example of a hyperspectral curve from leaf measurements and simulated Landsat-8
bands that were used as PROSPECT inputs. ................................................................................ 32
Figure 9 PROSPECT-4 simulations for hyperspectral (left) and Landsat (right) bands by using
fixed input variables except for N, the structural parameter (Croft et al., 2015). ......................... 33
Figure 10 (A) The general differences in reflected light between healthy and stressed crop taken
from (Sensor, n.d.). (B) Two input leaf reflectance into the SAIL model. Measurements were
taken from healthy and unhealthy crop on the same day. ............................................................. 34
Figure 11 (A) Landsat reflectance of a healthy, fertilized pixel and (B) Landsat reflectance of an
unhealthy, non-fertilized pixel compared to two SAIL outputs. .................................................. 39
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Figure 12 A schematic depiction of the bilinear interpolation process between the two LUTs .. 40
Figure 13 Temporal variations in mean LAI for a) Wheat; and b) Corn across a growing season
....................................................................................................................................................... 43
Figure 14 Temporal variations in mean leaf chlorophyll for a) Wheat; and b) Corn across a
growing season.............................................................................................................................. 44
Figure 15 The top performing spectral indices for (a) corn (b) wheat (c) both corn and wheat
when compared to measured chlorophyll. The first row shows results for GNDVI, the second for
GRg, third for EVI, and the last row shows results for SAVI. ..................................................... 50
Figure 16 LAI correlation to RSR................................................................................................ 51
Figure 17 LAI maps for wheat plots derived Landsat images using RSR ................................... 52
Figure 18 LAI maps for corn plots derived from Landsat images using RSR............................. 53
Figure 19 Modelled leaf chlorophyll content for wheat and corn combined as well as separately.
PROSPECT was evaluated for hyperspectral and Landsat bands. The first column shows
hyperspectral bands outputs while the second column shows Landsat simulation band outputs . 55
Figure 20 PROSPECT chlorophyll estimates compared to measured chlorophyll. The first row is
with a fixed N of 2.5 while the second row for hyperspectral and Landsat inputs respectively.
The second row is a fixed N of 3.0 for hyperspectral and Landsat inputs respectively. .............. 56
Figure 21 Modelled and measured leaf reflectance spectra for corn (CE1_01 & CE2_11) and
wheat (WE1_01 &WE1_02). ........................................................................................................ 57
Figure 22 Two-step inversion validation with the left showing leaf-level validation and the right
showing canopy-level validation. ................................................................................................. 58
Figure 23 Chlorophyll maps with two study plots centered in the middle of the image. The
circled area highlights areas with no fertilization. ........................................................................ 59
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Figure 24 Chlorophyll maps with two corn study plots centered in the middle of the image. The
circled area highlights areas with no fertilization. ........................................................................ 60
Figure 25 Photos the end of the growing season for wheat. The top of the canopy began to turn
brown. Photos were taken on July 11th, 2013. ............................................................................. 62
Figure 26 Photos at the beginning of the growing season for corn. Bare soil is visible due to low
LAI. Photos taken on June 4th, 2013 (left) and June 18th, 2013 (right)......................................... 62
Figure 27 Reflectance spectra from the beginning of the growing season for corn and end of the
growing season for wheat ............................................................................................................. 63
1
Chapter 1 Introduction & Background
1 Introduction & Objectives
The terrestrial biosphere presents a major challenge in modelling its carbon flux and is arguably
the most variable component of the global carbon cycle (Asner et al., 1998; Curran, 1994).
Several aspects of the terrestrial biosphere are not yet well understood and cannot be effectively
quantified. Chlorophyll provides important information about the physiological status of plants
and is the main control for photosynthetic activity (Delegido, Vergara, Verrelst, Gandía, &
Moreno, 2011). Photosynthetic activity is directly related to the carbon intake and release from
vegetation. Furthermore, monitoring vegetation health is an important concern for both crop
management and carbon modelling. Land surface parameters, including leaf area index (LAI)
and leaf chlorophyll content, are critical factors for understanding plant productivity. Leaf area
index is defined as one-half total leaf area over ground area and is important for quantifying the
structure of plant cover that affects radiation interception and productivity (Chen & Black,
1991).
Acting as a main indicator of vegetation health and stress, chlorophyll content is important to
quantify and model in order to have a comprehensive understanding of the carbon cycle. Remote
sensing is a tool capable of providing a global dataset that can be used for analysis. Satellite
imagery is an efficient means of collecting data that have large-scale applications. The ability of
using remote sensing to measure chlorophyll content in forests and croplands is a current topic
that is of interest to global carbon cycle research.
While there has been progress towards aspects of the terrestrial biosphere including global land
cover maps, LAI maps, and clumping index maps, no global land chlorophyll map exists (Cihlar
et al, 1998; Chen et al, 2005). Ocean chlorophyll maps have been developed at a global scale at
high temporal resolutions to improve primary production estimates (Antoine, Andre, and Morel,
1996). Global ocean chlorophyll maps are created using the amount and location of
phytoplankton and are easily accessible by NASA’s Earth Observation (NASA,
http://earthobservatory.nasa.gov/GlobalMaps/). Current research is aimed to develop an
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algorithm to map land chlorophyll, and many regional maps for specific land cover types have
been created, yet there is no accepted global land chlorophyll map (Croft, Chen, Zhang, & Simic,
2013; Houborg & Boegh, 2008) With this end goal in mind, my project aims to provide input
into the overall creation of a global land chlorophyll mapping algorithm. In order to create a
global chlorophyll map, several vegetation types need to be considered. My thesis will focus on
contributing to the cropland aspect of chlorophyll mapping using remote sensing techniques. In
the end, I aim to create an algorithm to retrieve crop chlorophyll depending on LAI for the region
of Ontario. I focus on analyzing two types of crops within croplands: wheat and corn. These two
abundant crops in Ontario will be used within a two-step inversion process to retrieve
chlorophyll content.
1.1 Background
The sections below will provide brief background information on leaf optical properties, leaf
biophysical and biochemical properties, radiative transfer models (SAIL), and leaf-level radiative
transfer models (PROSPECT). All of this information is vital when modelling chlorophyll
content.
1.1.1 Leaf Optical Properties
The spectral signature of a leaf follows a general trend in the visible and near infrared spectrum
of wavelengths. When a leaf intercepts a light beam, it can be partly reflected, transmitted or
absorbed (Jacquemond & Ustin, 2008). The sum of reflectance R, transmittance T, and
absorbance A equals one:
𝑹 + 𝑻 + 𝑨 = 𝟏 Eq 1
The reflectance pattern of a healthy crop can be seen in Figure 1. The highest amount of
reflectance is found in the near infrared. In general, plants will absorb high amounts of blue and
red light (400nm to 500 nm and 600 to 700nm) and will reflect a relatively high amount of green
light (500nm to 600 nm). Thus, in the visible spectrum, healthy leaves normally appear green.
The absorption of blue and red radiation by leaves are both controlled by chlorophyll a and
chlorophyll b pigments.
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Figure 1 A typical leaf reflectance curve of green vegetation adapted from Hoffer (1978)
Leaf pigments, including chlorophyll a and b, are the main influence in the visible (400-700nm)
range, while cell structure controls the NIR plateau (700-1200nm) and water content controls the
1300-2500 range (Gates, Keegan, Schleter, & Weidner, 1965). For healthy vegetation, the
variations in the reflectance spectrum picked up by the sensor in the blue and red areas is because
of the absorption in the chloroplast. The chloroplast is the main control of photosynthetic activity,
with other key controls including stomata and fluorescence. Insight into chlorophyll content will
give insight into the health of the plant (Vogelmann, Bornman, & Yates, 1996).
1.1.2 Plant biophysical and biochemical parameters
For the convenience of description, I separate leaf-level parameters into biophysical and
biochemical parameters. Plant biophysical parameters play a vital role in studies involving
agricultural resource management, specifically for determining crop yield and crop performance
(McVicar & Jupp, 1998; Gower et al., 1999). Processes such as photosynthesis and transpiration
are influenced by biophysical and biochemical parameters. Biophysical parameters include leaf
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area index (LAI), biomass, Net Primary Productivity (NPP), and Fraction of Absorbed
Photosynthetically Active Radiation (fAPAR) while biochemical parameters include chlorophyll,
water content, lignin, cellulose and cellulose (Murray & William, 1987). LAI is defined as the
ratio of the sum of the upper leaf surface to the surface of the ground (Chen & Black, 1991).
Biomass is the mass of living matter and NPP is a measurement of plant growth by quantifying
the amount of carbon absorbed by plants (He, Chen, Pisek, Schaaf, & Strahler, 2012).
Biophysical, as well as biochemical parameters influence the reflectance captured from remote
sensing data. Estimation of these parameters using spectral signatures is important for
understanding the health (or greenness) of a plant.
LAI is a key biophysical parameter, particularly for modelling carbon flux and water flux of
vegetation canopies (Chen, Rich, Gower, Norman, & Plummer, 1997). Mapping of LAI provides
an important input to radiative transfer models. There are two main methods for estimating LAI:
(1) directly, using measurements from the field or (2) indirectly, from remote sensing by using
models or spectral indices. Estimating LAI from remote sensing is dependent on the radiometric
information from the top of the canopy. View geometries and illumination angles influence the
reflectance captured by the satellite. Since LAI is a measure of leaf area per unit ground area, the
amount of sunlit leaf area versus shaded leaf area will alter the estimation of LAI (Chen &
Leblanc, 1997). To accommodate for view angles, radiative transfer models must incorporate
components of sunlit and shaded leaf and ground reflectance in canopy-level modelling (Chen &
Leblanc, 1997).
Chlorophyll is a biochemical parameter that is one of the main indicators of plant photosynthetic
activity. The amount of absorbed solar radiation in a leaf is a function of the pigment content.
Chlorophyll a and chlorophyll b are pigments that convert light energy to stored chemical energy
(Gates et al., 1965). Moreover, leaf nitrogen is integrated into chlorophyll measurements, and
thus, chlorophyll gives insight into nutrient status, plant stress, and plant development. As with
LAI, leaf chlorophyll can be estimated using two methods: (1) directly, using leaf extraction with
organic solvents or (2) indirectly, using leaf optical models or spectral indices.
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1.1.3 Canopy and leaf reflectance modelling
Inversion of Radiative Transfer Models is a method in which these biophysical and biochemical
parameters can be quantified. Two models are used side-by-side for the estimation of these
parameters. Biophysical parameters (such as LAI) are used as inputs into canopy level models,
while biochemical parameters (such as chlorophyll) are used as inputs into leaf level models. My
study will work with SAIL (a canopy-level model) and PROSPECT (a leaf-level model) in
correspondence with direct measurements for LAI and chlorophyll estimation.
Canopy structure is important for understanding the interplay of solar energy and vegetation.
Radiative transfer models simulate both radiation in the atmosphere and radiation interaction
with vegetation. They are made to analyze the interaction between solar radiation and plants. In
order to do so accurately, the canopy architecture needs to be incorporated in the model. Aspects
such as the clumping of leaves, the sunlight and shaded portion of canopies, and the distribution
of leaves and branches are vital to accurately model this interaction (Chen & LeBlanc, 1997).
Such models include 4Scale and SAIL and are examples of radiative transfer models that are
currently being used for vegetation studies. The SAIL model incorporates the thermal differences
between the sunlit and shaded components of the canopy based on the hotspot parameter, the leaf
inclination distribution function, and LAI ( Verhoef, Jia, Xiao, & Su, 2007).
Leaf optical models are important to the estimation of biological and structural parameters at the
leaf level. PROSPECT is a leaf optical property model that requires leaf biochemical parameters
as well a structural parameter and is based upon Baret & Fourty (1997)’s simplification of cells
within leaves. PROSPECT is based on Allen et al. (1969)’s plate model which represents a leaf
as a semi-transparent plate. The plate model was further extended to describe a leaf as compact
plate layers (N) to account for the air space in the mesophyll within the leaf (Allen et al., 1969).
In forward mode, PROSPECT uses inputs of N, chlorophyll a & b, dry matter content,
equivalent water thickness, and brown pigments content to simulate leaf reflectance and
transmittance. The inversion of this model is available for the purpose of estimating biochemical
parameters using leaf reflectance and transmittance.
Two models will be used in my proposed study – PROSPECT and SAIL. PROSPECT is a leaf-
optical property model, whereas SAIL is a canopy bidirectional model. These two models in
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combination are radiative transfer models that help to understand how plant canopies absorb and
reflect light and the influence of the biochemical characteristics of vegetation on this light
interaction (Jacquemond, 2006). SAIL is effective in quantifying canopy structure and is a large-
scale model used with remote sensing inputs. Comparatively, PROSPECT is a leaf-level model
that quantifies biochemical aspects of vegetation. Their combination, referred to as PROSAIL, is
useful for biochemical contents such as total chlorophyll, water and dry matter. Furthermore,
these biochemical properties are related back to the canopy architecture, such as the LAI, angle
distribution, clumping index, etc. These two models will be used in order to quantify crop
chlorophyll with consideration of canopy structure.
1.2 Objectives
The objective of this thesis research was to develop an accurate method of estimating crop
chlorophyll using remote sensing data within a physically-based modelling approach. More
specifically, the model approach would entail a two-step inversion method. The two-step
inversion method plays a critical role in creating a Canada wide chlorophyll map that can
effectively consider the influence of canopy structure. The crop types chosen in this research
were wheat and corn, two of the most predominant crop types in Ontario and Canada. Controlled
areas with no-nitrogen application were set up to analyze the biochemical and biophysical
differences in crop productivity based on nutrient availability.
The two-step model inversion involves canopy-level reflectance modelling and leaf-level
reflectance modelling. The canopy-level model allows for the quantification of architecture using
biophysical parameters including LAI and the leaf inclination angle. The leaf-level model allows
for the quantification of plant biochemical parameters, including chlorophyll. The two-step
inversion method can be verified with model validation using ground measurements and is
beneficial in finding areas for model improvement. The end goal was to retrieve leaf-level
chlorophyll using LAI as an indicator.
My research objectives were to:
(1) develop an accurate two-step inversion method of modelling crop chlorophyll using
remote sensing data
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(2) assess the ability of this method to model chlorophyll during different phenological
stages
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Chapter 2 Literature Review
2 Literature Review
While retrieval of chlorophyll content using remote sensing is an emerging topic, the majority of
studies focus on defining chlorophyll indices and ignore leaf and canopy structural parameters.
Furthermore, most studies focus on chlorophyll mapping of forests rather than croplands. The
following sections will discuss current and past work being conducted on chlorophyll mapping
using both spectral indices and radiative transfer models.
2.1 Remote sensing in vegetation
As discussed in section 1.1.1, spectral signatures explain how different objects on Earth reflect,
absorb, and transmit solar radiative energy. Remote sensing imagery captures the amount of
radiance reflected from land and water. The radiance is the amount of light the satellite captures
from the object and is measured in watt per steroradian per square meter. While the radiance is
what is directly measured from remote sensing instruments, reflectance is used in most remote
sensing studies. Reflectance is often expressed as a percentage and describes a ratio between the
amount of light leaving a target to the amount of light striking a target (irradiance) (Schott,
2007).
The amount of radiance a satellite sensor measures is affected by many aspects. Light becomes
either scattered in the atmosphere or absorbed by the atmosphere (Schott, 2007). View angles,
the solar zenith angle, and solar azimuth angle also impact the amount of radiance captured by
the sensor. Figure 2 depicts the angles and how they impact the amount of radiance captured.
When radiance is converted to reflectance, the angles need to be incorporated and atmospheric
correction needs to be conducted (Schott, 2007).
A study conducted by Ranson, Daughtry, Biehl, & Bauer (1985) looks at the bidirectional
reflectance of corn throughout the growing season. The effects of sun and view angles at various
leaf stages were observed. My study will be using Landsat-8 which is taken at nadir. Research by
Ranson et al. (1985) found that with low LAI values, and with photos taken at nadir, the solar
angle played a significant role in reflectance collected by the sensor. At large solar zenith angles,
9
there was less contrast between bare soil and vegetation because of shadows. Chapter 5 will
discuss the limitations of my study due to issues at low LAI and the inability of the sensor to
capture canopy reflectance.
Figure 2 An example of a satellite sensor capturing radiance from a target; Ɵ𝒔 represents the
solar zenith angle, Ɵ𝒗 represents the view angle, ɸ𝒔 represents the solar azimuth angle, and ɸ𝒗
represents the view azimuth angle. Adapted from Ranson, Daughtry, Biehl, & Bauer (1985).
2.2 Vegetation & chlorophyll indices
Vegetation indices are often dimensionless measures that aim to estimate characteristics of plants
based on spectral signatures. Most vegetation indices focus on creating equations based on the
near infrared (NIR) and red bands of the radiation spectrum. For healthy vegetation, the NIR and
red bands represent the lowest absorption points and highest absorption point, respectively.
Indices are simple equations that can be conducted through manipulating band reflectance in
attempts to quantify the amount of ground vegetation per pixel. Of vegetation indices, some of
the most well known in literature are the following:
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Table 1 Common vegetation indices based on the NIR and red bands
Index Equation Source
The Difference Vegetation Index 𝑅𝑁𝐼𝑅 − 𝑅𝑅 Jordan (1969)
Normalized Difference Vegetation Index (NDVI) 𝑅𝑁𝐼𝑅 − 𝑅𝑅
𝑅𝑁𝐼𝑅 + 𝑅𝑅
Simple Ratio Vegetation Index (SR) 𝑅𝑁𝐼𝑅
𝑅𝑅
Jordan (1969)
In recent years, many empirical indices have been developed to estimate chlorophyll content
both at the canopy level and leaf level (Bannari, Khurshid, Staenz, & Schwarz, 2006). Many of
these chlorophyll indices are used with hyperspectral data for different types of vegetation and
are based on absorption of pigments in the leaf at different bands. Examples of these chlorophyll
indices include: Simple Ratio Pigment Index (SRP), Normalized Difference Pigment Index
(NDPI), Normalized Pigment Chlorophyll Ratio Index (NPCI), and Chlorophyll Absorption in
Reflectance Index (CARI). There is an extensive amount of research put into accurate estimation
of leaf or canopy level chlorophyll.
Bannari et al. (2007), Lin et al. (2011), Haboudane et al. (2002), and Delegido et al. (2011) focus
on assessing current chlorophyll indices for croplands. Using reflectance values of certain
wavelengths, the chlorophyll indices aim to calculate the chlorophyll content using spectral data.
Bannari et al. (2007) focus on investigating 17 chlorophyll indices and chlorophyll
concentrations of wheat crop in Saskatchewan using lab measurements. The indices were
correlated to the chlorophyll content measurements. Lin et al. (2011) conducted a similar study
in China also to study wheat canopy. They analyzed 12 spectral indices – many of which were
studied by Bannari et al. (2007). The two studies, though with a similar end goal, produced
different results. Bannari et al. (2007) discovered that the normalized pigment chlorophyll ratio
index (NPCI) was best for chlorophyll estimation in wheat, while Lin et al. (2011) found that
transformed absorption in reflectance index and optimized soil adjusted index (TCARI/OSAVI)
as well as modified soil adjusted index (TCARI/MSAVI) was best to measure chlorophyll. These
two indices were studied by Bannari et al. (2007) as well but did not produce the best fit for their
case. Both studies failed to incorporate structural parameters of the crops, but rather just took
into account leaf optical reflectance patterns.
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Delegido et al. (2011)’s study also aimed to generate an index to remotely estimate the spatial
distribution of chlorophyll. Their study in Europe analyzed wheat, barley, corn, and sugar to a
Normalized Area over Reflectance Curve (NOAC). Delegido et al. (2007) argue previous indices
are limited to a few bands (two to five) and thus cannot detect the subtle variation in chlorophyll
absorption features. Moreover, with huge international cooperation, their project had a vast
amount of empirical data as well as satellite imagery. The study conducted by Delegido et al.
(2011) was to find leaf-level chlorophyll, yet many structural parameters were ignored.
While chlorophyll indices are simple, empirical methods to chlorophyll estimation, the
biophysical parameters of the crops are not integrated into the methods. With no structural
parameters, such as LAI or clumping, indices ignores vital information influencing the
chlorophyll-radiation interaction.
2.3 Recent studies with radiative transfer models
Radiative transfer models are a method in which both structural parameters and biochemical
parameters can be incorporated. The use of radiative transfer models to estimate canopy or leaf
level chlorophyll is a recent, less ventured topic. In recent years, some studies have been
emerging to generate an algorithm using model inversion – most of which have been based on
the models SAIL and PROSPECT or the combined model PROSAIL.
The following section will discuss radiative transfer models and recent works conducted with
remote sensing imagery. It will focus on the leaf-optical property model (PROSPECT), and the
bidirectional reflectance model (SAIL). Finally, it will discuss recent works with radiative
transfer models to retrieve chlorophyll in vegetation.
2.3.1 The PROSPECT model
As stated previously in section 1.1.3, the PROSPECT model is a radiative transfer model at the
leaf level. PROSPECT was one of the first models established to simulate hemispherical leaf
reflectance and transmittance (Jacquemound et al., 2006) . While other models have been
developed, PROSPECT still remains the most widely used and is also most validated. Using
inputs of biochemical properties of a leaf, it estimates leaf reflectance, transmittance, and
absorption (Jacquemond & Baret, 1990). In addition to biochemical parameters, PROSPECT has
12
a leaf structural parameter, N. Table 2 lists the inputs into PROSPECT and their corresponding
units.
Table 2 Inputs into the PROSPECT model
Symbol Quantity Units
Structural Parameter 𝑁 Number of leaf layers -
Biochemical Parameter
𝐶𝑎𝑏 Chlorophyll a+b concentration 𝜇𝑔 𝑐𝑚−2
𝐶𝑤 Equivalent water thickness 𝑐𝑚
𝐶𝑚 Dry matter content 𝑔 𝑐𝑚−2
𝐶𝑏 Brown pigments content -
The latest version of the model is PROSPECT-5, which succeeded PROSPECT-4 and came with
the advancement of being empirically invertible (Feret et al., 2008). Both the forward model and
invertible model are available and easily accessible to the research community. In the inverted
model, the input leaf reflectance, transmittance and in the specified domain is reconstructed.
Validation shows that the fitted reflectance and transmittance corresponds well to the input with
negligible biases, nevertheless improvements can be made with low chlorophyll leaves where the
fit is not as strong (Feret et al., 2008; Renzullo, Blanchfield, Guillermin, Powell, & Held, 2006).
In a grapevine stress study, Powell et al. (2006) found the modelled spectra to be an average of
13% of the measured one demonstrating the ability of PROSPECT’s model inversion to fit the
corresponding optical range.
PROSPECT is used for numerous chlorophyll studies on different types of vegetation including
broadleaf, needle leaf and agriculture. Seasonal variation of leaves in a deciduous forest in
Fontainbleau, France, shows an increase in chlorophyll in the beginning of the growing season, a
period of stability, and a drastic decline towards the end (Demarez, Duthoit, Baret, Weiss, &
Dedieu, 2008). Sunlit leaves contained a higher concentration of chlorophyll when compared to
shaded leaves, showing the importance of radiative transfer models to incorporate the two
scenerios. PROSPECT was able to capture the seasonal pattern of chlorophyll changes.
13
Studies on eucalyptus leaves show the sensitivity of chlorophyll in far-red wavelength (Datt,
1998), and Barry et al. (2009)’s research compared measured chlorophyll in eucalyptus leaves to
modelled ones in both PROSPECT-3 (an earlier version) and 4. Furthermore, their study
accessed the ability of the model to estimate chlorophyll in both juvenile and adult leaves. While
their work shows high R² values, particularly for adult leaves, the regression fell beneath the 1:1
line, showing an underestimation of chlorophyll by the model (Barry, Newnham, & Stone,
2009). Similarly, Croft et al. (2015)’s work showed an underestimation of chlorophyll by
PROSPECT in needle leaf forest, but with high correlation coefficients.
2.3.2 The SAIL model
At the canopy level, SAIL is one of the first canopy reflectance models (Verhoef, 1984). It is
effective in quantifying canopy reflectance using remote sensing inputs and canopy structure.
SAIL is based on Suit’s model which is founded on a set of four differential equations: (1)
diffuse incoming flux, (2) diffuse outgoing flux, (3) direct solar flux, and (4) flux with radiance
in the direction of remote sensing observation. Nine coefficients within the equations, including
the extinction coefficient and scattering coefficient, are calculated using the LAI and the leaf
inclination angle (Verhoef, 1984). In SAIL, canopy architecture is described by LAI and by the
Leaf Angle Distribution (LAD). Table 3 lists the inputs info the SAIL model, including both
remote sensing inputs and canopy structure inputs.
14
Table 3 Inputs into the SAIL model
Symbol Quantity Units
Structural Parameter
𝐿𝐴𝐼 Leaf area index -
𝐿𝐼𝐷𝐹 Leaf inclination distribution function -
𝑆𝐿 Hot spot parameter -
Remote sensing input
𝜌𝑠 Soil reflectance -
𝑆𝐾𝑌𝐿 Ratio of diffuse to total incident
radiation
-
Ɵ𝑠 Solar zenith angle deg
Ɵ𝑣 View zenith angle deg
𝜑𝑠𝑣
Relative azimuth angle deg
The LAD is characterized by the leaf azimuth and leaf inclination orientations (Liang, 2004a) as
depicted in Figure 3. The LAD is defined as the probability density of the distribution of the leaf
normal with respect to the upper hemisphere. The leaf inclination distribution function (LIDF) is
based on the leaf angles, is an input into SAIL, and has a significant impact on the outputs of
canopy reflectance, transmittance, and absorption. It describes the frequency distribution of leaf
angles in different directions. LAD distributions can be measured in using protractors with a
compass (Norman and Campell, 1989), a clinometer (Gratani and Ghia, 2002) and 3-D digitizers
(Sinoquet et al., 1998; Shibayma, 2004). These methods are laborious, specifically for crop
canopies where is it important to repeatedly measure the LAD throughout the growing season,
meaning the number of measured leaves are limited (Hosoi, Nakai, and Omasa, 2009)
15
Figure 3 A diagram showing the leaf inclination and azimuth angle for a single leaf.
Different models for this function have been based on classifying crops into morphological types
including spherical, planophile, erectophile, plagiophile, extremophile or uniform. The spherical
distribution describes a leaf inclination distribution that can be represented by the surface of a
sphere. It is a distribution such that if all the angles of the leaves in a canopy were maintained,
the leaves would cover a sphere uniformly. The planophile distribution is for canopies with
higher probability for leaves to lie more horizontally than the spherical distribution. The
erectophile describes canopies where leaves have a higher probability to lie more vertically
compared to the spherical distribution. Plagiophile are characterized by leaves that tend towards
the same angle, and extremophile are leaves with dominant tendencies in both horizontal and
16
vertical positions (de Wit, 1965). A uniform distribution is when all leaf inclinations are equally
as likely (Eagelson, 2002). Of these, two of the most common classes are erectophile and
planophile. Figure 4 shows the cumulative frequency that a leaf inclination angle will be
planophile, erectophile, or spherical.
Figure 4 Three LIDF curves available in SAIL. LIDF show the cumulative frequency of leaves
at different angles. Adapted from Verhoef et al. (2007)
Different studies categorize wheat and corn as either planophile or erectophile with no agreed
upon consensus. Jacquemoud et al. (2000) categorizes both wheat and corn as erectophile while
Lang et al. (1985) categorize wheat as erectophile and corn as planophile. Wang et al. (1995)
state that corn is planophile and conduct an experiment to alter its type to erectophile, suggesting
that it can be grown in either orientation. Yanli et al. (2007) chose to classify wheat as both
erectophile and planophile based on visualization. Bernard et a. (2012) tested differences in
classifying wheat as either erectophile or planophile on leaf temperature but found no significant
17
difference. Moreover, their work hypothesized leaf morphology varied in a greenhouse
compared to wheat grown outdoors. In a study conducted by Hosoi et al. (2009), mean leaf
inclination angles for wheat were between 44.3 to 56.1 degrees throughout the growing season.
At the beginning of the growing season, the majority of the leaves were inclined upwards
(erectophile distribution). However, at the stem elongation stage, while some leaves grew
upwards, most bent downward to near horizontal positions. Moreover, at the flowering stage,
almost all the leaves bent downwards to horizontal positions (planophile distribution) (Hosoi et
al, 2009). Measurements of leaf inclination were not taken in this study due to the labourious and
repeated nature of the work. Based on visual inspection in the field, both corn and wheat will be
classified as planophile.
Goal & Thompson (1984) worked to validate the SAIL model using soybean canopy reflectance.
Their study showed the accuracy of SAIL to estimate canopy reflectance in the near infrared
region. Accuracy and sensitivity analysis for corn concluded that SAIL simulated canopy
reflectance well in the red and near infrared regions. Furthermore, the model could be invertible
using seasonal data and constant values for leaf angle distribution for the entire growing season
(Major, Schaalje, Wiegand, & Blad, 1992). While SAIL can be used for temporal variability
studies, crop reflectance from SAIL has high errors when the soil reflectance is strong. This is
particularly true for low LAI at the beginning of the growing season. Additionally, soil moisture
creates large differences in reflectance data even with the same soil type (Duke & Guérif, 1998).
Results from Su, Ransom, & Kanemasu (1997)’s study suggests that SAIL is not only able to
predict canopy reflectance during the growing season, but also after harvesting. Their work on
wheat crop residue shows a strong correlation between measured and simulated reflectance.
2.3.3 Past studies on radiative transfer models to estimate chlorophyll
PROSPECT and SAIL are two of the most widely used models to study crop and plant
biochemical parameters; nevertheless, other models, such as 4SCALE, are also used. The use of
models to estimate chlorophyll is a more recent topic that has begun to emerge in literature.
One study conducted by Hunt et al. (2011) uses SAIL and PROSPECT when analyzing
chlorophyll indices, and thus better incorporates canopy architecture. Nevertheless, the end goal
was to validate an index rather than generate an algorithm. Though these spectral indices provide
18
efficient and sensitive measurements of chlorophyll, they are developed and studied for certain
purposes. As the size, shape, surface, and internal structure of leaves may vary from species to
species, the indices cannot be applied to other vegetation types or biomes. Thus, these physical
variations are likely the cause of previous studies finding different indices to be more accurate
(Zhang et al., 2008).
While there have been attempts to study leaf chlorophyll content retrieval from hyperspectral
remote sensing imagery, much of the work has been forforestry. Zhang et al. (2008), as well as
Croft et al. (2013), work towards modelling chlorophyll content for forest health. Both studies
examine canopy biochemical composition as well as canopy structure. Zhang et al. (2008)
attempted to extrapolate leaf-level relationships to canopy-level. The two models used in the
study are 4SCALE and PROSPECT. While their study is successful in retrieving leaf
chlorophyll content for open and closed forests, their algorithm cannot be applied to cropland
systems.
Few studies have worked toward using a physically-based radiative transfer process to estimate
crop chlorophyll. Houborg & Boegh (2008) and Houborg et al. (2009) are exceptions, with
agriculture being the focus of their work. In their study, the canopy reflectance model ACRM
was used in combination with PROSPECT. Based out of Denmark, their work was focused on
barley, wheat, and maize and the use of the biophysical parameter of LAI for chlorophyll
estimation.
The linking of PROSPECT and SAIL has led to the model called PROSAIL. Jacquemound et al.
(1995) tested the use of PROSAIL for sugar beet biochemical properties. Using AVIRIS
hyperspectral data and LANDSAT TM bands, they found the two yielded similar results.
PROSAIL is the most widely used models for biochemical properties of leaves as it provides a
one-step inversion process. It has been used by Darvishzadeh et al. (2008) for grassland
chlorophyll estimation, by Kang et al. (2015) for barley, and by Feng et al. (2015) for wheat.
While all studies using PROSAIL demonstrate acceptable results, a two-step model inversion
method using SAIL and PROSPECT separately would allow for improved model validation. A
two-step model inversion has not been conducted for crops, making this research unique and a
new contribution to cropland analysis. In comparison to a one-step approach, areas where there
19
is model error, or room for model improvements, could be found and corrected for in a two-step
inversion method.
20
Chapter 3 Methods
3 Methods
3.1 Introduction
Both empirical data and model estimations were used to gather LAI and chlorophyll information.
For analysis of croplands, two techniques were used as model inputs: (i) ground-level sampling
(section 3.3) and (ii) data retrieval from satellite imagery (section 3.4).
SAIL and PROSPECT are two models that were applied in an inversion procedure to retrieving
chlorophyll information from remote sensing data. A schematic diagram of the model inversion
process can be seen in Figure 5. The gray boxes represent model inputs while the black boxes
represent outputs. The double arrows represent model validation. Both PROSPECT and SAIL
were validated to ensure accuracy and the process is discussed in section 3.6 and 3.7,
respectively. Validation with ground measurements and satellite imagery allowed for
adjustments in PROSPECT and SAIL models to be made. Finally, using a look-up-table (LUT)
created with SAIL, leaf reflectance was used in an improved PROSPECT model to retrieve
chlorophyll content.
21
Figure 5 Model inversion process. The gray boxes represent inputs while the black boxes represent outputs. The dashed lines represent
the inversion process and the double arrow lines represent comparison to empirical data. This model flowchart is modified from a past
study conducted by Zhang et al. (2008)
22
3.2 Study sites and background
Four field sites were used in this analysis. The sites included were two corn fields and two wheat
fields which were located in Perth County, Ontario. Perth County is a highly productive
agricultural area with 90% of the land being classified as prime agricultural land (Reid, Smit,
Caldwell, & Belliveau, 2006). With the majority of Perth County being located on the Stratford
Till Plain, the soils in the area are mostly clay and silty loams and have good natural fertility
(Schwan & Elliott, 2010). Within each of these fields were several sites for which data were
obtained. There were a total of 13 sites for corn (seven within one field and six in another) and
15 sites for wheat (eight in one field and seven in another). Most study sites were at least 30
meters apart with distances varying from 30 meters to 86 meters apart from the nearest site.
Study sites CE2_08 and CE2_01 were 22 meters apart, and CE2_08 and CE2_02 were 25 meters
apart; however, each site was located in a different Landsat pixel. Additionally, within each of
the study sites, there was a control area where zero nitrogen was applied. For the full level of
nitrogen (N), 105 kg N ha-1 in the form of 28% urea-ammonium nitrate was applied for the
winter wheat, and 134 kg N ha-1 was applied for the maize.
Table 4 Four study sites used to study chlorophyll content. Zero N sites refer to sites where no
fertilizer was applied while N sites refer to sites that received fertilizer.
Field ID Location Crop type Zero N sites N sites
WE1 43°29'33" N 80°54'23" W
Wheat
WE1-01 WE1-36 WE1-38
WE1-02 WE1-18 WE1-26 WE1-50
WE2 43°24'35" N 80°48'43" W
Wheat
WE2-19 WE2-50 WE2-55
WE2-01 WE2-09 WE2-20 WE2-52 WE2-54
CE1 43°27'40" N 80°48'53" W
Corn
CE1-01 CE1-19 CE1-21
CE1-02 CE1-05 CE1-06 CE1-07
CE2 43°27'40" N 80°48'10" W
Corn
CE2-01 CE2-02 CE2-08 CE2-04 CE2-05 CE2-11
23
Figure 6 Images of the two wheat plots (WE1 & WE2) are shown in the top two images while
images of the corn plots (CE1 &CE2) are seen in the bottom image.
24
Chlorophyll content is highly dependent on nitrogen availability and thus will help provide a
dataset with variations in nitrogen. Wheat data were collected between May and August while
corn content was measured between June and September. Images of the four field plots taken
from Google Earth show the sites that data were collected for. The controlled areas with zero
nitrogen application are indicated on the images. The images are from September 4th, 2013. At
the time of the images, wheat had already been plowed and harvested, while corn was nearing
the end of the growing season. The zero nitrogen application areas are easy to distinguish for
plots WE1, WE2, and CE1.
Within each site, effective LAI, chlorophyll content, and a leaf reflectance spectra were
collected. Full canopy reflectance was measured once over the course of the growing season.
Table 5 provides an overview of the data collected.
Table 5 Summary of field data collected between May and September. Wheat (W) data was
collected between May and July while corn (C) was collected between June and September.
May 8 May 24 June 5 June 18 June 26 July 11 July 22 July 26
Aug 7th
Aug 16 Sept 5 Sept 9 Sept 19
Leaf reflectance Missing W W & C W & C W & C W & C W & C - C C C C C
Canopy photos - - - - - - - W &
C - - - - -
LAI 2000 W Missing W & C W & C W & C W & C W & C - C C C C C
Leaf chlorophyll W W W & C W & C W & C W & C W & C - C C C C C
3.3 Field data collection
3.3.1 Ground-based effective LAI measurements
Effective LAI measurements were taken by using the LAI-2000 (Licor, 2010). This device uses
measurements of solar radiation above the canopy in reference to measurements below the
canopy to calculate the leaf cover (Chen, Rich, Gower, Norman, & Plummer, 1997). The LAI-
200 calculates eLAI by using a fish-eye optical sensor with a 148˚ field of view. Incoming
25
irradiance between 320nm to 490 nm is computed over five ranges of zenith angles. Random
spatial distribution of leaves is assumed in the integration process (Chen & Black, 1991; Chen et
al., 1997). To reduce the instruments field of view, a 90˚ view cap was used. The purpose of the
cap was to mask the operator from the instrument. Most measurements in the field were taken in
uniform sky conditions with cloud cover. Overcast sky acts as optimal conditions for LAI-2000
where diffuse radiation is ideal (Chen et al., 1997). Two reference measurements at the
beginning of each measurement were taken above the canopy in an open area at each site. After
the reference was taken, nine below canopy measurements were taken perpendicular to the crop
rows to get eLAI at each location. The radiation intercepted by the canopy is found my dividing
the above-canopy detector outputs by the below canopy detector outputs (Licor, 2010).
3.3.2 Measurement of crop biophysical parameters
In the lab, chlorophyll content was calculated by measuring different variables. First, small discs
were clipped from the leaves and the disc area (cm²) was recorded. The moisture content of each
leaf was found. This was done by obtaining the fresh weight of the leaf (g). After a period of 24
hours in an oven at 75 ˚C, the leaf was reweighed for dry weight (g). Using the difference
between these two values, the water content was calculated.
In order to extract chlorophyll from the leaf, a 4 ml solution of dimethylformamide (DMF) was
added in a vial with a small disc from the leaf (Minocha, Martinez, Lyons, & Long, 2009). After
being placed in dark conditions at 4˚C, the chlorophyll from the disc was extracted from the leaf
into the solution of DMF. Placing the extracted solution in a cuvette, a Spectrophotometer was
then used to collect absorbance values. The Spectrophotometer is a double-beam UV-1700 with
a 20 W halogen lamp, deuterium lamp, and Silicone photodiode detector. It has a wavelength
range of 300-1100 nm with a sampling interval of 1nm and an accuracy of -/+ 0.3nm
(PharmaSpec, Shimadzu). Absorbance at wavelengths of 663.8 nm, 646.8nm, and 480nm was
collected. Using the absorbance values, chlorophyll a (𝐶𝑎), chlorophyll b (𝐶𝑏) and total
carotenoids could be calculated. The calculations are seen below:
26
𝐶𝑎 = (12𝐴663.8 − 3.11𝐴646.8)(𝑊𝑑) Eq 2
𝐶𝑏 = (20.78𝐴646.8 − 4.88𝐴663.8)(𝑊𝑑) Eq 3
𝐶𝑥+𝑐 = (100𝐴480 − 1.12𝐶𝑎 − 34.09𝐶𝑏)(𝑊𝑑) Eq 4
where 𝐴 represents the absorbance at difference wavelengths, and 𝑊𝑑 represents the dry weight
of the leaf (Richardson, Duigan, & Berlyn, 2002; Wellburn, 1994).
3.3.3 Leaf reflectance and transmittance measurements
An ASD spectroradiometer Fieldspec Pro FR attached to a Li-Cor 1800 integrating sphere was
used to acquire reflectance and transmittance data (Licor, 1989). The spectral sampling range of
the spectroradiometer is 350nm to 2500nm.
From each site, leaves were taken from the upper crop canopy. These leaves where then
transported back to the University of Toronto in plastic bags and were kept in dark conditions at
a temperature near 0˚C. The ASD was turned on for 90 minutes prior to sampling to allow the
device to warm up. A white reference panel and a dark measurement were taken before sampling
leaf reflectance. These measurements removed the influence of electrical noise and allowed for
more accurate reflectance measurements. Next, a leaf was placed within the ASD by being
placed in the sample port and the reflectance spectrum of each leaf was calculated as
𝑅(λ) = leaf radiance(λ)
reflectance standard radiance(λ). Eq 5
The position of the light source was moved to behind the leaf in order to measure leaf
transmittance in a similar method.
Unexpected issues arose from the measurements of the ASD spectroradiometer. Most incidents
had issues with the integrating sphere. As stated in Chapter 1,
𝑅 + 𝑇 + 𝐴 = 1 Eq 6
From Figure 7(A), it is noticeable that the transmittance and reflectance equal to a value greater
than 1. Thus, corrections needed to be done to modify the values. It was assumed that the error
27
lay in the transmittance measurements because of the structure of the sphere itself. Of the 215
samples taken from the beginning to the end of the growing season, 78% of them encountered
this issue. The inverse model of PROSPECT aims to fit a reflectance and transmittance curves to
the measured curves. The measured values can be seen as the solid line in Figure 7 while the
fitted curves are seen as the dashed lines. PROSPECT adequately aimed to remove the
transmittance error produced by the integrating sphere.
Figure 7 CE1_07 on June 18. Measured Cab was 42.31. The image on the left demonstrates the
errors with the integrating sphere, and the image on the right shows the correction.
Using PROSPECT’s reflectance and transmittance curves, absorption could be calculated. From
this absorption, a new transmittance was found at each wavelength using the following formula:
𝑇𝑁𝐸𝑊𝜆 = 1 − 𝑅𝑜𝑏𝑠𝜆 − 𝐴𝜆 Eq 7
where Tnew is the new transmittance, Robs is the measured reflectance and A is the absorption at
each wavelength. The correction was done for the near infrared range from 730nm to 1500nm
3.4 Remote sensing data
Remote sensing imagery provides the means to collect reflectance values of the canopy. Landsat-
8 (OLI) is provided by the USGS and is readily accessible to the public (USGS,
A B
28
http://landsat.usgs.gov/landsat8.php). It has an overpass time of 15 days. While not having the
advantage of a red edge band, Landsat-8 had cloud-free images throughout the growing season.
Landsat-8 is composed of the Operational Land Imager (OLI) and the Thermal Infrared Sensor
(TIRS). Landsat-8 is different than its predecessors in that it provides three new bands: a deep
blue band used for coastal or aerosol studies, a shortwave infrared band used for cirrus
detections, and a quality assessment band (USGS, http://landsat.usgs.gov/landsat8.php).
Additionally, all Landsat-8 images come both in reflectance or radiance products, wherein the
reflectance product has been atmospherically corrected. It has a 30 m spatial resolution which
will suffice for this study as all field collected points were greater than 30m apart. Landsat-8
Surface Reflectance data were downloaded from Earth Explorer (USGS,
http://earthexplorer.usgs.gov/). These images were atmospherically and geometrically corrected
by the USGS using the 6S model and are ready for user application Table 6 demonstrates the
dates available from Landsat-8 as well the field dates of collection to be compared. The solar
zenith (𝜃𝑠) and solar azimuth (𝜑𝑠) are included for the dates. Landsat-8 (OLI) is fixed for nadir
view and the view zenith angle always remains smaller than 7.5 degrees and the influence of its
variation across the scene on canopy radiation modeling is assumed to be negligible (USGS,
http://landsat.usgs.gov/landsat8.php).
Table 6 Summary of Landsat imagery, and the field dates of collection to be compared. The
solar zenith (𝜃𝑠) and solar azimuth (𝜑𝑠) are included for the dates.
Field Collection Date
Landsat-8 (OLI) Acquisition Date
𝜽𝒔 𝝋𝒔
May 24 - - -
June 5 June 4 25.49 137.70
June 18 June 20 25.12 135.04
June 26 - - -
July 11 July 15 27.35 135.68
July 22 - - -
August 7 - - -
August 16 August 16 34.00 144.08
September 5 September 8 40.20 151.80
September 19 September 17 43.78 154.60
September 29 September 24 49.19 156.58
29
3.5 Chlorophyll indices to retrieve chlorophyll
Spectral indices are a simple, straightforward method that can be applied to estimate chlorophyll
content (Croft, Chen, & Zhang, 2014). While this study will focus on inverting physical-based
models, there has been a significant effort in literature to improve the relationship between
chlorophyll content and spectral indices (see section 2.2). Leaf optical properties, or more
specifically reflectance values, can be an indicator of crop health. By understanding different
spectral behaviour and absorption features of pigments, it is possible to gain insight on
chlorophyll within a crop canopy. Index-based studies focus on using the relationship between
laboratory-measured chlorophyll concentrations and observed spectral reflectance (Haboudane,
Tremblay, Miller, & Vigneault, 2008). Indices require little expertise, minimal software
knowledge, and are computationally fast.
Since chlorophyll a and chlorophyll b appear in two absorption peaks, several indices have been
proposed to gain chlorophyll content using these ranges. In chlorophyll a, the peaks occur at
430nm and 662 nm. Similarly, 453nm and 642nm peaks help quantify chlorophyll b (Delegido et
al., 2011). By combing the low reflectance and high reflectance ranges, ratios and calculations of
indices are proposed. Using the high and low values puts emphasis on the variable of interest by
maximizing sensitivity (Daughtry, Walthall, Kim, De Colstoun, & McMurtrey, 2000). Satellite
imagery that includes a narrow red-edge band, and hyperspectral imagery have proven to show
strong linear correlations with empirical chlorophyll measurements (Daughtry et al., 2000). This
study is limited by the use of Landsat, of a broad range sensor, which limits both the number of
indices that can be calculated, and the accuracy of the calculation. Nevertheless, indices based on
near infrared, red, green, blue, and coastal reflectance values were used for calculation of some
indices. A total of 16 vegetation and chlorophyll indices were tested in this study.
30
Table 7 A list of tested spectral indices in this study. The formula contains the calculations
conducted with Landsat-8 bands.
Index Name Formula Reference
BGI Blue green pigment index 𝑅𝐵/𝑅𝐺 Zarco-Tejada et al. ( 2005)
BI Brightness Index 𝑅𝑁𝐼𝑅 + 𝑅𝑅 + 𝑅𝐺
√3
Liu & Moore (1990)
DVI Difference Vegetation Index 𝑅𝑁𝐼𝑅 − 𝑅𝑅 Jordan (1969)
EVI Two band enhanced vegetation index
2.5(𝑅𝑁𝐼𝑅 − 𝑅𝑅)
𝑅𝑁𝐼𝑅 + 2.4𝑅𝑅 + 1
G Greenness Index 𝑅𝐺/𝑅𝑅 Zarco-Tejada et al. ( 2005)
GNDVI Green NDVI 𝑅𝑁𝐼𝑅 − 𝑅𝐺
𝑅𝑁𝐼𝑅 + 𝑅𝐺
Smith et al. (1995)
GRg Gitelson ratio green 𝑅𝑁𝐼𝑅/𝑅𝐺 − 1 Gitelson et al. (2003)
MCARI1 Modified chlorophyll absorption 1
1.2[2.5(𝑅𝑁𝐼𝑅
− 𝑅𝑅) − 1.3(𝑅𝑁𝐼𝑅 − 𝑅𝐺)] Daughtry et al. (2000) or Habourdane et al. (2004)
NDVI Normalized difference vegetation Index
(𝑅𝑁𝐼𝑅 − 𝑅𝑅)/(𝑅𝑁𝐼𝑅 + 𝑅𝑅) Rouse et al. (1974)
NPCI Normalized pigment chlorophyll index
(𝑅𝑅 − 𝑅𝐶)/(𝑅𝑅 + 𝑅𝐶) Penuelas et al. (1995)
OSAVI Optimized soil-adjusted vegetation index
1.16(𝑅𝑁𝐼𝑅 − 𝑅𝑅)
𝑅𝑁𝐼𝑅 + 𝑅𝑅 + 0.16
Rondeau et al. (1996)
RNDVI Renormalized difference vegetation index
𝑅𝑁𝐼𝑅 − 𝑅𝐶
√𝑅𝑁𝐼𝑅 + 𝑅𝐶
Rougean and Breon (1995)
SAVI Soil-adjusted vegetation index 1.5(𝑅𝑁𝐼𝑅
− 𝑅𝑅)
𝑅𝑁𝐼𝑅 + 𝑅𝑅 + 0.5
Huete (1988)
SIPI Structure intensive pigment index [680]
𝑅𝑁𝐼𝑅 − 𝑅𝐵
𝑅𝑁𝐼𝑅 − 𝑅𝑅
Penuelas et al. (1995)
SR Simple Ratio 𝑅𝑁𝐼𝑅/𝑅𝑅 Jordan (1969)
SPRI Simple ratio pigment index 𝑅𝐶/𝑅𝑅 Penuelas et al. (1995)
31
3.6 PROSPECT Model validation
To compare modelled outputs to measured values, the inverted version of PROSPECT was used.
In inverse mode, PROSPECT uses the inputs of reflectance and transmittance and estimates five
leaf biophysical properties: the leaf structure parameter (N), chlorophyll content (Cab) in μg/cm²,
the carotenoids content (Car) in μg/cm², brown pigments content (Cbrown), equivalent water
thickness (Cw) in g/cm² and the dry matter content (Cm) in g/cm². In inverse mode, the model is
able to measure the chlorophyll pigment concentration from leaf reflectance and transmittance
data.
Using field data collected from four plots in Stratford, corn and wheat reflectance and
transmittance data was inputted into PROSPECT. Eleven collection dates of data were used
throughout the growing season (May to September).
3.6.1 Landsat simulation and hyperspectral spectral comparison
PROSPECT was run twice: (1) using hyperspectral bands measured from the ASD
spectroradiometer and (2) using Landsat-8 simulated bands as inputs. With less spectral detail,
Landsat-8 will be used for the inversion process. At the leaf level, PROSPECT must be able to
perform adequately with Landsat bands for the overall model performance to work. The spectral
response function provided by the USGS describes how the sensor responds to different
wavelengths. The spectral response function in Equation 8 was used to simulate Landsat bands
using the measured hyperspectral data.
𝐿 = ∑ 𝛽(𝜆)𝐿′(𝜆)𝑁
𝜆=1
∑ 𝛽(𝜆)𝑁𝜆=1
Eq 8
In the weighted sum formula above, 𝐿 is the reflectance in the larger bandwidth range and 𝐿′(𝜆)
is the reflectance in the smaller, wavelength. 𝛽(𝜆) is the weight of the spectral response function
of the particular sensor (Chen et al., 2002; Croft, Chen, Zhang, & Simic, 2013). By running the
model using Landsat-8 simulated bands, the performance of PROSPECT in hyperspectral and
broadband could be compared.
32
Figure 8 Example of a hyperspectral curve from leaf measurements and simulated Landsat-8
bands that were used as PROSPECT inputs.
3.6.2 Fixing the leaf structural parameter
PROSPECT’s inputs include the leaf structure parameter, N, which quantifies the number of
compact layers. In particular, N, is an average number of air/cell wall interfaces in the mesophyll
and is the main influencer of leaf optical properties (Feret et al., 2008; Jacquemoud et al., 2009).
N is assumed to be wavelength independent and changes from leaf to leaf. It cannot be measured,
yet the first step within PROSPECT is the determination of N (Feret et al., 2008). A sensitivity
analysis conducted by Jacquemoud et al. (2009) reveals that N alters the output reflectance from
the model. The internal structure of the leaf controls both reflectance and transmittance along the
spectrum, but the influence is most evident in the NIR range when absorption is low
(Jacquemoud & Baret, 1990). A study conducted by Croft et al. (2015) examined the structural
parameter’s influence on reflectance through forward runs of PROSPECT. The study was done
through forward runs using both hyperspectral and Landsat bands and the results can be seen in
Figure 9.
33
Figure 9 PROSPECT-4 simulations for hyperspectral (left) and Landsat (right) bands by using
fixed input variables except for N, the structural parameter (Croft et al., 2015).
In general, a higher N value generates a higher reflectance curve. The red edge is particularly
important for chlorophyll and LAI calculations, as the position of the inflection point is used in
inverse modelling for chlorophyll estimation (Jacquemoud et al., 2009). As seen in Equations 2-
4, weighted differences between wavelengths reflectance values of 663.8nm and 646.8nm are
used to calculate chlorophyll. This implies a direct relationship where a larger difference (such as
in a higher N value) generates a larger chlorophyll value within the model.
Since PROSPECT tends to under predict chlorophyll values, this study aimed to see the impact
of N on generating more accurate estimations. Using Landsat-8 simulated bands as inputs,
PROSPECT was run for a fixed N parameter of 2.5 and 3.0.
3.7 Two-step model inversion using SAIL and PROSPECT
The objective of model inversion is to be able to estimate leaf chlorophyll content based on LAI
and satellite imagery. There are two methods of inversion: 1) the Look-up Table approach in
which forward runs of a model with differing parameters are repeated; or 2) based on empirical
relationships with the variables of the model (Peddle, Johnson, Cihlar, & Latifovic, 2004; Weiss,
Baret, Myneni, Pragnére, & Knyazikhin, 2000). PROSPECT has been inverted through the
second technique, whereby a minimizing merit function is used (Feret et al., 2008). A major
issue with the second inversion technique is the complexity of many models and variables,
making them non-invertible, as is the case with SAIL. The LUT approach is a simple and
effective method to overcome these issues (Weiss et al., 2000). PROSAIL has been used to
create LUT in the past, where a set of reflectance values is generated from varying parameter
34
values (Jacquemoud et al., 2009). The table, containing both the input parameters and the canopy
reflectance values, is then used to find the solution to the problem. The modelled reflectance is
compared to the measured reflectance to find the closest match, often with the smallest RMSE
value, or through a minimizing function (Jacquemoud et al., 2009; Weiss et al., 2000).
While the inversion of PROSAIL is common in the literature to provide estimations of Cab, LAI,
and fAPAR, it does not allow for model evaluation or model validation. In order to validate the
inversion through the LUT approach, a two-step process was created whereby canopy and leaf
level reflectance could be compared to measured values. The process involved the generation of
canopy reflectance through SAIL, and the calculation of leaf reflectance to be used in
PROSPECT. The following sections will discuss the creation of the LUTs, using the LUTs to
calculate leaf reflectance, and the use of the leaf reflectance in the inverted version of
PROSPECT.
3.7.1 Creating a LUT using altering LAI and SZA
To create a LUT, inputs of leaf and soil reflectance were needed. Two LUTs were created using
different input reflectance data into SAIL: (1) unhealthy crop (2) healthy crop. The soil
background reflectance was not measured during field work, and was simply considered to be
known. Agricultural soil inputs that were also used as inputs intPROSPECT inversion was set to
be the soil reflectance in SAIL for consistency. The two LUTs were created in order to
accommodate for crop with no nitrogen application. Differences in leaf reflectance spectra
between a healthy and unhealthy crop can be seen in Figure 10.
Figure 10 Two input leaf reflectance into the SAIL model. Measurements were taken from
healthy and unhealthy crop on the same day.
35
A partial inversion was conducted whereby certain parameters were set to a fixed value and the
LAI and the solar zenith angle were incremented. Table 8 lists the inputs into SAIL. Since
Landsat is taking at NADIR (or 7.5 degrees within NADIR), the viewing angle can be set to 0.
With a fixed viewing angle, the solar azimuth angle will not influence the canopy reflectance and
could also be set to 0. LAI, hotspot and leaf orientation are not independent of each other, so
they are not inferable unless the others are known (Jacquemoud, Baret, Andrieu, Danson, &
Jaggard, 1995). LAI is one of the most important variables into the model for determining
growth and yield, and by setting fixed values for the other two variables, LAI could be better
estimated (Jacquemoud et al., 1995). In SAIL, the hotspot parameter quantifies the ratio between
leaf size and canopy height (Jacquemoud et al., 1995). The hotspot scalar falls between the
values of 0-1 and generally, an increase in the hotspot size increased the canopy reflectance
(Jacquemoud et al., 1995). While it was not measured in the field, the hotspot can be set to a
constant value of 0.5. This is based on an average corn leaf which grows to be about 120 cm in
length and 250 cm in height, making the length to height ratio ~0.5. Wheat grows to around 120
cm high on average and has leaf length of 50 cm long. The leaf type was set to planophile and
the soil reflectance factor was set to 0, implying it was wet soil. With 0 as the soil reflectance
factor, the background reflectance input was a dark soil background. Similarly, LAI was set to
increment from a minimum value of 0.1 to a maximum value of 10 with 0.1 increments. The
solar zenith angle was set to increment between 0˚ and 60˚ with increments of 10˚.
36
Table 8 Inputs in the SAIL model to create a LUT. Two LUTs were created with the same
parameters but with different inputs of leaf reflectance; one healthy and one unhealthy leaf.
Symbol Quantity Units Fixed Value Incrementing Step
Factor
𝐿𝐴𝐼 Leaf area index - - 0.1
𝐿𝐼𝐷𝐹 Leaf inclination distribution
function
- Planophile
𝑆𝐿 Hot spot parameter - 0.5
𝜌𝑠 Soil reflectance factor (0 is wet
and 1 is dry)
- 0
Ɵ𝑠 Solar zenith angle deg - 10 ̊
Ɵ𝑣 View zenith angle deg 0
𝜑𝑠𝑣
Relative azimuth angle deg 0
3.7.2 Modelling leaf reflectance using the SAIL canopy model
Chlorophyll content was modelled by linking a canopy radiative transfer model (SAIL) to a leaf
radiative transfer model (PROSPECT). While SAIL simulates canopy reflectance, leaf
reflectance is needed as an input in the PROSPECT model. To derive leaf level reflectance, the
LUT was structured to calculate leaf reflectance (see Appendix 2 for a portion of the LUT). The
LUT contained the ratio between the input leaf reflectance into SAIL, and the output canopy
reflectance (see Equation 9). Based on the canopy reflectance in each pixel from an image, the
ratio could then be used to find leaf reflectance. The modelled leaf reflectance was found by
multiplying the pixel’s canopy reflectance by the ratio within the LUT.
37
R𝑎𝑡𝑖𝑜(𝜆) =𝑅𝑒𝑓𝐿𝑒𝑎𝑓(𝜆)
𝑅𝑒𝑓𝐶𝑎𝑛𝑜𝑝𝑦(𝜆) Eq 9
3.7.3 Estimating LAI using RSR
In order to use the LUT, LAI is needed for each pixel in the Landsat image. In Chen et al.
(2002)’s paper, they aim to create a Canada-wide leaf area index map using Landsat imagery.
Their study measured LAI for different land cover types across Canada. In their work, a new
vegetation index called reduced simple ratio (RSR) was developed using the red, NIR and
shortwave NIR (SWIR). RSR is a modification of SR. Its formula can be seen in Equation 10.
𝑅𝑆𝑅 =𝑅𝑁𝐼𝑅
𝑅𝑅(1 −
𝑅𝑆𝑊𝐼𝑅−𝑅𝑆𝑊𝐼𝑅𝑚𝑖𝑛
𝑅𝑆𝑊𝐼𝑅𝑚𝑎𝑥−𝑅𝑆𝑊𝐼𝑅𝑚𝑖𝑛) Eq 10
where 𝑅𝑁𝐼𝑅, 𝑅𝑅, and 𝑅𝑆𝑊𝐼𝑅 are the reflectance in the NIR, red, and SWIR respectively. 𝑅𝑆𝑊𝐼𝑅𝑚𝑖𝑛
and 𝑅𝑆𝑊𝐼𝑅𝑚𝑎𝑥 are the minimum and maximum SWIR reflectance found in the image. These are
found from the 1% boundary in the histogram of the SWIR band. According to Chen et al.
(2002), RSR is more advantageous than SR for estimating LAI because it helps improve the
accuracy of LAI retrieval for mixed land cover types, and the background influence is
suppressed with the use of the SWIR band. The SWIR band captures the amount of vegetation
containing water (Chen et al., 2002).
While Chen et al. (2002) validated crop LAI using RSR, their work was only focused on mid-
summer maps and did not capture all of the growing season. Moreover, limited ground
measurements did not allow for accurate LAI empirical formulas. My study used RSR on
Landsat-8 images, and validated the results with measured LAI values to formulate an equation
that could be applied to the image to create an LAI map. Results from the application of RSR can
be seen in section 4.3.
3.7.4 SAIL modelled canopy reflectance
The SAIL models canopy reflectance using inputs parameters stated in section 2.3.2. The canopy
reflectance from SAIL would then be used to calculate leaf reflectance by multiplying the ratio
in the LUT to the satellite image. To test the accuracy of the SAIL model, it was validated
against the Landsat-8 reflectance.
38
Since two LUTs were created, the Landsat-8 canopy reflectance was compared to both LUTs
corresponding reflectance values. Figure 11 provides examples of two sites – one fertilized with
nitrogen and one not fertilized with nitrogen. The graphs are from the same date and adjacent
sites. In Figure 11 (A), the pixel from the Landsat image represents a site with nitrogen
application. The healthy canopy reflectance output from SAIL almost perfectly matches to that
of the satellite image while the match from the unhealthy LUT overestimates in the green and red
band. Figure 11 (B) represents a site with no nitrogen application. In this example, the healthy
LUT match underestimates the green and red bands. The unhealthy LUT provides a closer match
in these bands.
The red edge is most vital for chlorophyll estimation. To more accurately be capable of
predicting the red band regardless of nitrogen and no nitrogen application areas, an inverse
distance weighting was applied per pixel. The procedure for inverse distance weighting is
provided in the following section.
39
Figure 11 (A) Landsat-8 reflectance of a healthy, fertilized pixel and (B) Landsat-8 reflectance
of an unhealthy, non-fertilized pixel compared to two SAIL outputs.
40
3.7.5 Bilinear Interpolation and Inverse Distance Weighting
Application of the LUT was done through a set of two methods; first with bilinear interpolation
and next by an inverse distance weighting.
With bilinear interpolation, each pixel in the Landsat-8 image was compared to the two LUT
(healthy and unhealthy) to find the closest solar zenith angle and LAI match. Since LAI
incremented by 0.1 and SZA incremented by 10 degrees, the nearest two LUT entries were taken.
Bilinear interpolation was applied to each table to interpolate the appropriate reflectance and ratio
values. Figure 12 shows a graphic approach to this process.
Figure 12 A schematic depiction of the bilinear interpolation process between the two LUTs
𝑅𝑒𝑓ℎ𝑒𝑎𝑙𝑡ℎ𝑦(λ) and 𝑅𝑒𝑓𝑢𝑛ℎ𝑒𝑎𝑙𝑡ℎ𝑦(λ) are the reflectance found from the match in the healthy and
unhealthy LUT respectively, and 𝑅𝑎𝑡𝑖𝑜ℎ𝑒𝑎𝑙𝑡ℎ𝑦(λ) and 𝑅𝑎𝑡𝑖𝑜𝑢𝑛ℎ𝑒𝑎𝑙𝑡ℎ𝑦(λ) are the ratios found from
the match in the healthy and unhealthy LUT respectively.
Once the bilinear interpolation was completed, an inverse distance weighting (IDW) was applied
to extrapolate an appropriate ratio value between the healthy and unhealthy LUT match. IDW is
based on the concept that nearer points are more similar than further points. By this theory, if a
Landsat pixel’s reflectance was more similar to that of the unhealthy LUT match, it would apply
a greater influence to the ratio values that would be used to calculate leaf reflectance. The
41
opposite was true, in which if the canopy reflectance from Landsat, the healthy LUT match
would have a greater influence. Equation 11 states the IDW equation used to calculate the ratio.
𝑅𝑎𝑡𝑖𝑜 (λ) =
𝑅𝑎𝑡𝑖𝑜ℎ𝑒𝑎𝑙𝑡ℎ𝑦(λ)
𝑑ℎ𝑒𝑎𝑙𝑡ℎ𝑦+
𝑅𝑎𝑡𝑖𝑜𝑢𝑛ℎ𝑒𝑎𝑙𝑡ℎ𝑦(λ)
𝑑𝑢𝑛ℎ𝑒𝑎𝑙𝑡ℎ𝑦1
𝑑ℎ𝑒𝑎𝑙𝑡ℎ𝑦+
1
𝑑𝑢𝑛ℎ𝑒𝑎𝑙𝑡ℎ𝑦
Eq 11
where 𝑅𝑎𝑡𝑖𝑜 (λ) is the ratio found for the particular pixel, 𝑅𝑎𝑡𝑖𝑜ℎ𝑒𝑎𝑙𝑡ℎ𝑦(λ) and 𝑅𝑎𝑡𝑖𝑜𝑢𝑛ℎ𝑒𝑎𝑙𝑡ℎ𝑦(λ)
are the ratios found from the match in the healthy and unhealthy LUT respectively, and
𝑑ℎ𝑒𝑎𝑙𝑡ℎ𝑦 = [𝑅𝑒𝑓ℎ𝑒𝑎𝑙𝑡ℎ𝑦
(λ) − 𝐿𝑎𝑛𝑑𝑠𝑎𝑡𝑅𝑒𝑓(λ)]2 Eq 12
𝑑𝑢𝑛ℎ𝑒𝑎𝑙𝑡ℎ𝑦 = [𝑅𝑒𝑓𝑢𝑛ℎ𝑒𝑎𝑙𝑡ℎ𝑦(λ) − 𝐿𝑎𝑛𝑑𝑠𝑎𝑡𝑅𝑒𝑓(λ)]2 Eq 13
The canopy reflectance from the Landsat-8 scene was compared to the LUT healthy and
unhealthy match. Based on the distance (or difference) between the healthy and unhealthy and
the Landsat-8 image, a weighting was applied on the ratio.
3.7.6 Using modelled leaf reflectance to calculate transmittance
PROSPECT’s inputs include leaf reflectance and transmittance. The previous sections outline the
algorithm by which leaf reflectance was found using the SAIL LUT. While leaf transmittance
does not play as vital a role in chlorophyll modelling, it is used to calculate absorption at
different inflection points, including the red edge.
To calculate leaf transmittance, a ratio between measured transmittance and measured
reflectance was found. The average ratio in each of the five bands (deep blue, blue, green, red
and NIR) was then used to model transmittance based on modelled reflectance.
3.8 Summary of Methods
Using SAIL and PROSPECT, a two-step model inversion method was created. In the first step,
canopy reflectance was modelled using SAIL. A LUT table was created by varying the LAI and
solar zenith angle. Using bilinear interpolation and inversion distance weighting, an appropriate
42
reflectance match was found for each pixel in a Landsat-8 scene. In the second step, the canopy
reflectance was converted to leaf level reflectance. The leaf reflectance was then run through
PROSPECT to retrieve leaf chlorophyll.
Throughout this process, both SAIL and PROSPECT were validated against field measurements.
Field data were collected for wheat and corn during the growing season of 2013. Improvements
were made in PROSPECT and in SAIL to more adequately estimate chlorophyll in agriculture.
43
Chapter 4 Results
4 Results
4.1 Seasonal trends in LAI and chlorophyll
Patterns in LAI can be seen in Figure 13 and patterns in chlorophyll can be seen in Figure 14.
Mean values were calculated for all the fertilized and non-fertilized sites within each field and
were plotted. Within the graphs there are multiple trends lines. WE1 represents the first plot for
wheat, and WE2 represents the second. The same is true for corn (CE1 is the first plot, CE2 is
the plot). Within each site, there is a further division of N and zero N, where N refers to sites
with nitrogen application within the site and zero N refers to sites with no nitrogen application
areas. It can be noticed that areas with zero nitrogen had both lower chlorophyll values and lower
LAI values. Variation in LAI throughout the growing season for non-fertilized and fertilized
wheat and corn field were evidently different, as seen by Figure 13.
While at the beginning of the growing season, the values between the two plot types are
comparable, a clear separation does occur in the middle of the growing season. For wheat, the
fertilized fields had a maximum average LAI of 4.3 while the non-fertilized fields only reached a
maximum average of 1.6. In corn, the LAI stabilizes to a maximum quickly and remains stable
for some time during the middle of the growing season before dropping abruptly.
Figure 13 Temporal variations in mean LAI for a) Wheat; and b) Corn across a growing season.
The error bars are the standard deviation for each site based on the number of samples (refer to
Table 10).
44
Changes in leaf chlorophyll content across the growing season (Figure 14) also show differences
between nitrogen and no-nitrogen application areas. Similar to LAI, chlorophyll for both crop
types is comparable at the beginning of the growing season in fertilized and non-fertilized sites.
The drastic difference during the middle of the growing season is noticeable where fertilized
areas reach a higher value of leaf chlorophyll content than non-fertilized areas. For wheat, the
maximum is 64 𝜇/𝑐𝑚2 in fertilized plots, while it is only 45 𝜇/𝑐𝑚2 in non-fertilized plots. The
difference between CE1 and CE2 trends is also noticeable with fertilized areas reaching an
average maximum of 80 𝜇/𝑐𝑚2 and non-fertilized areas reaching an average maximum of
47 𝜇/𝑐𝑚2. Additionally, leaf chlorophyll in no nitrogen application areas declined sooner than
their corresponding nitrogen sites. Once again, it should be noted that in CE2, no nitrogen failed
to follow trends seen for other non-fertilized areas.
Figure 14 Temporal variations in mean leaf chlorophyll for a) Wheat; and b) Corn across a
growing season. The error bars are the standard deviation for each site based on the number of
samples (refer to Table 9).
Comparing wheat and corn, it can be noticed that wheat reaches maximum values of chlorophyll
and LAI earlier in the growing season than corn. This is expected because of differences in the
crop. Moreover, corn reaches a higher chlorophyll maximum in comparison to wheat. The
maximum chlorophyll for corn is around 80𝜇/𝑐𝑚2while for wheat it is only 64 𝜇/𝑐𝑚2. The
maximum LAI reached by both crops is similar, with wheat being slightly higher than corn.
A t-test was conducted to compare the nitrogen and no nitrogen application plots to determine
when chlorophyll and LAI were significantly different (p<0.05). Tables 9 and 10 show t-test
45
results for the various plots throughout the growing season. P-values < 0.05 are shown in bold. In
Table 9, the t-test was run for chlorophyll variations in the plots while Table 10 was run for
variation in LAI. WE1 shows no significant difference in chlorophyll content between nitrogen
and no nitrogen plots on May 8, May 24, June 6, and June 18, and July 22. However, in the mid-
growing season, on June 26 and July 11, there is a significant difference. The second wheat plot,
WE2, shows significant difference for chlorophyll content between the nitrogen and no nitrogen
sites for all days except May 8 and July 22. For LAI content, both wheat sites showed significant
differences between the nitrogen and no nitrogen plots for the entire growing season (with the
exception of May 8, where not enough data were collected to conduct a t-test). For the corn plot,
CE1, chlorophyll content between the fertilized and non-fertilized plots showed no significant
difference at the beginning of the growing season. However, from the middle to the end of the
growing season, from July 22 to September 29, there was a significant difference between the
control sites. Results for LAI in corn was found to be more scattered, with only the dates on June
18, July 11, and September 29 having a significant difference between the control plots. This was
visible in the field as well, as corn leaves tended to appear greener, but the LAI were not visually
different between the control plots. Refer to Table 9 and Table 10 for specific p-values.
46
Table 9 Differences between chlorophyll in the study plots throughout the growing season. P-
values<0.05, shown in bold, show statistically significant differences between the nitrogen and
no nitrogen areas.
Plot Mean
Standard Deviation
Number of
Observations pValue
WE1 May 8 , 2013 No Nitrogen 33.51 2.48 3 Nitrogen 36.28 4.88 3 0.45
May 24, 2013 No Nitrogen 34.37 5.10 3 Nitrogen 39.91 13.40 3 0.56
June 6, 2013 No Nitrogen 36.13 1.72 3 Nitrogen 51.76 9.63 4 0.04
June 18, 2013 No Nitrogen 40.87 4.41 3 Nitrogen 58.12 11.26 4 0.05
June 26, 2013 No Nitrogen 40.72 2.66 3 Nitrogen 59.88 10.56 4 0.03
July 11, 2013 No Nitrogen 30.08 6.34 3 Nitrogen 55.93 3.41 4 0.01
July 22, 2013 No Nitrogen 4.93 7.43 3 Nitrogen 17.44 6.12 4 0.08
WE2 May 8 , 2013 No Nitrogen 25.39 6.18 2 Nitrogen 33.68 7.58 3 0.28
May 24, 2013 No Nitrogen 25.84 7.36 3 Nitrogen 51.16 1.57 3 0.02
June 6, 2013 No Nitrogen 36.75 3.33 3 Nitrogen 62.01 9.84 5 0.00
June 18, 2013 No Nitrogen 45.03 1.23 3 Nitrogen 60.95 10.99 5 0.03
June 26, 2013 No Nitrogen 39.39 7.70 3 Nitrogen 63.53 14.48 5 0.02
July 11, 2013 No Nitrogen 18.49 3.00 3 Nitrogen 59.89 7.33 5 0.00
July 22, 2013 No Nitrogen 0.50 0.09 3 Nitrogen 20.30 20.39 5 0.07
CE1 June 6, 2013 No Nitrogen 12.60 1.77 3 Nitrogen 21.00 7.26 4 0.10
June 18, 2013 No Nitrogen 36.01 8.17 3 Nitrogen 33.20 7.75 4 0.67
June 26, 2014 No Nitrogen 52.46 7.25 3 Nitrogen 63.40 4.55 4 0.10
July 11, 2013 No Nitrogen 47.32 17.39 3 Nitrogen 79.64 11.71 4 0.06
July 22, 2013 No Nitrogen 40.88 4.82 3 Nitrogen 66.33 15.51 4 0.04
August 7, 2013 No Nitrogen 27.79 1.40 3 Nitrogen 62.40 13.11 4 0.01
August 16, 2013 No Nitrogen 27.78 6.16 3 Nitrogen 69.44 6.48 4 0.00
September 5, 2013 No Nitrogen 21.11 1.64 3 Nitrogen 67.87 7.20 4 0.00
September 9, 2013 No Nitrogen 14.40 5.44 3 Nitrogen 44.50 17.51 4 0.04
September 29, 2013 No Nitrogen 7.95 3.24 3 Nitrogen 44.73 14.00 4 0.01
47
Table 10 Differences between LAI in the study plots throughout the growing season. P-
values<0.05, shown in bold, show statistically significant differences between the nitrogen and
no nitrogen areas.
Plot Mean
Standard Deviation
Number of
Observations pValue
WE1 May 8 , 2013 No Nitrogen 0.26 0.09 3 Nitrogen 0.68 - 1 -
June 6, 2013 No Nitrogen 1.28 0.12 3 Nitrogen 3.06 0.58 4 0.01
June 18, 2013 No Nitrogen 1.60 0.09 3 Nitrogen 3.92 0.31 4 0.00
June 26, 2013 No Nitrogen 1.59 0.19 3 Nitrogen 4.29 0.34 4 0.00
July 11, 2013 No Nitrogen 1.44 0.14 3 Nitrogen 3.45 0.36 4 0.00
July 22, 2013 No Nitrogen 0.79 0.59 3 Nitrogen 3.00 0.49 4 0.01
WE2 May 8 , 2013 No Nitrogen 0.68 - 1 Nitrogen 0.38 0.04 3 -
June 6, 2013 No Nitrogen 1.39 0.23 3 Nitrogen 2.89 0.12 5 0.00
June 18, 2013 No Nitrogen 1.38 0.10 3 Nitrogen 3.20 0.27 5 0.00
June 26, 2013 No Nitrogen 1.41 0.12 3 Nitrogen 3.61 0.24 5 0.00
July 11, 2013 No Nitrogen 0.94 0.58 3 Nitrogen 2.79 0.31 5 0.02
July 22, 2013 No Nitrogen 1.18 0.40 3 Nitrogen 2.63 0.11 5 0.02
CE1 June 6, 2013 No Nitrogen 0.08 0.03 3 Nitrogen 0.13 0.02 4 0.11
June 18, 2013 No Nitrogen 0.31 0.04 3 Nitrogen 0.22 0.05 4 0.03
June 26, 2014 No Nitrogen 0.80 0.11 3 Nitrogen 0.82 0.12 4 0.80
July 11, 2013 No Nitrogen 1.94 0.32 3 Nitrogen 2.65 0.23 4 0.04
July 22, 2013 No Nitrogen 2.89 0.44 3 Nitrogen 3.61 0.37 4 0.08
August 7, 2013 No Nitrogen 3.19 0.11 3 Nitrogen 3.84 0.41 4 0.05
August 16, 2013 No Nitrogen 2.93 0.13 3 Nitrogen 3.56 0.75 4 0.19
September 5, 2013 No Nitrogen 2.27 0.18 3 Nitrogen 3.21 0.99 4 0.15
September 9, 2013 No Nitrogen 2.34 0.20 3 Nitrogen 2.85 0.59 4 0.18
September 29, 2013 No Nitrogen 1.56 0.19 3 Nitrogen 2.38 0.41 4 0.02
48
4.2 Chlorophyll indices to estimate crop chlorophyll
Table 9 shows the results from comparing the calculated indices to the measured chlorophyll.
The indices included both vegetation induces (such as NDVI) and well as chlorophyll indices
(such as GNDVI). With the limitation of not having the red edge band in Landsat-8, many
indices did not perform well for chlorophyll content. The best performing indices for corn were
GNDVI, GRg, EVI and SAVI with R² values of 0.67, 0.60, 0.56, and 0.56 respectively. For
wheat, many of the indices had similar R² values, ranging between 0.32 and 0.54. However, like
corn, GRg, SAVI, EVI and GNDVI were amongst the top performing indices with R² of 0.54,
0.49, 0.49, and 0.48 respectively. When combining the crop types, GNDVI outperformed all
other indices with GRg being a close second.
While vegetation indices are a straightforward way to estimate leaf chlorophyll content, the use
of the indices ignores canopy architecture and variables such as LAI (Croft et al., 2014;
Haboudane et al., 2008). Additionally, background reflectance from soil and understory
contributes to canopy reflectance, thus reflectance values do not only represent solar radiation
reflected off the leaves. Moreover, there is a lack of generality - the top performing indices for
wheat were not necessarily the same as for corn. The chlorophyll indices did not do well to
accurately predict chlorophyll content. Low R² values as well as the scatter in the graphs in
Figures 15 (a) (b) and (c) show the lack of relationship between chlorophyll and spectral indices.
49
Table 11 A summary of the results from spectral indices. Relationships between the index and
empirical chlorophyll measurements are displayed in the table. Linear regressions were used for
analysis.
Index Corn Wheat Corn and Wheat
R² Equation of Line of fit R² Equation of Line of fit R² Equation of Line of fit
BGI 0.21 187.90x -33.00 0.39 -127.94x + 114.18 0.03 58.02x + 24.08
BI 0.20 21.39x – 10.70 0.32 26.49x -13.63 0.17 17.62x + 2.56
DVI 0.41 13.26x + 0.83 0.45 10.68x + 18.27 0.37 11.43x + 10.22
EVI 0.55 53.38x – 32.05 0.49 29.78x + 8.17 0.49 44.74x - 17.08
G 0.23 33.88x – 0.33 0.40 27.29x + 12.71 0.24 31.44x + 4.85
GNDVI 0.67 197.23x – 101.9 0.48 97.71x – 22.43 0.59 164.01x – 75.02
GRg 0.60 5.43x + 7.63 0.54 3.48x + 26.66 0.56 4.83x + 13.83
MCARI1 0.38 8.39x + 3.45 0.45 6.88x + 19.52 0.34 7.27x + 12.19
NDVI 0.56 150.63x – 73.71 0.48 78.93x – 12.02 0.50 125.33x – 51.73
NPCI 0.49 -129.39x + 93.67 0.49 -338.20x + 152.19 0.45 -130.9x + 92.99
OSAVI 0.55 129.01x – 68.59 0.48 68.53x + 9.88 0.50 107.47x -47.45
RNDVI 0.42 1.72x - 52.30 0.46 1.09x – 7.36 0.34 1.34x – 26.98
SAVI 0.56 85.17x -35.73 0.49 46.12x + 6.99 0.50 71.14x + 20.11
SIPI 0.49 -224.92x + 287.31 0.44 -189.62x + 250.28 0.48 -219.09x + 281.18
SR 0.48 2.25x + 19.41 0.49 1.51x + 33.01 0.46 2.02x + 23.95
SPRI 0.47 116.54x – 8.94 0.48 281.09x – 100.79 0.44 119.19x – 11.12
50
Figure 15 The top performing spectral indices for (a) corn (b) wheat (c) both corn and wheat
when compared to measured chlorophyll. The first row shows results for GNDVI, the second for
GRg, third for EVI, and the last row shows results for SAVI.
51
4.3 LAI maps using RSR
The Landsat-8 calculated vegetation index, RSR, was graphed against measured LAI values for
that pixel. The regression can be seen in Figure 16 and has an R² of 0.69. From the inversion of
the linear function shown in Figure 16, a formula was derived for estimation of LAI for the
Landsat scene.
Figure 16 LAI correlation to RSR
Using the LAI formula from Equation 14, LAI maps with Landsat-8 images were produced. The
main issue of these LAI maps is that the algorithm was created for agricultural pixels, and may
not work well for areas of forest or roads around the farmland. Moreover, in the 30 m resolution,
many of the areas may be mixed in land cover. Nevertheless, the LAI algorithm works well for
the crop area, and differences between no nitrogen and nitrogen application areas could be
differentiated. In Figure 17 and Figure 18, LAI maps for wheat and corn are shown. The white
dots represent field collection points and the black circles highlight areas where there was no
nitrogen application. These areas show lower LAI values than their surrounding area.
52
𝐿𝐴𝐼 =𝑅𝑆𝑅+0.56
4.10 Eq 14
Figure 17 LAI maps for wheat plots derived Landsat images using RSR
53
Figure 18 LAI maps for corn plots derived from Landsat images using RSR
54
4.4 Model Validation
4.4.1 PROSPECT Model Validation
Validation of PROSPECT showed similar results between hyperspectral band inputs and
Landsat-8 simulated bands. As seen in Figure 19, both displayed strong linear relationships
between modelled chlorophyll and measured chlorophyll, with hyperspectral results having a
slightly stronger relationship. The Landsat chlorophyll demonstrated a strong relationship with
hyperspectral chlorophyll (R²=0.96), exhibiting that Landsat is capable of estimating chlorophyll
comparable to hyperspectral input.
Overall, PROSPECT performed well for corn and wheat combined, with high R² values and low
RMSE values. For corn and wheat combined, with hyperspectral inputs, PROSPECT estimated
chlorophyll content with an R² of 0.79, and an R² of 0.78 with the Landsat simulated bands. The
two values are similar with negligible differences. The patterns exhibited in two runs
(hyperspectral and Landsat bands) of PROSPECT show similar patterns. Visually, the regression
lines between the hyperspectral outputs and the Landsat outputs are similar in slope and in y-
intercept.
Despite the high R² values, PROSPECT continuously under predicted chlorophyll values with
the regression falling beneath the 1:1 line. For wheat, the under prediction is more evident, with
PROSPECT performing worse for higher chlorophyll values. When being run, the N parameter
that is automatically determined within the model ranged between 0.3 and 2.9.
55
Figure 19 Modelled leaf chlorophyll content for wheat and corn combined as well as separately.
PROSPECT was evaluated for hyperspectral and Landsat bands. The first column shows
hyperspectral bands outputs while the second column shows Landsat simulation band outputs
56
To accommodate for the under prediction of PROSPECT, a fixed N parameter was used on
Landsat bands. The N parameter to set values of 2.5 and 3.0. Croft et al. (2015) conducted a
similar analysis on needle leaf forests to compare hyperspectral chlorophyll to Landsat
chlorophyll, and demonstrated a strong relationship between the two (R²=0.97). Figure 20 shows
the systematic increase of the fixed structure parameter and the difference in the modelled
outputs. The fixed N of 2.5 does not aid with the under-prediction of chlorophyll; nevertheless,
an N of 3.0 aided with bringing wheat estimations closer to the 1:1 line for both corn and wheat
crop.
Figure 20 PROSPECT chlorophyll estimates compared to measured chlorophyll. The first row is
with a fixed N of 2.5 while the second row for hyperspectral and Landsat inputs respectively.
The second row is a fixed N of 3.0 for hyperspectral and Landsat inputs respectively.
57
4.4.2 SAIL Model Validation
The use of the SAIL model to retrieve leaf reflectance values was validated against measured
leaf reflectance. Figure 21 demonstrates the correspondence between the modelled and measured
leaf reflectance spectra. There is close correspondence with ground measurements for both the
visible and near infrared bands. The leaf spectrum was well modelled for both the corn and
wheat sites. Additionally, the strong correlation between the modelled and measured spectra can
be noticed in both the no-nitrogen plots and the nitrogen plots. Leaf reflectance was well
modelled for a range of image acquisition dates.
July 11, CE1_01, No Nitrogen July 11, CE2_11, Nitrogen
June 5, WE1_01, No Nitrogen June 5, WE1_02, Nitrogen
Figure 21 Modelled and measured leaf reflectance spectra for corn (CE1_01 & CE2_11) and wheat (WE1_01
&WE1_02).
58
4.5 Model Inversion
The leaf chlorophyll content retrieval algorithm was applied to the study areas as well as the
surrounding area. First, the leaf-level modelled chlorophyll content was compared to the
measured chlorophyll content to test the reliability of the process. Additionally, canopy-level
chlorophyll was validated by multiplying the modelled chlorophyll content and the measured
chlorophyll content by LAI. The validation of the two-step inversion can be seen in Figure 22,
where Figure 22 (A) shows the leaf-level validation and Figure 22 (B) shows the canopy-level
validation. Both show high R² and low RMSE values. The validation for the canopy level shows
higher R² values than that of the leaf level. This is likely due LAI, which corrects for areas of
under predication and over-prediction seen at the leaf level.
The spatial variability of leaf chlorophyll can be seen in Figure 23 which was mapped using
Landsat-8 data. Chlorophyll estimates range between 4.2 and 92.4 μg/cm² at the leaf level. The
no nitrogen application areas in Figure 23 are highlighted by black circles. The no nitrogen areas
show spatial variability in chlorophyll with lower content than the surrounding plot.
While collection dates and available Landsat-8 imagery were limited for wheat plots, the corn
plots had more seasonal availability of data. The progression of images from July 15th to
Figure 22 Two-step inversion validation with the left showing leaf-level validation and the
right showing canopy-level validation.
59
September 24th shows the change from midseason to end of season for corn. The chlorophyll
declines in this mapped time series, with the non-fertilized areas declining earlier than the
fertilized areas.
WE1 - June 5, 2013 WE1 - June 18, 2013
WE2 - June 5, 2013 WE2 - June 18, 2013
Figure 23 Chlorophyll maps with two study plots centered in the middle of the image. The circled area
highlights areas with no fertilization.
60
CE1&2 - July 15, 2013 CE1&2 – August 16, 2013
CE1&2 – September 8, 2013 CE1&2 – September 17, 2013
CE1&2 – September 24, 2013
Figure 24 Chlorophyll maps with two corn study plots centered in the middle of the image. The circled
area highlights areas with no fertilization.
61
Chapter 5 Limitations
5 Limitations
Certain limitations on satellite imagery narrowed the extent of this study. First, Landsat-8
imagery was used but does not have the advantage of a red edge band, a main influence on
chlorophyll estimation. Over the course of the field collection, the RapidEye sensor was flown
over the field sites. RapidEye is a German Earth observation sensor under the satellite company
of BlackBridge (BlackBridge, http://blackbridge.com/rapideye/ ). The RapidEye sensor acquired
images over the study area every 10 days during the growing season for corn and wheat
canopies, but only five images were cloud free. Of these five, three overlapped within the range
of field data collect with the following dates: May 25, September 17 and September 28. While
RadpidEye is a high spatial resolution satellite and has the advantage of capturing a red edge
band, the images failed to cover most of the growing season. The available dates captured only
the beginning and end of the growing season when chlorophyll content was at a minimum. The
limitation of not having image acquisitions during the key growth stages meant RapidEye
imagery could not be used for this study.
With both Landsat and RapidEye, the beginning and end of the growing season provided a
challenge. Figure 25 shows images from the end of the growing season for wheat. The photos,
taken on July 11th, demonstrate the browning of the crop. While the top of the wheat canopy is
brown and has lost chlorophyll, the bottom layers were still green with chlorophyll ranging
between 26 μg/cm² to 70 μg/cm².
For corn, two dates at the beginning of the growing season had low LAI values. On the
collection dates of June 4 and June 18, LAI ranged between 0.07 and 0.41. As seen in the images
in Figure 26, the low LAI values result in a large amount of soil being visible. Nevertheless, the
leaves of the corn are green with chlorophyll ranging between 10 μg/cm² and 42 μg/cm².
62
Figure 25 Photos the end of the growing season for wheat. The top of the canopy began to turn brown.
Photos were taken on July 11th, 2013.
Figure 26 Photos at the beginning of the growing season for corn. Bare soil is visible due to low LAI.
Photos taken on June 4th, 2013 (left) and June 18th, 2013 (right).
63
In Figure 27, examples of reflectance curves on these dates are shown. All three curves show
high red reflectance values and the spectral signature more generally represents that of soil. In
the case of wheat, the view angle at nadir captures a view at the top of the canopy whereby it is
capturing only the brown pigments. In the case of corn, the large amount of bare soil visible is
captured by the sensor.
Figure 27 Reflectance spectra from the beginning of the growing season for corn and end of the
growing season for wheat
Ranson et al. (1985)’s study proves that the solar angle is vital for photos taken at nadir and with
low LAI values. The solar zenith angle for these dates ranged between 25 degrees and 27
degrees. Their study states that a larger solar zenith angle provides less contrast between the
green vegetation and the brown soil due to the influence of shadows. Despite the limitations in
the remote sensing data, the two-step model inversion was capable of estimating chlorophyll
content well, particularly for the mid-growing season.
64
Chapter 6 Discussion and Conclusion
6 Discussion and Conclusion
Leaf chlorophyll content is an important indicator of plant productivity. Field work collected
shows seasonal variations in leaf chlorophyll and indicates the most productive stages of crop
growth. Additionally, the areas of no nitrogen application demonstrate the influence of fertilizers
on crop yield and the difference between healthy and unhealthy crops. While vegetation and
chlorophyll indices are simple and quick methods for estimating chlorophyll, their results are
inconclusive and show weak relationships. No agreed upon index performs best between studies
and, without validation, their use is unreliable. In this research, the GNDVI chlorophyll index
showed an R²= 0.56. Without structural parameters, indices ignore the influence of background
reflectance, shaded leafs, sunlit leaves, and scattering. In comparison, the two-step inversion
algorithm formulated in this thesis includes leaf and canopy structure, and shows a stronger
validation result with an R²= 0.64 and can provide a reputable method to estimate leaf
chlorophyll.
The photosynthetic activity in a leaf is directly influenced by the leaf chlorophyll content. The
variation of leaf reflectance and transmittance in the visible and near infrared can be used to
estimate leaf level chlorophyll, and was done so using the PROSPECT inversion model.
Nevertheless, as demonstrated in Figure 19, PROSPECT consistently underestimates leaf
chlorophyll. To compensate for this, the leaf structural parameter N had to be fixed to a higher
value than the model estimation and a value of 3.0 was found to be the best for corn and wheat.
The influence of N on leaf reflectance can be seen in Figure 9 (Croft et al., 2015). The N
parameter represents the number of mesophyll air/cell interfaces and is difficult to measure
(Ollinger, 2011). It encompasses leaf structural effects due to leaf thickness, or number of leaf
layers which can be an oversimplification of the internal leaf structure (Ollinger, 2011). Similar
studies have fixed the N parameter for vegetation types or per leaf sample based on inferences
from measurements (Malenovsky et al., 2013; Zarco-Tejada et al., 2004a). In general, N
describes the way light interacts with all molecules in the leaf – it is a method of quantifying how
light scatters, refracts, and transmits with the leaf. A higher N value implies a more complex
internal structure and more interaction of light in the leaf molecular structure. While N was
65
artificially fixed for this study, further research needs to be conducted to understand the internal
leaf structure and its relationship to leaf pigments and to leaf reflectance.
Despite the small number of Landsat bands used for PROSPECT inversion simulations, and their
larger bandwidths, the predicted chlorophyll shows highly similar results to hyperspectral inputs.
Thenkabail et al. (2004) identify seven optimal bands (495nm, 55nm, 655m, 675 nm, 705m, 915,
and 985 nm) for vegetation studies and argue that data volume can be reduced by 97% for
vegetation studies when hyperspectral wavelengths are reduced. Vegetation studies may have
high redundancy of wavelength channels (Jacquemoud, Baret, Andrieu, Danson, & Jaggard,
1995).
At the canopy level, SAIL performs well at predicting canopy-level reflectance. Nevertheless,
the leaf reflectance input for SAIL is a major influence on the output of canopy reflectance. An
input healthy leaf spectrum does not represent well to an unhealthy crop. Similarly, an unhealthy
leaf spectrum does not represent well to a healthy crop. In order to compensate for this, healthy
and unhealthy leaf LUTs were created and inverse distance weighting was applied pixel by pixel
for the best match. The overall mapped leaf chlorophyll content at the study areas in Figure 6
shows meaningful spatial and temporal distributions of leaf chlorophyll content. Changes in the
growing season can be seen despite limits to the satellite data. Moreover, areas of no fertilization
are noticeable within the field. The results are effective in demonstrating the use of the inversion
method in monitoring crop health using multi-spectral remote sensing data.
6.1 Significance
This research demonstrates the use of data from Landsat-8 and other multispectral sensors to
predict leaf chlorophyll. More specifically, it demonstrates the ability of a process-based model
to adequately estimate leaf and canopy chlorophyll content. The research is significant in
understanding plant productivity, crop yield, and health of vegetation. Moreover, this work is an
integral part of an effort in mapping global terrestrial chlorophyll distributions that is being
conducted by colleagues. While there have been studies in the past aimed to map chlorophyll,
many rely on the use of chlorophyll indices. The physically-based modelling approach allows for
canopy structural parameters to be included, which are not encompassed in the use of indices.
Current work is being done to develop a two-step inversion method to produce a chlorophyll
66
map of Canada with different land cover types. This thesis assists in the agricultural aspect and
provides validation.
6.2 Future Research
With aims to develop a global chlorophyll map, future research needs to be conducted to develop
and validate methods for other land cover types. First and foremost, improvements to the
PROSPECT model need to be made. In PROSPECT, the structure parameter N is an
oversimplification of the internal structure of the leaf (Ollinger, 2011). Moreover, while N
cannot be measured, it is used to define the interaction of light within the leaf at a molecular leaf.
The structural parameter has not been measured. Future work in the setting of the N value needs
to be considered and the internal structure and thickness of a leaf needs to be understood.
Improvements to the two-step inversion technique could be made with the use of satellite data
with a red edge band. While Landsat-8 validated well in PROSPECT, it does not have the
advantage of a red edge band. The red edge is most vital for chlorophyll estimation as it dictates
the amount of absorption by chlorophyll pigments. In PROSPECT, the inflection point is used
for inverse modelling for chlorophyll estimation (Jacquemond et al., 2009). For future studies,
the use of satellite imagery with the red edge band, such as RapidEye, would be more optimal to
use. Concurrent use of satellite imagery with a red edge band and concurrent measurements of
hyperspectral data in the field at the canopy needs to be conducted for rigorous validation.
Finally, with an end goal of a global chlorophyll map, continuous field measurements of
chlorophyll and LAI need to be conducted for other land cover types. Field measurements need
to cover the range of the growing season for continued validation of the two-step inversion
algorithm created in this thesis.
67
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Appendix A
Photographs of corn taken on July 26. The photos is during mid-growing season for corn.
74
Photographs of wheat taken on July 26. The photos mark the end of the growing season for
wheat
75
Appendix B
A sample of the healthy LUT created by the two-step inversion algorithm
Reflectance (λ) Ratio= Leaf/Canopy (λ)
LAI sza 440 480 560 655 865 440 480 560 655 865
0.1 0 0.025818 0.02497 0.030085 0.036442 0.086711 2.2215 2.3982 3.2015 1.7163 4.7203
0.1 10 0.025652 0.02479 0.029832 0.036088 0.08587 2.2359 2.4156 3.2287 1.7332 4.7665
0.1 20 0.025551 0.024677 0.029665 0.035853 0.085359 2.2448 2.4266 3.2469 1.7445 4.795
0.1 30 0.025465 0.024582 0.029545 0.035652 0.085206 2.2523 2.436 3.26 1.7543 4.8036
0.1 40 0.025359 0.024472 0.029464 0.035427 0.085576 2.2617 2.4469 3.269 1.7655 4.7829
0.1 50 0.025208 0.024325 0.02943 0.035133 0.086747 2.2753 2.4618 3.2728 1.7803 4.7183
0.1 60 0.02501 0.024134 0.02946 0.034731 0.089157 2.2933 2.4812 3.2694 1.8009 4.5908
0.2 0 0.025495 0.024929 0.031454 0.036014 0.10097 2.2497 2.4021 3.0621 1.7367 4.0535
0.2 10 0.025173 0.024579 0.030958 0.035331 0.099289 2.2784 2.4363 3.1113 1.7703 4.1223
0.2 20 0.024976 0.024359 0.030632 0.03488 0.098286 2.2964 2.4583 3.1444 1.7932 4.1643
0.2 30 0.024811 0.024177 0.030399 0.034498 0.098003 2.3117 2.4768 3.1684 1.813 4.1764
0.2 40 0.02461 0.023968 0.030241 0.034076 0.098752 2.3305 2.4984 3.1849 1.8355 4.1447
0.2 50 0.024329 0.023692 0.030172 0.033533 0.10105 2.3575 2.5275 3.1922 1.8652 4.0505
0.2 60 0.023967 0.023342 0.030223 0.032806 0.10567 2.3931 2.5654 3.1869 1.9065 3.8733
0.3 0 0.025238 0.024925 0.032741 0.035655 0.11521 2.2725 2.4024 2.9417 1.7542 3.5527
0.3 10 0.024769 0.024414 0.03201 0.034666 0.11268 2.3156 2.4528 3.009 1.8043 3.6322
0.3 20 0.024482 0.024094 0.031534 0.034016 0.11121 2.3427 2.4853 3.0544 1.8388 3.6805
0.3 30 0.024245 0.023831 0.031195 0.03347 0.11081 2.3657 2.5128 3.0876 1.8687 3.6936
0.3 40 0.023959 0.023533 0.030964 0.032876 0.11194 2.3939 2.5446 3.1106 1.9025 3.6564
0.3 50 0.023566 0.023145 0.03086 0.032123 0.11531 2.4338 2.5872 3.1211 1.9471 3.5496
0.3 60 0.023069 0.022663 0.03092 0.031138 0.12195 2.4863 2.6423 3.115 2.0087 3.3564
0.4 0 0.025038 0.024952 0.033952 0.035355 0.12939 2.2907 2.3998 2.8369 1.7691 3.1632
0.4 10 0.024429 0.024288 0.032994 0.03408 0.12604 2.3478 2.4655 2.9192 1.8353 3.2475
0.4 20 0.02406 0.023875 0.032375 0.033248 0.1241 2.3839 2.5081 2.975 1.8812 3.2981
0.4 30 0.023755 0.023537 0.031936 0.032556 0.12361 2.4145 2.5442 3.0159 1.9212 3.3112
0.4 40 0.023393 0.023159 0.031637 0.031811 0.12511 2.4518 2.5857 3.0445 1.9662 3.2714
0.4 50 0.022903 0.022674 0.031496 0.030884 0.12949 2.5042 2.641 3.0581 2.0252 3.1609
0.4 60 0.022295 0.022081 0.031559 0.029693 0.13794 2.5725 2.712 3.052 2.1064 2.9672
0.5 0 0.024886 0.025004 0.03509 0.035105 0.14351 2.3047 2.3949 2.7449 1.7817 2.852
0.5 10 0.024146 0.024195 0.033916 0.033565 0.13932 2.3754 2.4749 2.8399 1.8634 2.9377
0.5 20 0.023698 0.023695 0.033161 0.032567 0.13695 2.4202 2.5272 2.9045 1.9206 2.9887
0.5 30 0.023331 0.023288 0.032628 0.031742 0.13637 2.4583 2.5714 2.952 1.9704 3.0013
0.5 40 0.022902 0.022838 0.032262 0.030867 0.13824 2.5044 2.622 2.9854 2.0263 2.9607
0.5 50 0.02233 0.022268 0.032085 0.029794 0.14356 2.5685 2.6891 3.0019 2.0993 2.8511
0.5 60 0.021631 0.021583 0.032144 0.028443 0.15363 2.6515 2.7745 2.9964 2.199 2.6642
76
A sample of the unhealthy LUT created by the two-step inversion algorithm
Reflectance (λ) Ratio= Leaf/Canopy (λ)
LAI sza 440 480 560 655 865 440 480 560 655 865
0.1 0 0.026066 0.025466 0.034158 0.037818 0.08695 2.3873 2.7266 5.3998 2.382 4.8075
0.1 10 0.025899 0.025283 0.033884 0.037457 0.086107 2.4027 2.7463 5.4434 2.4049 4.8546
0.1 20 0.025796 0.025166 0.033697 0.037217 0.085596 2.4123 2.7591 5.4737 2.4204 4.8836
0.1 30 0.025711 0.025074 0.033614 0.037039 0.085442 2.4203 2.7693 5.4871 2.4321 4.8924
0.1 40 0.025612 0.024984 0.033712 0.036897 0.085811 2.4296 2.7792 5.4711 2.4414 4.8713
0.1 50 0.025482 0.024887 0.034097 0.036787 0.086981 2.442 2.79 5.4094 2.4487 4.8058
0.1 60 0.02532 0.024785 0.034871 0.036719 0.089389 2.4576 2.8016 5.2893 2.4533 4.6763
0.2 0 0.025964 0.02587 0.039403 0.038645 0.10144 2.3967 2.684 4.681 2.331 4.1207
0.2 10 0.025639 0.025513 0.038852 0.037944 0.099754 2.4271 2.7216 4.7474 2.3741 4.1904
0.2 20 0.025438 0.025285 0.038481 0.037481 0.098748 2.4462 2.7461 4.7931 2.4034 4.2331
0.2 30 0.025274 0.025107 0.038317 0.037138 0.098462 2.4621 2.7656 4.8136 2.4255 4.2454
0.2 40 0.025088 0.024936 0.038498 0.036869 0.099207 2.4804 2.7846 4.791 2.4433 4.2135
0.2 50 0.024844 0.024752 0.039214 0.036665 0.1015 2.5047 2.8052 4.7036 2.4569 4.1184
0.2 60 0.024547 0.024562 0.040638 0.036545 0.10612 2.535 2.827 4.5387 2.4649 3.9392
0.3 0 0.025905 0.026266 0.044373 0.039433 0.1159 2.4021 2.6436 4.1567 2.2844 3.6067
0.3 10 0.02543 0.025743 0.043542 0.038409 0.11337 2.447 2.6973 4.236 2.3453 3.6873
0.3 20 0.025138 0.025411 0.042991 0.037738 0.11188 2.4755 2.7325 4.2903 2.387 3.7362
0.3 30 0.024901 0.025153 0.042747 0.037245 0.11148 2.499 2.7605 4.3148 2.4186 3.7496
0.3 40 0.024635 0.024907 0.042995 0.036861 0.1126 2.526 2.7878 4.2899 2.4438 3.7123
0.3 50 0.024293 0.024647 0.043992 0.036577 0.11596 2.5615 2.8172 4.1927 2.4628 3.6049
0.3 60 0.023883 0.024381 0.045955 0.036418 0.12258 2.6055 2.848 4.0136 2.4735 3.4101
0.4 0 0.025881 0.026653 0.049078 0.040182 0.1303 2.4044 2.6052 3.7582 2.2418 3.2081
0.4 10 0.025264 0.025971 0.047966 0.038853 0.12692 2.4631 2.6735 3.8453 2.3185 3.2934
0.4 20 0.024887 0.025542 0.047239 0.037989 0.12498 2.5004 2.7185 3.9045 2.3712 3.3447
0.4 30 0.024582 0.02521 0.046914 0.037358 0.12448 2.5314 2.7543 3.9315 2.4113 3.3582
0.4 40 0.024245 0.024895 0.047215 0.036872 0.12597 2.5666 2.7891 3.9065 2.4431 3.3184
0.4 50 0.023818 0.024566 0.048447 0.036518 0.13032 2.6126 2.8265 3.8071 2.4667 3.2076
0.4 60 0.023314 0.024235 0.05085 0.036331 0.13875 2.6691 2.8651 3.6272 2.4794 3.0127
0.5 0 0.025886 0.02703 0.053528 0.040894 0.14462 2.4039 2.5689 3.4458 2.2028 2.8904
0.5 10 0.025136 0.026198 0.052136 0.039277 0.14041 2.4757 2.6504 3.5378 2.2934 2.9771
0.5 20 0.024678 0.025676 0.051236 0.038234 0.13801 2.5215 2.7043 3.5999 2.356 3.0288
0.5 30 0.024312 0.025275 0.05083 0.037476 0.13743 2.5596 2.7472 3.6286 2.4037 3.0417
0.5 40 0.02391 0.024898 0.051169 0.036898 0.13927 2.6025 2.7888 3.6046 2.4414 3.0014
0.5 50 0.023409 0.024507 0.052595 0.036485 0.14456 2.6582 2.8332 3.5069 2.4689 2.8916
0.5 60 0.022828 0.02412 0.05535 0.036278 0.15459 2.726 2.8788 3.3324 2.4831 2.704
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