Downscaling the Maximum Carboxylation Rate ( π Derived ...
Transcript of Downscaling the Maximum Carboxylation Rate ( π Derived ...
Downscaling the Maximum Carboxylation Rate (πππππ₯) Derived from Satellite Sun-induced Chlorophyll
Fluorescence Data Using High-resolution Remote Sensing Products
by
Jiye Leng
A thesis submitted in conformity with the requirements for the degree of Master of Science
Department of Geography and Planning University of Toronto
Β© Copyright by Jiye Leng, 2020
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Downscaling the Maximum Carboxylation Rate (πππππ₯) Derived
from Satellite Sun-induced Chlorophyll Fluorescence Data Using
High-resolution Remote Sensing Products
Jiye Leng
Master of Science
Department of Geography & Planning
University of Toronto
2020
Abstract
The maximum carboxylation rate (πππππ₯) influences the magnitude of gross primary productivity
(GPP). Currently, reliable global πππππ₯ products derived from satellite sun-induced chlorophyll
fluorescence (SIF) data are at coarse resolutions, which cannot meet the demand of global
ecological research. In this thesis, the πππππ₯25 (πππππ₯ normalized to 25Β°C) dataset derived from
satellite SIF at a coarse resolution (0.1Β°, ~11 km) is downscaled to a higher resolution (1 km)
through a downscaling scheme using photochemical reflectance index (PRI) and spatial scaling
algorithms based on leaf chlorophyll content (LCC) and normalized difference vegetation index
(NDVI). The Boreal Ecosystem Productivity Simulator (BEPS) is used to evaluate the downscaled
πππππ₯25 using tower flux data. The results show that the LCC-downscaled πππππ₯
25 data appreciatively
improve GPP simulations at the tower sites, indicating LCC as a feasible way for downscaling the
πππππ₯25 dataset. GPP estimations at the 0.1Β° resolution decrease by 2-7% after πππππ₯
25 downscaling.
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Acknowledgments
I was fortunate to be admitted into University of Toronto. Life was unusual during this year with
the unprecedented pandemic, and Iβm writing to express appreciation to the people around me
throughout my master's study.
I would like to first thank my supervisor, Prof. Jing Chen, for offering me the opportunity to pursue
an M.Sc. degree and a Ph.D. degree at this university. He has always been supportive, willing to
help, and enlighten me when I encountered obstacles in my research. He is a role model for me as
a scientist and a bright lighthouse who leads me to step into the research career and encourages
me to fulfill my dream.
I also would like to thank my course instructor and TA instructor, Prof. Jane Liu, for guiding me
into the new scientific field. The three individual classes built me with a foundation of the
knowledge for exploring this new field and fostered my ability of logical and critical thinking.
I want to thank all the group members for their advice, help, and care during this year. Special
thanks to Yihong Liu, Dr. Rong Wang, Xinyao Xie, and Cheryl Rogers for their valuable
contributions to this thesis. Yihong has given me enormous help as a friend and as a senior. He
shared his datasets and gave useful suggestions when I was struggling with the research. Rong is
so supportive that she always responded to my questions with a smile as well as shared her own
experience of studying in U of T, even when she returned to China. Xinyao taught me code writing,
illuminated me with her unique learning experience, and exchanged her perspectives with me.
Cheryl helped me improve my academic writing and discussed the research with me. Besides,
thanks also go to Dr. Weiliang Fan, Dr. Zhaoying Zhang, etc. for the happy time we had during
my life in Toronto.
Finally, I want to express my sincere gratitude to my parents for their support of my dream of
studying abroad all the time. They are always open-minded and giving me the freedom to take my
own road. A particular βThank youβ also goes to my dear girlfriend, Jing Zhang, for the company
and help during this difficult and emotionally challenging year.
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Table of Contents
Acknowledgments.......................................................................................................................... iii
Table of Contents ........................................................................................................................... iv
List of Tables ................................................................................................................................ vii
List of Figures .............................................................................................................................. viii
Glossary of Acronyms and Abbreviations ..................................................................................... xi
Chapter 1 ..........................................................................................................................................1
Introduction .................................................................................................................................1
1.1 Introduction of πππππ₯ and methods of estimating πππππ₯ ................................................2
1.1.1 Definition of πππππ₯ ................................................................................................2
1.1.2 πππππ₯25 estimation from field measurements and flux measurements ................2
1.1.3 πππππ₯25 estimation from remote sensing .............................................................3
1.1.3.1 Direct correlations between πππππ₯25 and VIs ........................................3
1.1.3.2 Indirect estimation of πππππ₯25 from other parameters ...........................4
1.2 Introduction of spatial scaling, upscaling, and downscaling in remote sensing ..................5
1.2.1 Introduction of intra-pixel spatial heterogeneity......................................................5
1.2.2 Introduction to spatial scaling ..................................................................................5
1.2.3 Introduction to upscaling and downscaling .............................................................6
1.3 Significance of downscaling πππππ₯25 ..............................................................................8
1.3.1 Introduction of the eddy covariance and flux tower measurements ........................8
1.3.2 Building a bridge linking πππππ₯25 from coarse to high resolutions .....................9
1.4 Objectives and main structure of this research ..................................................................10
1.4.1 Research objectives ................................................................................................10
1.4.2 Structure of this research .......................................................................................11
1.5 References ..........................................................................................................................13
Chapter 2 ........................................................................................................................................19
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Trial of photochemical reflectance index (PRI) on downscaling πππππ₯25 ...........................19
2.1 Introduction ........................................................................................................................19
2.2 Data and methods ...............................................................................................................21
2.2.1 Data ........................................................................................................................21
2.2.2 Estimating GPP based on PRI................................................................................22
2.2.2.1 Trial of establishing generic correlations between PRI and LUE ...........22
2.2.2.2 Trial of establishing correlations between PRI and LUE at flux sites.....23
2.2.3 Retrieving πππππ₯25 based on GPP estimated from PRI .....................................24
2.2.3.1 Model description ....................................................................................24
2.2.3.2 Lookup-table establishment and πππππ₯25 searching ............................25
2.3 Discussion ..........................................................................................................................26
2.3.1 Results and problems found in the progress ..........................................................26
2.3.1.1 Trial of establishing generic PRI-LUE correlations for each PFT ..........26
2.3.1.2 Trial of establishing PRI-LUE correlations at each site ..........................30
2.3.2 Further work for solving the issues ........................................................................33
2.4 References ..........................................................................................................................34
Chapter 3 ........................................................................................................................................37
Leaf chlorophyll content (LCC) as a feasible way for downscaling πππππ₯25 .......................37
3.1 Introduction ........................................................................................................................37
3.2 Data and methods ...............................................................................................................39
3.2.1 Data ........................................................................................................................39
3.2.2 Algorithms for downscaling πππππ₯25 .................................................................41
3.2.3 Evaluation and Statistical Analysis ........................................................................42
3.3 Results and discussion .......................................................................................................43
3.3.1 The intra-pixel heterogeneity of the TROPOMI πππππ₯25 product .....................43
3.3.2 The πππππ₯25 seasonal variation ..........................................................................47
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3.3.3 Evaluation of downscaled πππππ₯25 at sites.........................................................49
3.3.3.1 The comparison of GPP simulation results .............................................49
3.3.3.2 The seasonal variation of GPP simulation results ...................................51
3.3.3.3 Statistical analysis of GPP simulation results .........................................54
3.3.3.4 GPP responses to πππππ₯25 before and after downscaling ....................56
3.3.4 Applying the downscaling method to regional and global scales ..........................58
3.4 Conclusion .........................................................................................................................63
3.5 References ..........................................................................................................................65
Chapter 4 ........................................................................................................................................69
Summary ...................................................................................................................................69
4.1 Main conclusions ...............................................................................................................69
4.2 Limitation of current work and plan for further work .......................................................71
4.3 References ..........................................................................................................................73
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List of Tables
Table 2-1 Basic information of datasets for establishing the lookup table. ................................. 22
Table 2-2 The abbreviations and full forms of the nine PFTs. ..................................................... 23
Table 2-3 Squared Pearson correlation coefficient between PRI and LUE using MODIS bands
10, 12, and 13 at 190 sites for nine plant functional types............................................................ 29
Table 2-4 Squared Pearson correlation coefficient between sPRI and LUE using MODIS band
10, 12, and 13 at five sites............................................................................................................. 30
Table 3-1 Site specifications for five flux sites ............................................................................ 39
Table 3-2 Statistical evaluation results of the downscaled πππππ₯25 based on GPP simulated
with πππππ₯25 before and after downscaling against GPP derived from tower flux measurements
....................................................................................................................................................... 55
Table 3-3 The summary of GPP responses to πππππ₯25 before and after downscaling .............. 56
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List of Figures
Figure 1-1 Variations of LAI retrieval biases of coarse resolution pixels with different water area
fraction using an NDVI-based non-linear LAI retrieval algorithm, expressed as the relative
difference in LAI. Source: Chen (1999). ........................................................................................ 6
Figure 1-2 Basic operations involving upscaling and downscaling in remote sensing. Source:
Bierkens et al. (2000). ..................................................................................................................... 7
Figure 1-3 Variation of global annual GPP with πππππ₯25 in the sensitivity analysis of the High-
Dimensional Model Representation (HDMR). Source: Ziehn and Tomlin (2017). ....................... 9
Figure 1-4 The theoretical framework and flowchart of downscaling πππππ₯25 and evaluation.11
Figure 2-1 The xanthophyll cycle involving the de-epoxidation and epoxidation of xanthophyll
pigments. Source: Demmig-Adams (1990). ................................................................................. 19
Figure 2-2 PRI-LUE correlations for each PFT, using band 10 as the reference band. Unit of
LUE: gC/MJ. The blue lines are regression lines of the scatter points in each subplot................ 27
Figure 2-3 PRI-LUE correlations for each PFT, using band 12 as the reference band. Unit of
LUE: gC/MJ. The blue lines are regression lines of the scatter points in each subplot................ 28
Figure 2-4 PRI-LUE correlations for each PFT, using band 13 as the reference band. Unit of
LUE: gC/MJ. The blue lines are regression lines of the scatter points in each subplot................ 29
Figure 2-5 sPRI-LUE correlations at each site using bands 10, 12, and 13. Unit of LUE: gC/MJ.
The blue lines are regression lines of the scatter points in each subplot. ..................................... 32
Figure 3-1 Typical leaf reflectance spectra, from 400nm to 2500nm. Source: Croft and Chen
(2018). ........................................................................................................................................... 38
Figure 3-2 The correlation between MTCI and measured leaf chlorophyll content. Source: Croft
et al. (2014). .................................................................................................................................. 40
Figure 3-3 Intra-pixel heterogeneous πππππ₯25 distribution within the TROPOMI pixel of each
site on the closest cloud-free day to the day of year 190. The πππππ₯25 values at 1 kmΓ1 km are
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downscaled through LCC. The red point represents the relative location of the site (1 kmΓ1 km)
in the TROPOMI pixel (0.1Β°Γ0.1Β°). ............................................................................................. 45
Figure 3-4 Intra-pixel heterogeneous πππππ₯25 distribution within the TROPOMI pixel of each
site on the closest cloud-free day to the day of year 190. The πππππ₯25 values at 1 kmΓ1 km are
downscaled through NDVI. The red point represents the relative location of the site (1 kmΓ1
km) in the TROPOMI pixel (0.1Β°Γ0.1Β°). ..................................................................................... 46
Figure 3-5 πππππ₯25 seasonal variation of each site from the day of year 100 to 300................ 48
Figure 3-6 GPP scatter plot of each site from the day of year 100 to 300. The Y-axis represents
GPP estimates and the X-axis represents flux measured GPP. ..................................................... 51
Figure 3-7 GPP scatter plot of all sites from the day of year 100 to 300...................................... 51
Figure 3-8 GPP seasonal variation of each site from the day of year 100 to 300. The daily EC
GPP data were merged from half-hourly EC measurements. The daily modeled GPP data were
merged from hourly BEPS GPP simulation results. ..................................................................... 52
Figure 3-9 Temporal patterns of the bias of GPP estimated with TROPOMI and downscaled
πππππ₯25 ...................................................................................................................................... 53
Figure 3-10 GPP responses to πππππ₯25 in the BEPS model. The Y-axis represents the GPP
estimates. Other inputs are kept consistent, using data on the day of year 124 at US-WCr site.
The X-axis represents πππππ₯25 values changing from 0 to 80 Β΅mol/m2/s. The blue curve shows
the GPP responses to change of πππππ₯25. The black line is the straightened version of the curve
between mean πππππ₯25 β SD and πππππ₯25 + SD. The blue point represents lumped GPP,
simulated using πππππ₯25 at the 0.1Λ resolution. The red point represents distributed, the mean
of GPP simulated based on the LCC-downscaled πππππ₯25 within the 0.1Λ pixel. .................... 57
Figure 3-11 Spatial distribution of standard deviation of downscaled πππππ₯25 values in
TROPOMI πππππ₯25 pixels on the day of year 200. ................................................................... 58
Figure 3-12 Comparison of global πππππ₯25 maps on the day of year 200. a) The 0.1Β°Γ0.1Β°
TROPOMI πππππ₯25 map; b) The downscaled 1 kmΓ1 km πππππ₯25 map. ........................... 59
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Figure 3-13 Comparison of πππππ₯25 maps of North America on the day of year 150. a) The
0.1Β°Γ0.1Β° TROPOMI πππππ₯25 map; b) The downscaled 1 kmΓ1 km πππππ₯25 map; c) and d)
Partially enlarged details of the red labeled region in a) and b); e) and f) Partially enlarged details
of the red labeled region in c) and d). ........................................................................................... 60
Figure 3-14 Comparison of πππππ₯25 maps of North America on the day of year 200. a) The
0.1Β°Γ0.1Β° TROPOMI πππππ₯25 map; b) The downscaled 1 kmΓ1 km πππππ₯25 map; c) and d)
Partially enlarged details of the red labeled region in a) and b); e) and f) Partially enlarged details
of the red labeled region in c) and d). ........................................................................................... 61
Figure 3-15 Comparison of πππππ₯25 maps of North America on the day of year 250. a) The
0.1Β°Γ0.1Β° TROPOMI πππππ₯25 map; b) The downscaled 1 kmΓ1 km πππππ₯25 map; c) and d)
Partially enlarged details of the red labeled region in a) and b); e) and f) Partially enlarged details
of the red labeled region in c) and d). ........................................................................................... 62
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Glossary of Acronyms and Abbreviations
APAR Absorbed Photosynthetically Active Radiation
BEPS Boreal Ecosystem Productivity Simulator
CI Clumping Index
EC Eddy Covariance
FPAR Fraction of Photosynthetically Active Radiation Absorbed by Vegetation
GEE Google Earth Engine
GPP Gross Primary Productivity
IOA Index Of Agreement
LAI Leaf Area Index
LCC Leaf Chlorophyll Content
LUE Light Use Efficiency
MAE Mean Absolute Error
MERIS MEdium Resolution Imaging Spectrometer
MODIS MODerate-resolution Imaging Spectroradiometer
MTCI MERIS Terrestrial Chlorophyll Index
NDVI Normalized Difference Vegetation Index
PAR Photosynthetically Active Radiation
PFT Plant Functional Type
PRI Photochemical Reflectance Index
R2 Squared Pearson correlation coefficient
RMSE Root Mean Square Error
SIF Sun-Induced chlorophyll Fluorescence
TROPOMI TROPOspheric Monitoring Instrument
πππππ₯ The maximum carboxylation rate
πππππ₯25 πππππ₯ normalized to 25Β°C
VI Vegetation Index
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Chapter 1
Introduction
The carbon cycle is an essential part of the earth system dynamics. To understand the carbon cycle,
various models have been developed to simulate carbon cycle processes (IPCC, 2013). Terrestrial
ecosystems play an essential role in the climate system through carbon cycling among vegetation,
soil, and atmosphere (Cao and Woodward, 1998). Process-based models are important for
understanding the terrestrial carbon cycle. In the widely-adopted Farquharβs scheme (Farquhar et
al., 1980), the maximum carboxylation velocity (πππππ₯) is a crucial parameter in modeling gross
primary productivity (GPP). The magnitude of πππππ₯ exerts an impact on the magnitude of GPP,
and the uncertainty in πππππ₯ will propagate through the model and be even magnified by the model
(Bonan et al., 2011, Chen et al., 2011), inducing errors in the final simulation results. Therefore,
reliable πππππ₯ datasets are prerequisite for accurate GPP modeling.
Remote sensing offers continuous observations of the globe, providing frequent and extensive
coverage of terrestrial ecosystems. Reflectance measurements taken by satellite sensors have been
successfully used for estimating πππππ₯, through vegetation indices (VIs), leaf chlorophyll content,
sun-induced chlorophyll fluorescence, etc. (Croft et al., 2017; He et al., 2019; Jin et al., 2012; Zhou
et al., 2014). Those measurements are at moderate spatial resolutions ranging from several hundred
meters (MERIS/ENVISAT, MODIS/TERRA) to several kilometers (TROPOMI/Sentinel-5P). At
such moderate resolutions, the field of view of the measurements would be heterogeneous due to
the nature of the land (Garrigues et al., 2006b). Radiometric sensors integrate the surface
reflectance over each pixel, so the intra-pixel heterogeneous information is lost during
measurement. The intra-pixel spatial heterogeneity causes biases in retrieved parameters if the
retrieval algorithm is nonlinear (Chen, 1999; Hu and Islam, 1997; Raffy, 1994; Tian et al., 2002).
Therefore, the intra-pixel spatial heterogeneity has been a subject of intensive studies for the
purpose to accurately retrieve land surface parameters (Chasmer et al., 2009; Duveiller and
Cescatti, 2016; Hong et al., 2011; Kim and Barros, 2002; Piles et al., 2011).
In this thesis, three factors were selected to investigate the intra-pixel spatial heterogeneity of
πππππ₯ and to examine if they can provide information for downscaling the πππππ₯ dataset derived
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from sun-induced chlorophyll fluorescence. The factors are photochemical reflectance index
(PRI), leaf chlorophyll content (LCC), and normalized difference vegetation index (NDVI).
This chapter introduces the background and main structure of this study. It serves three purposes:
1) to review various methods of estimating πππππ₯ ; 2) to review concepts of spatial scaling,
upscaling, and downscaling using remote sensing images; 3) to introduce the eddy covariance
techniques and state the significance of downscaling πππππ₯ ; and 4) to outline the research
objectives and main structure of this thesis.
1.1 Introduction of πππππ₯ and methods of estimating πππππ₯
1.1.1 Definition of πππππ₯
Terrestrial ecosystems βbreatheβ in carbon dioxide through the photosynthetic process and
βreleaseβ carbon dioxide into the atmosphere by autotrophic respiration and heterotrophic
respiration (Schimel, 1995). The feedbacks from the terrestrial carbon cycle can dramatically exert
effects on the biosphere-atmosphere carbon fluxes and the future climate change (Schimel et al.,
2015). Photosynthesis is the key driver of the terrestrial carbon cycle (Cadule et al., 2010; Canadell
et al., 2007), and it is an essential part of carbon cycle models (Bonan et al., 2011; Sitch et al.,
2003). Within the photosynthetic process, carboxylation fixes carbon dioxide in the air into
carbohydrates by adding carbon dioxide to ribulose 1,5 bisphosphate. In the Farquharβvon
CaemmererβBerry model (Farquhar et al., 1980), πππππ₯ (the maximum carboxylation rate) is a
fundamental parameter in simulating the photosynthetic activity of vegetation, which determines
the maximum photosynthetic capacity of leaves, and directly influences the amount of gross
primary productivity (GPP) in terrestrial ecosystems (Cramer and Field, 1999; Running et al.,
2004). In most process-based models, the πππππ₯ normalized to 25 Β°C (πππππ₯25 ) is used. There are
several methods to estimate πππππ₯25 , including the field measurements, the flux measurements, and
the remote sensing.
1.1.2 πππππ₯25 estimation from field measurements and flux measurements
πππππ₯25 can be estimated based on field measurements. As a key parameter of understanding the
capacity of a leaf for CO2 assimilation, πππππ₯25 can be retrieved through gas-exchange process
analysis (Harley and Baldocchi, 1995; Wullschleger, 1993). πππππ₯25 is obtained from the A/Ci
curves by measuring the photosynthesis rates under different carbon dioxide levels, where A
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represents the leaf photosynthesis rate and Ci stands for the intercellular carbon dioxide
concentration of the leaf. πππππ₯25 values of different species and different plant functional types
(PFTs) have been measured from field experiments in many studies (Kosugi and Matsuo, 2006;
Kosugi et al., 2003). Bahar et al. (2017) compared πππππ₯25 values of 210 species at 18 field sites in
tropical moist forests. These field measurements provide substantial πππππ₯25 data for global carbon
cycle simulation. However, the field gas exchange experiments cannot provide πππππ₯25 values for
large geographical areas, for it is time and labor-consuming for data collections.
To address the limitations of gas exchange experiments, many studies utilized flux measurements
using the eddy covariance technique to improve the efficiency and accuracy of πππππ₯25 estimation
(Wolf et al., 2006). Eddy covariance is a micro-meteorological method which can directly observe
the energy and gas exchange between ecosystems and the atmosphere (Liang et al., 2012a). The
eddy covariance technique can directly, accurately, and continuously measure the carbon exchange
and evapotranspiration of an ecosystem (Baldocchi, 2008). Many studies estimated πππππ₯25 based
on eddy covariance flux measurements. Wang et al. (2007) applied data assimilation techniques to
invert models to derive πππππ₯25 values from flux measurements. The πππππ₯
25 values estimated from
eddy covariance flux data agree well with the πππππ₯25 retrieved from field measurements, indicating
the usefulness of flux data for πππππ₯25 estimates (Zheng et al., 2017).
1.1.3 πππππ₯25 estimation from remote sensing
Remote sensing provides spatially continuous observation of the Earth's surface, covering
extensive spatial and temporal land surface processes at the global scale. Based on the reflectance
spectra of ground objects, data from satellite sensors offer information on biophysical and
physiological characteristics of terrestrial ecosystems. However, remotely measured reflectance
cannot estimate πππππ₯25 , because the variations of πππππ₯
25 cannot lead to directly detectable spectral
signals in the reflectance received by the sensors. To solve this issue, many efforts have been made
to retrieve πππππ₯25 indirectly from remotely sensed data.
1.1.3.1 Direct correlations between πππππ₯25 and VIs
Vegetation indices (VIs) are compositions of reflectances in several spectral bands of remotely
sensed data to assess a particular property of vegetation. VIs designed for assessing vegetation
physiological status are often composed of spectral bands that are sensitive to the physiological
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change of vegetation, often together with bands as references to exclude the influence of the plant
structure and the background. Various VIs have been designed to trace structural, biophysical and
physiological traits of vegetation from remote sensing data, and have been successfully used to
estimate structural parameters, including leaf area index (Chen and Cihlar, 1996; Zheng and
Moskal, 2009) and clumping index (Chen et al., 2005; He et al., 2012) and physiological
parameters, including leaf chlorophyll content (Croft et al., 2014). Wang et al. (2008) found the
correlation between πππππ₯25 and the broadband simple ratio for beech stands in the cold-temperate
zone of Japan and concluded different VI-πππππ₯25 correlations at different elevations. Zhou et al.
(2014) observed close VI-πππππ₯25 relationships in deciduous and mixed forests. Jin et al. (2012)
investigated the correlation between NDVI and πππππ₯25 and found tight exponential relationships,
showing the feasibility of NDVI to derive the interannual trajectory of photosynthetic capacity.
However, the interseasonal and interannual variations of VI- πππππ₯25 relationships restrict the
reliability of the retrieval of πππππ₯25 through VIs (Croft et al., 2020).
1.1.3.2 Indirect estimation of πππππ₯25 from other parameters
Sun-induced chlorophyll fluorescence (SIF) has been widely used to study and track vegetation
traits (Guan et al., 2016; Guanter et al., 2014; He et al., 2017; Joiner et al., 2013; KΓΆhler et al.,
2018; Sun et al., 2017). Zhang et al. (2014) generated unique relationships between SIF and πππππ₯25 ,
and obtained πππππ₯25 values using remotely sensed SIF through model inversion. He et al. (2019)
retrieved a πππππ₯25 dataset from a data assimilation system based on the significant correlation
between SIF and GPP, showing the feasibility of using satellite SIF data to retrieve the information
on leaf photosynthetic capacity. Liu (2019) adopted the main framework of He et al. (2019) and
used the SIF data from the newly launched sensor, TROPOMI onboard Sentinel-5P, to produce a
πππππ₯25 map. Besides, leaf chlorophyll content (LCC), which is an essential parameter responsible
for the light harvest as part of photosynthetic processes, can be retrieved from remote sensing data
(Croft et al., 2020; Croft et al., 2014; Croft et al., 2015). Croft et al. (2017) found a significant
relationship between LCC and πππππ₯25 , and assessed the feasibility of LCC retrieved from satellite
data to estimate πππππ₯25 . Other studies also show strong correlations between LCC and πππππ₯
25
(HomolovΓ‘ et al., 2013; Houborg et al., 2013), suggesting the potential for retrieving πππππ₯25 over
large areas based on LCC. πππππ₯25 can also be quantified considering leaf nitrogen content and
nitrogen use efficiency (Kattge et al., 2009).
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1.2 Introduction of spatial scaling, upscaling, and downscaling in remote sensing
1.2.1 Introduction of intra-pixel spatial heterogeneity
Remote sensing techniques provide observations covering large spatial extents at high temporal
frequencies. However, those observations are often at moderate or coarse spatial resolutions from
several hundred meters to several tens of kilometers, which may contain very heterogeneous land
surfaces in the footprint of the sensors (Garrigues et al., 2006b). The landscape features within a
moderate-resolution pixel, such as agricultural fields and vegetation patches, are relatively small
in comparison with the pixel dimension. Many such features are included within one pixel, and
this within-pixel heterogeneous information is lost in moderate resolution images. Therefore, it is
of great importance to explore how the intra-pixel spatial heterogeneity of remote sensing data
affects the retrieval of land surface parameters and the simulation results of satellite-data driven
models.
1.2.2 Introduction to spatial scaling
The spatial heterogeneity includes two components, the spatial variability and the spatial structures
(Garrigues et al., 2006b). To understand the subpixel information, intra-pixel heterogeneity and
their effects, spatial scaling has been widely adopted to link information across scales. Spatial
scaling refers to using the information at one scale to derive information at another scale (Jarvis,
1995). If the algorithms used for retrieving land surface parameters are non-linear, the intra-pixel
heterogeneity induces biases in the retrieved parameters over pixels at coarse resolutions because
the reflectance acquired at the coarse resolutions is an averaging process that masks subpixel
variations (Chen, 1999).
Several studies have been carried out to investigate the effect of spatial scaling and to correct the
biases of retrieved parameters at coarse resolutions. Garrigues et al. (2006a) founded that if the
pixel is heterogeneous and the transfer function from remote sensing data to LAI is not linear,
biases would exist in the computation of LAI, and then they proposed a model to estimate and
correct the errors in LAI estimates. Similar results in Chen (1999) showed that negative biases of
LAI estimates occurred in heterogeneous pixels. Chen et al. (2013) studied the effect of vegetation
heterogeneity and surface topography on net primary productivity (NPP) estimates at a coarse
resolution (1 km) using an eco-hydrological model applied to a fine resolution (30 m). Biases were
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introduced when averaging subpixel inputs for NPP estimation and a scaling algorithm was
developed to correct the biases in NPP retrieval at coarse resolutions. El Maayar and Chen (2006)
proposed a method to use subpixel information to correct the evapotranspiration at a coarse
resolution, considering the spatial heterogeneity of vegetation, topography, and soil texture.
Chasmer et al. (2009) assessed the influence of spatial heterogeneity on GPP estimation using
airborne light detection and ranging (Lidar), scaling from 1 m to 1000 m, and pointed out Lidar as
an appropriate method for scaling between tower flux GPP and satellite-based GPP products.
Therefore, it is essential to investigate the spatial scaling effect for accurate land surface
parameters derived from remote sensing data.
Figure 1-1 Variations of LAI retrieval biases of coarse resolution pixels with different water
area fraction using an NDVI-based non-linear LAI retrieval algorithm, expressed as the
relative difference in LAI. Source: Chen (1999).
1.2.3 Introduction to upscaling and downscaling
Differing from spatial scaling studies, which focus on the biases of retrieved surface parameters
from non-linear algorithms in heterogeneous pixels, upscaling and downscaling are aggregation
and disaggregation of the original dataset, combined with other data for better retrieval of land
surface parameters. Upscaling is defined as a decrease in spatial resolution or extrapolation from
point to grid (Bierkens et al., 2000). In situ measurements can be upscaled with remote sensing
data and models to regional scales. Ueyama et al. (2013) upscaled CO2 fluxes from 21 towers to
7
estimate the Alaskan CO2 budget by combining remote sensing data based on a support vector
regression model and the predicted upscaled regional fluxes were found to be consistent with GPP
and respiration field observations. Kang et al. (2015) developed a regression Kriging model to
upscale soil moisture measurements with MODIS products and the upscaling model showed high
prediction accuracy. Fu et al. (2014) combined tower flux measurements with satellite data based
on an upscaling model framework to estimate net ecosystem exchange (NEE) at high spatial-
temporal resolutions. The modeled results showed consistency with observed data and they found
that higher spatial resolution remote sensing products with tower flux measurements resulted in
better upscaled results. Thus, the upscaling method can integrate in situ measurements with
satellite data for precise regional environmental monitoring.
Figure 1-2 Basic operations involving upscaling and downscaling in remote sensing. Source:
Bierkens et al. (2000).
On the contrary, downscaling is defined as an increase in spatial resolution and also referred to as
the disaggregation of the original dataset into finer spatial units (Bierkens et al., 2000). Data at
coarse resolutions provide useful information and the downscaled results at high resolutions are
obtained through various methods based on the coarse-resolution data. The downscaling process
requires useful and available information at that resolution and the downscaled results restore the
spatial variation at a finer scale (Price et al., 2000).
Downscaling methods have been extensively used in remote sensing to infer information at a fine
resolution from data at a coarse resolution. Hong et al. (2011) downscaled an evapotranspiration
map at 250 m resolution derived from MODIS data using Landsat imagery at 30 m resolution. The
8
spatial distribution patterns of the disaggregated evapotranspiration maps were investigated from
the downscaled imagery. Duveiller and Cescatti (2016) performed a spatial downscaling of SIF,
which led to an improved temporal correlation between SIF and GPP. Their results supported that
the downscaled SIF could be used as new datasets for estimating GPP using satellite data. Kim
and Barros (2002) proposed a downscaling model to investigate the heterogeneous subpixel
information of remotely sensed soil moisture data. Their model adopted a modified fractal
interpolation method, which generated unique fractal surfaces to study the heterogeneity.
Therefore, downscaling can explore subpixel information and bring datasets from coarse to high
spatial resolutions.
1.3 Significance of downscaling πππππ₯25
1.3.1 Introduction of the eddy covariance and flux tower measurements
Eddy covariance, a micro-meteorological method, is prevailing in observing the exchanges of gas,
energy, and momentum between ecosystems and the atmosphere (Liang et al., 2012a, b). The eddy
covariance method can directly, precisely, and continuously measure the carbon, water, and heat
fluxes at various time scales ranging from hour, day, month to year. The spatial scales of
observations at each tower site extend through the flux footprint around the tower, ranging from
100 m to 1000 m (GΓΆckede et al., 2004). The eddy covariance technique has been proved to be the
most efficient way to measure the interactions between terrestrial ecosystems and the atmosphere
at the ecosystem scale (Baldocchi, 2008; Friend et al., 2007).
Photosynthesis happens during the day, absorbing solar energy and transforming it into vegetation
productivity, and stops at night. The observed flux at night represents the respiration activities of
the terrestrial ecosystem, including the plant autotrophic respiration and the heterotrophic soil
respiration. Without the influence of soil water, the respiration of terrestrial ecosystems (πΈπ ) can
be calculated as below (Liang et al., 2012a):
πΈπ = πΈπ 0 Γ π10
π β π0π0 (1-1)
where πΈπ 0 is the respiration rate at the base temperature π0 . π is the temperature at the
measurement time and π10 is the temperature-sensitivity factor of ecosystem respiration, which is
defined as the increase of the ecosystem respiration rate with the increase in temperature every 10
9
Β°C. Therefore, when photosynthesis, plant autotrophic, and heterotrophic soil respiration co-occur
during the day, the observed flux represents the net ecosystem productivity (NEP) and GPP can
be obtained as:
πΊππ = ππΈπ + πΈπ πππ¦ (1-2)
where πΈπ πππ¦ represents the ecosystem respiration during the day, and it is positive for losing
carbon from the ecosystem. NEP is positive for carbon uptake by the ecosystem. In this way, GPP
at each tower site can be accurately and continuously measured.
1.3.2 Building a bridge linking πππππ₯25 from coarse to high resolutions
As one of the key parameters in terrestrial biosphere process-based models, πππππ₯25 accounts for the
photosynthetic capacity of plants. However, little attention has been drawn to the intra-pixel spatial
heterogeneity of πππππ₯25 . Since GPP is sensitive to the variation of πππππ₯
25 (Ziehn and Tomlin, 2017),
biases in πππππ₯25 would induce errors in GPP estimates, as shown in Figure 1-3. Currently, the
spatial resolution of πππππ₯25 datasets derived from remote sensing data ranges from several to
several tens of kilometers while tower flux footprints are only about one kilometer. Therefore,
there is an apparent mismatch between the spatial resolutions of tower flux footprints and available
πππππ₯25 datasets derived from satellite data. This mismatch will induce biases in GPP estimates using
flux tower measurements as model inputs if the intra-pixel πππππ₯25 values are not homogeneous.
Thus, the intra-pixel spatial distribution of πππππ₯25 datasets needs to be explored.
Figure 1-3 Variation of global annual GPP with π½ππππππ in the sensitivity analysis of the High-
Dimensional Model Representation (HDMR). Source: Ziehn and Tomlin (2017).
10
In order to better understand the intra-pixel heterogeneous information of πππππ₯25 datasets, it is
necessary to downscale πππππ₯25 with the useful and available information at a finer resolution. This
will also enhance the accuracy of GPP estimates at a finer resolution within the pixel such as tower
flux sites and support investigation into the intra-pixel spatial distribution of πππππ₯25 . Hence, the
downscaling process builds a bridge linking the πππππ₯25 datasets from coarse to high spatial
resolutions and resolves the mismatch between the πππππ₯25 datasets and tower flux footprints.
1.4 Objectives and main structure of this research
1.4.1 Research objectives
This research is designed to quantitively analyze the heterogeneity and the spatial distribution of
πππππ₯25 within the pixels of the πππππ₯
25 dataset produced by Liu (2019) and downscale the πππππ₯25
dataset to 1 km, same as the footprint of tower sites. This dataset was produced by firstly,
estimating GPP from angularly normalized TROPOMI SIF data, and then retrieval πππππ₯25 at the
0.1Β°Γ0.1Β° resolution from an Ensemble Kalman filter (EnKF) data assimilation system (He et al.,
2019). Photochemical reflectance index (PRI), normalized difference vegetation index (NDVI),
and leaf chlorophyll content (LCC) are selected to investigate whether they can provide useful
information at a high spatial resolution for downscaling πππππ₯25 . A spatial scaling algorithm is also
developed for the downscaling purpose.
The theoretical framework of this study is presented in the flowchart below.
This thesis research encompasses the following four objectives:
1. To quantify the heterogeneity within the pixels of the πππππ₯25 product derived from satellite sun-
induced chlorophyll fluorescence;
2. To explore a feasible and effective way to downscale πππππ₯25 using high-resolution remote
sensing images;
3. To demonstrate the improvement of GPP simulation at tower flux sites using the downscaled
πππππ₯25 .
11
4. To produce the first global 1 km πππππ₯25 map with the downscaling method satisfactorily
evaluated with the flux-derived GPP.
Figure 1-4 The theoretical framework and flowchart of downscaling π½ππππππ and evaluation.
1.4.2 Structure of this research
The thesis is organized following the research objectives mentioned in Chapter 1.4.1.
In Chapter 2, PRI is selected for downscaling πππππ₯25 derived from satellite sun-induced chlorophyll
fluorescence from Liu (2019) at 0.1-degree resolution (approximately 11 km) to 1 km resolution.
Generic correlations of PRI-LUE for each plant functional type and unique correlations of PRI-
LUE of five sites are established based on historical data from FLUXNET and AMERIFLUX,
respectively. GPP estimates at the downscaled resolution are then derived following the
established PRI-LUE correlations. A lookup table approach for searching πππππ₯25 values is
established based on BEPS simulations that determine the relationship between πππππ₯25 and GPP
12
under given meteorological conditions. The downscaling results are found to be unsatisfactory and
are analyzed, leading to the development of alternatives shown in Chapter 3.
In Chapter 3, LCC and NDVI are selected for downscaling the πππππ₯25 dataset mentioned above. A
scaling algorithm for obtaining downscaled πππππ₯25 at 1 km resolution is developed and a scaling
ratio is designed. To evaluate the results, the original πππππ₯25 and the downscaled πππππ₯
25 are used as
inputs to the BEPS model to simulate GPP. The downscaled πππππ₯25 is evaluated over five different
sites from AMERIFLUX with available GPP measurements in 2018, by comparing GPP from EC
towers with GPP values simulated from the original πππππ₯25 and the downscaled πππππ₯
25 . After a
downscaling method is satisfactorily evaluated with the flux-derived GPP at the tower sites, a
πππππ₯25 map at 1 km resolution for North America is produced as an example.
In Chapter 4, the main conclusions of this thesis are summarized. The limitations are discussed
and a plan for future work is proposed.
13
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Chapter 2
Trial of photochemical reflectance index (PRI)
on downscaling πππππ₯25
2.1 Introduction
When plants receive excessive photosynthetic active radiation, the xanthophyll pigments in leaves
dissipate the excessive energy and prevent plants from photodamage. This energy dissipation is
achieved through the interconversion of three xanthophyll pigments in the xanthophyll cycling.
When plants receive excess energy, the light-harvesting xanthophyll, violaxanthin is de-
epoxidized to the energy quenching xanthophyll, zeaxanthin via antheraxanthin, and this process
is reversed under light-limiting conditions (Demmig-Adams, 1990; Demmig-Adams and Adams,
1996). Therefore, the xanthophyll cycle is associated with the efficiency of light harvesting, thus
the photosynthetic efficiency.
Under excessive light conditions, the content of zeaxanthin increases in the xanthophyll cycle and
a decrement in the leaf reflectance at around 531nm (π 531) has been observed (Gamon et al., 1990).
Then, Gamon et al. (1992) proposed a narrow band index, the physiological reflectance index
(PRI), using the signal at π 531 and a reference band (π πππ) to minimize the effects of other factors
on the xanthophyll signal. The original formula was presented by:
ππ πΌ = π πππ β π 531
π πππ + π 531 (2-1)
Figure 2-1 The xanthophyll cycle involving the de-epoxidation and epoxidation of
xanthophyll pigments. Source: Demmig-Adams (1990).
20
Gamon et al. (1992) used π 550 as the reference band and observed its capacity to track diurnal
photosynthetic efficiency. The relationship between PRI and LUE was also tested under nitrogen
stressed and water stress situations in sunflower canopies. PeΓUelas et al. (1995) used π 570 as the
reference band and renamed the index as photochemical reflectance index (PRI), which was
calculated as:
ππ πΌ = π 531 β π 570
π 531 + π 570
(2-2)
The rearranged PRI was found to yield a positive correlation with LUE and this formula became
the PRI definition.
Many studies have been done to use remotely sensed PRI to capture LUE changes across different
species, at sites, and over regional areas. Rahman et al. (2004) first used PRI derived from band
11 (bandwidth 526-536nm) and band 12 (546-556nm) of the Moderate Resolution Imaging
Spectroradiometer (MODIS) onboard the Terra and Aqua platforms to track the seasonal variation
of LUE. The correlation between PRI and LUE was used to improve the LUE model (Monteith,
1972) for better net primary productivity simulation. Drolet et al. (2005) tested MODIS bands 1
(620-670nm), 4 (545-565nm), 12, and 13 (662-672nm) as potential reference bands for PRI and
selected band 13 to correlate the scaled PRI with LUE over a boreal trembling aspen site. Drolet
et al. (2008) later tested the relationship between MODIS-derived PRI and LUE over eight boreal
eddy covariance (EC) towers and found a strong PRI-LUE correlation when all sites points were
combined. Goerner et al. (2009) used MODIS-derived PRI to track LUE under seasonal drought
conditions in a Mediterranean forest. The relationship between MODIS PRI and LUE has been
studied and tested in many studies in various ecosystems (Garbulsky et al., 2008; Guarini et al.,
2014; Middleton et al., 2016; Moreno et al., 2012), showing the capacity of MODIS PRI to track
LUE changes.
Therefore, PRI can provide useful physiological information of plants at a higher spatial resolution.
In this chapter, the PRI data obtained from MODIS are used to downscale the πππππ₯25 dataset. In
Section 2.3, the data preprocessing process and the designed methodology are introduced. The
BEPS model used for establishing the lookup table is also introduced. In Section 2.4, the results
are presented. The problems found in the process are discussed, and further works for solving those
issues are proposed.
21
2.2 Data and methods
2.2.1 Data
A total number of 190 sites from FLUXNET (https://fluxnet.fluxdata.org) were selected to
establish generic correlations between PRI and LUE for nine plant functional types (The basic
information of the sites can be found at https://fluxnet.fluxdata.org/sites/site-list-and-
pages/?view=table). Five sites from AMERIFLUX (https://ameriflux.lbl.gov/) were selected to
establish unique correlations between PRI and LUE at each site, including US-Bi2 (Sanchez et al.,
2017-), US-Los (Sulman et al., 2009), US-Rpf (Ueyama et al., 2019), US-Tw4 (Sanchez et al.,
2013-), and US-WCr (Cook et al., 2004). For PFT-level correlations between PRI and LUE, all
historical data from FLUXNET (2001-2015) were used. For site-level correlations between PRI
and LUE, data of five sites in 2017 were used. The established correlations were used for the GPP
estimation of 2018. The meteorological data as inputs for GPP simulation were obtained from the
flux measurements, including shortwave radiation, air temperature, vapor pressure deficit,
precipitation, and wind speed.
Google earth engine (GEE) as a cloud-based platform for planetary-scale geospatial analysis
provides an efficient cloud-computing tool for remote sensing studies (Gorelick et al., 2017).
Surface reflectance data were collected from the MODIS Terra surface spectral reflectance data
product (MODOCGA) at a resolution of 1 km from GEE. LAI and FPAR data were collected from
MODIS LAI/FPAR 4-Day 500 m product (MCD15A3H) from GEE. And the LAI/FPAR data
were smoothed and resampled to a daily 1 km product. Clumping index (CI) data used for
calculating the gap fraction of a canopy were derived from the MODIS BRDF product (He et al.,
2016). Global πππππ₯25 data were derived from TROPOMI SIF datasets at a resolution of 0.1 degrees
(Liu, 2019).
For establishing the lookup table to search the πππππ₯25 values with GPP derived from PRI, the input
data of the BEPS model corresponds to the data used by Liu (2019), as shown in Table 2-1. The
data were all spatially interpolated to a 0.1Β° Γ 0.1Β° grid.
22
Table 2-1 Basic information of datasets for establishing the lookup table.
Input data Data source Original Spatial
Resolution References
Surface wind speed
Air temperature
Shortwave radiation
Surface precipitation
Atmosphere pressure
MERRA-2 0.625Β° Γ 0.5Β°
LAI MODIS & AVHRR 8 km Γ 8 km Liu et al. (2012)
CI MODIS 36 km Γ 36 km He et al. (2016)
PFT MODIS 1 km Γ 1 km Friedl et al. (2002)
2.2.2 Estimating GPP based on PRI
The LUE model was adopted to link PRI and GPP. The LUE model was initially established from
the linear relationship between the amount of PAR absorbed by plants, and their GPP (Monteith,
1972; Monteith et al., 1977), where the APAR can be further estimated by introducing a factor
referring to as the fraction of incident PAR absorbed by plants (FPAR).
πΊππ = πΏππΈ β π΄ππ΄π (2-3)
The LUE values of each day with available data can be obtained by:
πΏππΈ = πΊππ
ππ΄π β πΉππ΄π (2-4)
2.2.2.1 Trial of establishing generic correlations between PRI and LUE
To estimate the global GPP distributions of 2018 at 1 km resolution, reliable PRI-LUE correlations
should be initially established. In this part, the generic correlations were firstly established based
on all historical flux measurements from FLUXNET. Band 11 (centered at 531nm) was selected
as the signal band of PRI. Band 10 (centered at 488nm), band 12 (centered at 551nm), and band
13 (centered at 667nm) were selected as the reference band of PRI, respectively. PRI was
calculated as:
23
ππ πΌ = π΅11 β π΅πππ
π΅11 + π΅πππ
(2-5)
From the first day with available data to the ending day of each site, PRIs using those three bands
were derived on cloud-free days on the pixels of each site. Then noise filters were applied to
exclude noises and errors.
Considering different physiological characteristics among different plant species, nine PFTs were
selected. The abbreviations and full forms of the nine PFTs are listed in Table 2-2 below.
Table 2-2 The abbreviations and full forms of the nine PFTs.
Abbreviation Full form
DBF Deciduous Broadleaf Forest
EBF Evergreen Broadleaf Forest
DNF Deciduous Needleleaf Forest
ENF Evergreen Needleleaf Forest
MF Mixed Forest
GRA Grassland
CRO Cropland
SH Shrubs
WET Wetlands
The GPP and PAR data derived from half-hourly flux measurements during all daytime were
averaged to daily GPP and PAR, to obtain daily LUE data. All available PRIs of each PFT were
aggregated to perform regression analysis against the corresponding LUE. The PRI reference band
with the best correlations was finally selected to derive the generic correlations for each PFT,
which would be further used to estimate the GPP of 2018 at a 1 km resolution.
2.2.2.2 Trial of establishing correlations between PRI and LUE at flux sites
In this part, unique PRI-LUE correlations at each site were established based on flux
measurements, the MODIS reflectance product, and the MODIS LAI/FPAR product (mentioned
in Chapter 2.3.1) of 2017. Band 10 (centered at 488nm), band 12 (centered at 551nm), and band
13 (centered at 667nm) were selected as the reference band of PRI, respectively. The LUE data
were calculated from the GPP and PAR data derived from half-hourly flux measurements from 10
24
am to 12 pm at local time, which approximately corresponds to the crossing time of MODIS Terra
(Drolet et al., 2008). In order to obtain only positive values, PRIs using those four bands were
scaled using the mathematical transformation (Rahman et al., 2004):
π ππ πΌ = ππ πΌ + 1
2(2-6)
The scaled PRI (sPRI) derived on cloud-free days on the pixels of each site and noise filters were
applied to exclude noises and errors of sPRI. The sPRI reference band with the best correlations
was finally selected to derive the unique correlation between sPRI and LUE for that site. Then
following the established correlations of each site, the GPP estimates were retrieved.
2.2.3 Retrieving πππππ₯25 based on GPP estimated from PRI
2.2.3.1 Model description
In this study, the Boreal Ecosystem Productivity Simulator (BEPS) was adopted to link πππππ₯25 with
the GPP estimated from PRI. The BEPS model is a diagnostic enzyme kinetic model, which has
been frequently used for regional and global carbon cycle simulation (Chen et al., 2019; Gonsamo
et al., 2013; Wang et al., 2004). BEPS was first developed for the Boreal Ecosystem-Atmosphere
Study, coupling water and carbon cycles at regional scales (Liu et al., 1999, 2002; Liu et al., 1997).
BEPS adopts a two-leaf theory, separating sunlit and shaded leaves during the modeling process
(Chen et al., 1999). The total photosynthesis (Ac) at the canopy level is modeled by combining the
photosynthetic rates of sunlit and shaded leaves (π΄π π’π and π΄π βπππ). GPP at the canopy level is
modeled by multiplying the corresponding LAI to the photosynthetic rates of sunlit and shaded
leaf groups:
πΊππ = π΄π π’ππΏπ΄πΌπ π’π + π΄π βππππΏπ΄πΌπ βπππ (2-7)
In this study, the MODIS LAI represents the total LAI of the canopy, where πΏπ΄πΌπ π’π and πΏπ΄πΌπ βπππ
stand for LAI of sunlit and shaded leaves. When separating πΏπ΄πΌπ π’π and πΏπ΄πΌπ βπππ, the clumping
index (Ξ©) is adopted to consider the non-random distribution patterns of the leaves inside the
canopy (Chen, 1996). The clumping index characterizes the extent of clumping of the foliage,
where the Ξ© equals to 1 in canopies with randomly distributed foliage. The more clumping of the
leaves, the smaller the Ξ© is. Then πΏπ΄πΌπ π’π and πΏπ΄πΌπ βπππ are calculated as:
25
πΏπ΄πΌπ π’π = 2πππ π (1 β πβ0.5Ξ©πΏπ΄πΌ
πππ π ) (2-8)
πΏπ΄πΌπ βπππ = πΏπ΄πΌ β πΏπ΄πΌπ π’π (2-9)
BEPS follows Farquharβs principle (Farquhar et al., 1980) to calculate the photosynthetic rate of a
leaf as the minimum value between ππ (radiation-limited gross photosynthesis rate) and ππ
(Rubisco-limited gross photosynthetic rate), where:
ππ = π½πΆπ β Ξ
4.5πΆπ + 10.5Ξ(2-10)
ππ = ππ
πΆπ β Ξ
πΆπ + πΎ(2-11)
π΄π = min{ππ , ππ} β π π (2-12)
π½, ππ, πΆπ , Ξ, πΎ, and π π represent the radiation-dependent electron transport rate, the maximum
carboxylation rate, intercellular carbon dioxide concentration, temperature-dependent carbon
dioxide compensation point without dark respiration, the temperature-dependent function of
enzyme kinetics, and daytime leaf dark respiration, respectively. Following this method, with
given πππππ₯25 values, LAI and clumping data, meteorological data, and other inputs, BEPS can
simulate hourly GPP of that day. Likewise, when given a GPP value, together with other
environmental conditions, the πππππ₯25 value can be estimated.
2.2.3.2 Lookup-table establishment and πππππ₯25 searching
In order to invert an ecological model, for example, to assess the physiological conditions of plants
based on remote sensing or flux measurements, many methods have been employed, including
iterative optimization methods, neural networks, lookup-tables, etc. (Croft and Chen, 2018).
Among various methods, the lookup-table method provides a computationally efficient way to
model the physiological characteristics. In this study, the BEPS model was iteratively run using
the input data described in Chapter 2.3.1. The simulated GPP and meteorological data were
averaged to each day, and the daily GPP, daily meteorological conditions, PFT, LAI, and πππππ₯25
formed the lookup-table. Then, the GPP estimated from PRI at 1 km resolution was used to search
26
the nearest πππππ₯25 value from the lookup-table as the downscaled πππππ₯
25 . The criteria here were: 1)
whether the scatters of PRI and LUE were clustered; 2) whether the Pearson coefficients of PRI
and LUE were good enough to achieve reliable GPP estimations. However, due to the
unsatisfactory accuracy of GPP estimates from PRI, this part of work was not completed.
2.3 Discussion
2.3.1 Results and problems found in the progress
2.3.1.1 Trial of establishing generic PRI-LUE correlations for each PFT
The generic PRI-LUE correlations were established using MODIS bands 10, 12, and 13 as the
reference band. The PRI data were derived using MODIS bands 10, 12, and 13 as the reference
band (ππ πΌπ΅10, ππ πΌπ΅12, and ππ πΌπ΅13), respectively.
As shown in Figure 2-2, the scatter points are dispersed, indicating weak correlations between
either ππ πΌπ΅10, ππ πΌπ΅12 or ππ πΌπ΅13 and LUE. Similar results can also be observed in Figure 2-3, and
Figure 2-4. The R2 values are all low though most of the correlations are significant (p<0.001), as
shown in Table 2-3.
27
Figure 2-2 PRI-LUE correlations for each PFT, using band 10 as the reference band. Unit of
LUE: gC/MJ. The blue lines are regression lines of the scatter points in each subplot.
28
Figure 2-3 PRI-LUE correlations for each PFT, using band 12 as the reference band. Unit of
LUE: gC/MJ. The blue lines are regression lines of the scatter points in each subplot.
29
Figure 2-4 PRI-LUE correlations for each PFT, using band 13 as the reference band. Unit of
LUE: gC/MJ. The blue lines are regression lines of the scatter points in each subplot.
Table 2-3 Squared Pearson correlation coefficient between PRI and LUE using MODIS
bands 10, 12, and 13 at 190 sites for nine plant functional types.
Plant Functional Type ππ πΌπ΅10 ππ πΌπ΅12 ππ πΌπ΅13
30
ENF 0.02* 0.02* 0.01*
EBF 0.15* 0.04* 0.00*
DNF 0.06* 0.02 0.01
DBF 0.00 0.00 0.11*
MF 0.00 0.01* 0.01*
SH 0.10* 0.06* 0.07*
GRA 0.08* 0.02* 0.05*
WET 0.02* 0.02* 0.01*
CRO 0.00* 0.00 0.14*
*p<0.001
2.3.1.2 Trial of establishing PRI-LUE correlations at each site
The PRI-LUE correlations at each site also used MODIS bands 10, 12, and 13 as the reference
band. As shown in Figure 2-5, the PRI data of five sites were derived using MODIS band 10, 12,
and 13 as the reference band (ππ πΌπ΅10, ππ πΌπ΅12, and ππ πΌπ΅13), respectively.
Table 2-4 Squared Pearson correlation coefficient between sPRI and LUE using MODIS
band 10, 12, and 13 at five sites.
Site Code π ππ πΌπ΅10 π ππ πΌπ΅12 π ππ πΌπ΅13
US-Bi2 0.29* 0.26* 0.49*
US-Los 0.00 0.02 0.25*
US-Rpf 0.10* 0.13* 0.04
US-Tw4 0.12* 0.01 0.37*
US-WCr 0.11* 0.09* 0.28*
*p<0.001
31
32
Figure 2-5 sPRI-LUE correlations at each site using bands 10, 12, and 13. Unit of LUE:
gC/MJ. The blue lines are regression lines of the scatter points in each subplot.
Most of the correlations between sPRI and LUE are weak, as shown in Figure 2-5 and Table 2-4.
Though π ππ πΌπ΅13 correlates well with LUE at some sites and the π ππ πΌπ΅13-LUE correlations are
significant, the scatter points are dispersed and the correlations can induce bias in creating the LUE
map and GPP estimation.
In establishing PRI-LUE correlations, several problems were found:
1) Though the correlations of MODIS PRI and LUE have been tested at several sites (Drolet et al.,
2008; Goerner et al., 2009), a generic relationship between the two parameters cannot be
established. In this chapter, generic relationships of PRI-LUE for each plant functional type were
proposed to be established. However, the regressions are weak and cannot support further GPP
estimation based on the LUE derived from MODIS PRI. The locations and climate conditions of
sites were not considered. In establishing unique PRI-LUE correlations at five sites, GPP
measurements from 10 am to 12 pm at the local time were merged to derive LUE at Terra passing
time but the results were unsatisfying.
2) There are many noisy data points in the MODIS reflectance product but the filtering conditions
failed to exclude the outliers. In establishing PRI-LUE correlations, many PRI outliers exist and
exert negative impacts on the correlations. The data points in the regression are scattered widely
and the regressions are weak. However, the p values indicate that the correlations between PRI
and LUE are statistically significant or informative, indicating a certain value of PRI.
3) LUE, defined as the ratio of gross primary productivity and absorbed photosynthetically active
ration, can be influenced by temperature, moisture, and phenology (Liang et al., 2012). In
establishing PRI-LUE correlations at five sites, the air temperature was considered as an additional
factor but its consideration does not lead to improved results. The weak correlations after
considering environmental factors originate from noisy data in the MODIS reflectance product.
33
2.3.2 Further work for solving the issues
1) In addition to plant functional types, the geographical locations and climate conditions will also
be considered in establishing generic PRI-LUE correlations. GPP at corresponding satellite
passing time will be merged for LUE calculation following the method by Drolet et al. (2008).
2) Some improvements will be made in preprocessing MODIS reflectance data. Instead of using
the MODIS reflectance product, MODIS at-sensor radiance data (MOD/MYD021) will be used.
Besides, the corresponding geolocation data (MOD/MYD03) and cloud mask data
(MOD/MYD35) will be used to generate geolocations and remove the contaminated data,
following the method by Zheng (2017) and Drolet et al. (2008).
3) Environmental factors including air temperature and soil moisture will be considered in
establishing PRI-LUE correlations, by adopting the factors as coefficients in the correlations.
Besides, NDVI will also be adopted to generate PRI*NDVI-LUE correlations following the
method by (Zheng and Chen, 2017).
Overall, some future works are proposed to enhance the correlations between PRI and LUE.
Reliable GPP data can only be estimated if the PRI-LUE correlations are significant and thus the
downscaled πππππ₯25 dataset can be derived.
34
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Carboxylation Velocity (Vcmax) Using the Photochemical Reflectance Ratio. In,
Department of Geography and Planning: University of Toronto
Zheng, T., & Chen, J.M. (2017). Photochemical reflectance ratio for tracking light use efficiency
for sunlit leaves in two forest types. ISPRS Journal of Photogrammetry and Remote
Sensing, 123, 47-61
37
Chapter 3
Leaf chlorophyll content (LCC) as a feasible way for
downscaling πππππ₯25
3.1 Introduction
Leaf chlorophyll is essential to the cycles of carbon, water, and energy between the biosphere and
atmosphere, and to the functioning of terrestrial ecosystems (Croft et al., 2017; Croft et al., 2020).
As a crucial constituent involved in plant photosynthetic processes, chlorophyll is responsible for
harvesting photons and transporting electrons to facilitate the production of biochemical energy,
which supports the Calvin-Benson cycle (Alton, 2017; Porcar-Castell et al., 2014). Many studies
have been carried out to estimate LCC using remotely sensed data, through vegetation indices
(VIs) and biophysical modeling approaches. Leaf reflectance is controlled by the existence of
foliar constituents, including chlorophyll, carotenoids, nitrogen, and water (Ustin et al., 2004). The
spectral signals in the red-edge region (690-750 nm) are particularly sensitive to the changes in
chlorophyll content (Curran et al., 1990). Several narrowband VIs using reflectance bands in the
red-edge region have been tested to estimate chlorophyll content (Dash and Curran, 2004;
Haboudane et al., 2004; Wu et al., 2008). Besides, several studies coupled canopy models and leaf
models to retrieve chlorophyll content, and a two-step model inversion method has been
demonstrated to perform well. Zhang et al. (2008) combined the 4-Scale model (Chen and Leblanc,
1997) and the PROSPECT model (Jacquemoud and Baret, 1990) and retrieved chlorophyll content
at 20 m resolution using airborne hyperspectral data over a boreal landscape. Similar results were
also achieved by Croft et al. (2013) and Croft et al. (2015). As one of the key constituents
associated with plant photosynthetic activities, chlorophyll content can reflect vegetation growth
status and has been found to correlate well with plant photosynthetic capacity. Croft et al. (2017)
found that chlorophyll content strongly correlates with πππππ₯25 for four tree species in a temperate
forest and a global chlorophyll distribution map was later derived by Croft et al. (2020). Luo et al.
(2019) showed that GPP estimation at 124 tower sites (555 site-years) distributed around the globe
is significantly improved with R2 increasing from 0.74 to 0.84 after considering the seasonal
variation of πππππ₯25 at these sites using a global LCC map. Therefore, LCC can capture informative
vegetation traits, and the LCC at 1 km resolution can be a proxy for downscaling the πππππ₯25 product
at about 11 km resolution.
38
Figure 3-1 Typical leaf reflectance spectra, from 400nm to 2500nm. Source: Croft and Chen
(2018).
Besides, VIs have been widely adopted to monitor vegetation greenness (Boegh et al., 2002;
Haboudane et al., 2004; Huete et al., 2002), to track light use efficiency (PeΓUelas et al., 1995),
to estimate canopy nitrogen (Serrano et al., 2002), and to retrieve canopy water content (Ceccato
et al., 2001; Jackson et al., 2004). As one of the widely used VIs, normalized difference vegetation
index (NDVI) was first used to monitor vegetation growth conditions in rangeland (Rouse et al.,
1973), calculated as:
ππ·ππΌ = π πππ β π πππ
π πππ + π πππ
(3-1)
where π πππ and π πππ represent the spectral reflectance measurements obtained in the visible red
and near-infrared regions, respectively. NDVI has been tested to strongly correlate with green leaf
biomass, foliar nitrogen content, and maximum canopy CO2 uptake (Gamon et al., 1995). Jin et al.
(2012) correlated NDVI with πππππ₯ and found an exponential relationship after combining all sites
and similar conclusions were reached by Zhou et al. (2014), showing the capacity of NDVI for
capturing plant growth and photosynthetic status.
39
Therefore, LCC and NDVI can provide useful information for vegetation traits at higher spatial
resolutions than the existing πππππ₯25 product. In this chapter, LCC and NDVI data are used to
downscale the πππππ₯25 product. In Section 3.3, the data preprocessing process and the spatial scaling
algorithm are introduced, using the information of LCC and NDVI, respectively. The validation
and statistical analysis methods are also introduced. In Section 3.4, the downscaled results are
presented and discussed. The conclusions are reached in Section 3.5.
3.2 Data and methods
3.2.1 Data
Five flux sites with available GPP and meteorological measurements of 2018 were selected in this
research, including US-Bi2 (Sanchez et al., 2017-), US-Los (Sulman et al., 2009), US-Rpf
(Ueyama et al., 2019), US-Tw4 (Sanchez et al., 2013-), and US-WCr (Cook et al., 2004). Data of
these five sites were collected from AMERIFLUX ((https://ameriflux.lbl.gov/). The biological and
geographical specifications of these sites are summarized in Table 3-1. The clumping indices were
derived from the product by He et al. (2016), and the LAI data of each site were derived from
GLOBMAP LAI product by Liu et al. (2012). The LAI data were resampled to a daily LAI dataset
with a 1 kmΓ1 km resolution.
Table 3-1 Site specifications for five flux sites
Site Code Longitude Latitude Plant Functional Type Year Clumping Index Peak LAI
US-Bi2 -121.535Β° 38.109Β° CRO 2018 0.70 7.25
US-Los -89.979Β° 46.083Β° WET 2018 0.64 8.18
US-Rpf -147.429Β° 65.120Β° DBF 2018 0.66 3.92
US-Tw4 -121.641Β° 38.103Β° WET 2018 0.69 2.18
US-WCr -90.080Β° 45.806Β° DBF 2018 0.64 6.14
*CRO: croplands, WET: wetlands, DBF: deciduous broadleaf forest
40
Leaf chlorophyll content data were derived from the Medium Resolution Imaging Spectrometer
(MERIS) Terrestrial Chlorophyll Index (MTCI), according to a linear equation outlined in Croft
et al. (2014):
πΏπΆπΆ =πππΆπΌ + 0.16
0.04(3-2)
where the MTCI was originally calculated as (Dash and Curran, 2004):
πππΆπΌ = π 754 β π 709
π 709 β π 681
(3-3)
The MTCI has demonstrated that it could use red edge positions bands to estimate chlorophyll
content over a large spatial area (Dash and Curran, 2006). However, due to lack of data from
MERIS since 2012, in this study, the Sentinel-2 data were used as a substitution. The red edge
position bands of Sentinel-2 have been evaluated to provide estimates of biophysical variables in
Figure 3-2 The correlation between MTCI and measured leaf chlorophyll content. Source:
Croft et al. (2014).
vegetation (Clevers and Gitelson, 2013; Frampton et al., 2013). Hence, the modified MTCI
(πππΆπΌπ) using reference bands of Sentinel-2 was calculated as (Frampton et al., 2013):
πππΆπΌπ = π΅6 β π΅5
π΅5 β π΅4(3-4)
where B4, B5, and B6 center at 665 nm, 705 nm, and 740 nm, with a bandwidth of 30 nm, 15 nm,
and 15nm and a spatial resolution of 10 m, 20 m, and 20 m, respectively. All the images were
41
collected from the Sentinel-2 Multi-Spectral Instrument Level-1C dataset (COPERNICUS/S2) and
resampled to a 1 kmΓ1 km resolution on Google Earth Engine. Only cloud-free images were
selected for further analysis.
NDVI data were collected from the Terra Vegetation Indices 16-Day Global 500 m dataset
(MOD13A1) and resampled to a 1 kmΓ1 km resolution on Google Earth Engine. The SummaryQA
band was used to exclude outliers.
3.2.2 Algorithms for downscaling πππππ₯25
The TROPOMI πππππ₯25 dataset was firstly gap-filled by assigning the nearest available values to the
days with no data available. The TROPOMI πππππ₯25 dataset was assumed to be numerically reliable
and the downscaling factors only provided spatial variation information for distributing the
TROPOMI πππππ₯25 data within each 0.1Β°Γ0.1Β° pixel. The downscaled πππππ₯
25 values were then
obtained by applying a spatial scaling ratio πππ to TROPOMI Vcmax, as described in the
following equation:
πππππ₯βππ = πππ Γ πππππ₯βππ (3-5)
where πππππ₯βππ, πππππ₯βππ represent the downscaled πππππ₯25 with a 1 kmΓ1 km resolution, and
TROPOMI πππππ₯25 with a 0.1Β°Γ0.1Β° resolution, respectively.
The spatial scaling ratios of each pixel were derived as described in the following equations:
πππ πΏπΆπΆπ,π=
πΏπΆπΆπ,π
πΏπΆπΆΜ Μ Μ Μ Μ (3-6)
πππ ππ·ππΌπ,π=
ππ·ππΌπ,π
ππ·ππΌΜ Μ Μ Μ Μ Μ Μ Μ (3-7)
where πΏπΆπΆπ,π and ππ·ππΌπ,π represent LCC and NDVI of that 1 kmΓ1k m pixel; πΏπΆπΆΜ Μ Μ Μ Μ and ππ·ππΌΜ Μ Μ Μ Μ Μ Μ Μ
represent the mean LCC and NDVI of that 0.1Β°Γ0.1Β° pixel, respectively. When calculating the
mean LCC and the mean NDVI, outliers were replaced with the nearest available data in the
0.1Β°Γ0.1Β° pixel. The mean values of all downscaled πππππ₯25 values in 0.1Β°Γ0.1Β° pixels were kept
the same as the TROPOMI πππππ₯25 data. The downscaled πππππ₯
25 data for each site in this study using
42
two methods mentioned above were obtained, after gap-filling the days with missing data using
available πππππ₯25 values of the nearest day.
3.2.3 Evaluation and Statistical Analysis
The Boreal Ecosystem Productivity Simulator (BEPS) was adopted in this study to evaluate the
downscaled πππππ₯25 , and it is described in Chapter 2.3.3.1. The original πππππ₯
25 data and downscaled
πππππ₯25 data from LCC and NDVI were input to BEPS for GPP simulation. To evaluate the
performance of downscaled πππππ₯25 , simulated GPP based on original TROPOMI πππππ₯
25 data,
NDVI-downscaled πππππ₯25 data, and LCC-downscaled πππππ₯
25 data were compared with eddy
covariance (EC) flux GPP measurements, where squared Pearson correlation coefficient (R2),
mean absolute error (MAE) and root mean square error (RMSE) were used. The performance of
downscaled πππππ₯25 was also evaluated using the agreement index ( πΌππ΄ ), which was firstly
proposed by Willmott (1981, 1982) and has been widely adopted in model assessments (Gu et al.,
2002; Wang et al., 2001; Zhou et al., 2016). This index of agreement represents the ratio of the
mean square error and the potential error, taking both R2 and RMSE into consideration. The index
πΌππ΄ is calculated as:
πΌππ΄ = 1 ββ (ππ β ππ)2π
π=1
β (|πβ²π| β |πβ²
π|)2ππ=1
(3-8)
where πβ²π = ππ β οΏ½Μ οΏ½ and πβ²π = ππ β οΏ½Μ οΏ½ ; π is the total number of measurements; ππ and ππ
represent GPP simulated from BEPS and EC GPP measurements, respectively; οΏ½Μ οΏ½ is the mean
value of EC GPP. The value of πΌππ΄ ranges from πΌππ΄ = 0, for no agreement between ππ and ππ,
to πΌππ΄ = 1, for perfect agreement between simulations and observations.
Based on the downscaling factor with higher GPP estimation accuracy, a global 1 km πππππ₯25 map
was produced. To evaluate the spatial distribution and variation within the TROPOMI pixels, the
standard deviation (ππ·0.1π·) of each TROPOMI pixel was calculated and the standard deviations
of all global pixels (ππ·) were averaged, as described in the following equations:
ππ·0.1π· = ββ(πππππ₯π
25 β πππππ₯25Μ Μ Μ Μ Μ Μ Μ )
π1ππ
(3-9)
43
ππ· = βππ·0.1π·π
π0.1π·
(3-10)
where πππππ₯π25 , πππππ₯
25Μ Μ Μ Μ Μ Μ Μ , π1ππ , and π0.1π· stand for the 1 km πππππ₯25 values, the average of 1 km
πππππ₯25 values within the TROPOMI pixel, the total number of 1 km πππππ₯
25 pixels within the
TROPOMI pixel, and the total number of global TROPOMI pixels, respectively.
3.3 Results and discussion
Due to the availability of the πππππ₯25 dataset, the downscaled πππππ₯
25 starts from March 7th, 2018
(the day of year 66) to October 31st, 2018 (the day of year 304). The following analysis and results
are all in the range of the day of year 100 to 300, from the beginning to the end of the growing
season.
3.3.1 The intra-pixel heterogeneity of the TROPOMI πππππ₯25 product
As shown in Figure 3-3, significant intra-pixel heterogeneities exist in the 0.1Β°Γ0.1Β° πππππ₯25 pixels
on the day of year 190, in the middle of the growing season. The red points in the figures are the
relative location of the sites in the TROPOMI pixel. The downscaled πππππ₯25 values were retrieved
by applying the scaling ratio derived from LCC, πππ πΏπΆπΆ, of that day. The blue, green and yellow
pixels represent low, moderate and high πππππ₯25 values of that downscaled pixel. The vertical and
horizontal axes indicate the relative location of each downscaled pixel in the TROPOMI pixel.
US-Bi2 is a crop site, with growing corn on an island in the Sacramento San Joaquin Delta. The
site has deep peat, thus providing ideal environments for farming and the πππππ₯25 values are high in
that region. The site is located at the bottom of that TROPOMI pixel and there are some high
estimates of πππππ₯25 in the middle right of that TROPOMI pixel. The main landcover in that
TROPOMI pixel is crop, but the growing status of various subpixels differs, leading to
heterogeneous downscaled πππππ₯25 values. US-Los is a shrub wetland site with some coniferous and
grassy stands. It is located in the top left of that TROPOMI pixel and the landcover within that
TROPOMI pixel is intensively heterogeneous. Different types of plants have individual growing
patterns and physiological characteristics, which indicates that the variation of πππππ₯25 differs
among plant functional types. The middle right downscaled pixels have lower πππππ₯25 while the top
right downscaled pixels have higher values. US-Rpf is a deciduous broadleaf forest in Alaska. It
44
is located in the middle right of that TROPOMI pixel and there are lower downscaled πππππ₯25
estimates in the left part of that TROPOMI pixel. US-Tw4 is a permanent wetland site with woody
vegetation. It is located at the bottom of that TROPOMI pixel and intensive variation of πππππ₯25 can
be seen from the figure of US-Tw4. Lower values are situated in the left part and higher values
situate in the bottom right corner. US-WCr is a deciduous broadleaf forest site in Wisconsin,
mainly with sugar maple and basswood. It is located in the bottom-left part of that TROPOMI
pixel and lower downscaled πππππ₯25 values cluster around the bottom right corner.
45
Figure 3-3 Intra-pixel heterogeneous π½ππππππ distribution within the TROPOMI pixel of each
site on the closest cloud-free day to the day of year 190. The π½ππππππ values at 1 kmΓ1 km are
downscaled through LCC. The red point represents the relative location of the site (1 kmΓ1
km) in the TROPOMI pixel (0.1Β°Γ0.1Β°).
46
Figure 3-4 Intra-pixel heterogeneous π½ππππππ distribution within the TROPOMI pixel of
each site on the closest cloud-free day to the day of year 190. The π½ππππππ values at 1 kmΓ1
km are downscaled through NDVI. The red point represents the relative location of the site
(1 kmΓ1 km) in the TROPOMI pixel (0.1Β°Γ0.1Β°).
47
3.3.2 The πππππ₯25 seasonal variation
With the downscaled πππππ₯25 using πππ πΏπΆπΆ and πππ ππ·ππΌ , the temporal patterns of πππππ₯
25 at each site
can be generalized. At some sites, the seasonal variation of downscaled πππππ₯25 shows similar
patterns to the TROPOMI πππππ₯25 product, while at other sites, the patterns differ a lot.
As shown in Figure 3-5, in US-Bi2 and US-WCr, the LCC-downscaled and NDVI-downscaled
πππππ₯25 curves intersect at two points with the TROPOMI πππππ₯
25 curve. Interestingly, the
downscaled πππππ₯25 values at sites are higher than the TROPOMI πππππ₯
25 values in the interval
between the two intercepts, which is in the middle of the growing season. The LCC-downscaled
πππππ₯25 values at sites are higher than the NDVI-downscaled values at sites between the intercepts.
Beyond the two intercepts, i.e., before and after the growing season, the opposite situation occurs.
The downscaled πππππ₯25 values are lower than the TROPOMI πππππ₯
25 values. In US-Bi2, the peak
πππππ₯25 values are very high mainly because the soil fertility and the values are low before sowing
in spring and harvesting in autumn. In US-Los, the LCC downscaled πππππ₯25 values are all lower
than the TROPOMI πππππ₯25 values and the seasonal variation of the LCC-downscaled πππππ₯
25 is
similar to that of the TROPOMI πππππ₯25 , indicating the πππ πΏπΆπΆ of this site undulates slightly near
1. The downscaled NDVI πππππ₯25 curve is found to fluctuate moderately near the TROPOMI πππππ₯
25
curve. In US-Rpf, over the period of the day of year 180 to 220, a slight decrease occurs to the
LCC-downscaled πππππ₯25 values while the trends of the LCC-downscaled πππππ₯
25 and the TROPOMI
πππππ₯25 become consistent afterwards. In US-Tw4, a steady upward trend is observed in all the
πππππ₯25 curves, in which the LCC-downscaled πππππ₯
25 curve stays near the TROPOMI πππππ₯25 curve
but the NDVI-downscaled πππππ₯25 curve diverges drastically.
48
Figure 3-5 π½ππππππ seasonal variation of each site from the day of year 100 to 300.
49
3.3.3 Evaluation of downscaled πππππ₯25 at sites
Due to the lack of available πππππ₯25 measurements for validation, the downscaled results were
evaluated indirectly through terrestrial biosphere models. The original and downscaled πππππ₯25 data
were adopted as the input parameter into the BEPS model for GPP simulation.
3.3.3.1 The comparison of GPP simulation results
In Figure 3-6, the vertical and horizontal axes represent the GPP simulation and EC GPP
measurements. The blue, orange, and green points indicate the GPP simulation results with
TROPOMI πππππ₯25 , LCC-downscaled πππππ₯
25 , and NDVI-downscaled πππππ₯25 , and the blue, orange,
and green lines indicate the linear regression of the scatter points, respectively. It can be observed
that at all sites, the regression lines of LCC-downscaled πππππ₯25 scatter points are most aligned with
the 1:1 line and similar results are also exhibited after combining points from all 5 sites, as shown
in Figure 3-7. However, some of the regression lines of NDVI-downscaled πππππ₯25 scatter points
are more aligned with the 1:1 line compared to the original TROPOMI πππππ₯25 ones while some are
less aligned. This is because NDVI catches up to the greenness of plants but the physiological
connection between NDVI and photosynthetic capacity is weak.
50
51
Figure 3-6 GPP scatter plot of each site from the day of year 100 to 300. The Y-axis represents
GPP estimates and the X-axis represents flux measured GPP.
Figure 3-7 GPP scatter plot of all sites from the day of year 100 to 300.
3.3.3.2 The seasonal variation of GPP simulation results
In Figure 3-8, the vertical and horizontal axes represent the daily GPP simulation or GPP
measurements and day of year. The red, orange, blue, and green curves indicate the GPP seasonal
variation of EC measurements, simulation with TROPOMI πππππ₯25 , simulation with LCC-
52
downscaled πππππ₯25 , and simulation with NDVI-downscaled πππππ₯
25 . Overall, the GPP simulation
results with LCC-downscaled πππππ₯25 show most aligned patterns to the EC GPP measurements. In
Figure 3-8 GPP seasonal variation of each site from the day of year 100 to 300. The daily EC
GPP data were merged from half-hourly EC measurements. The daily modeled GPP data
were merged from hourly BEPS GPP simulation results.
53
Figure 3-9 Temporal patterns of the bias of GPP estimated with TROPOMI and downscaled
π½ππππππ
US-Bi2, LCC-downscaled πππππ₯25 data considerably improve the GPP simulation between the day
of year 150 to the day of year 250 and NDVI-downscaled πππππ₯25 data also improve the result to
54
some extent. The underestimates of GPP simulation during the growing season originate from a
lack of consideration of fertilization and irrigation. In US-Los and US-Rpf, significant
overestimates of simulated GPP are observed before the day of year 150, though GPP simulation
results with LCC-downscaled πππππ₯25 are closer to the EC measurements. There are overestimates
of GPP simulation in US-Tw4 and the estimates with LCC-downscaled πππππ₯25 show a gradual
decline from the estimates with TROPOMI πππππ₯25 . In US-WCr, GPP estimates with LCC-
downscaled πππππ₯25 capture the fluctuation of daily variation best. As shown in Figure 3-9, the
temporal patterns of the bias of GPP estimation with TROPOMI and downscaled πππππ₯25 are
presented. Due to the consistency of other parameters except πππππ₯25 in BEPS modeling, the
temporal patterns of the bias are similar. However, the LCC-downscaled πππππ₯25 reduces the bias of
GPP estimates at all sites.
3.3.3.3 Statistical analysis of GPP simulation results
In Table 3-2, the TROPOMI πππππ₯25 data and the downscaled πππππ₯
25 data are used as input of BEPS
to simulate GPP. The simulated GPP data are compared with EC GPP measurements. Squared
Pearson correlation coefficient (R2), mean absolute error (MAE), root mean square error (RMSE),
and the agreement index (πΌππ΄) are calculated for statistical analysis. The results of each site and
combined-all-sites are presented at all sites the GPP simulation results with LCC-downscaled
πππππ₯25 are observed to achieve the least RMSE and MAE, and highest IOA. After combining points
from all sites, LCC-downscaled πππππ₯25 data remarkably improve the simulation results from R2 =
0.80 to R2 = 0.85 and IOA = 0.93 to IOA = 0.95. The RMSE and MAE decrease from RMSE =
2.49 to RMSE = 2.15 gC/m2/d and MAE = 1.74 to MAE = 1.55 gC/m2/d. Although NDVI-
downscaled πππππ₯25 data improve the IOA and RMSE slightly, the same R2 and worse MAE indicate
the limits of NDVI for downscaling. However, the overall improvement of GPP estimates with
LCC-downscaled πππππ₯25 is not large because the improvements are small at four sites except US-
Bi2. Only US-Bi2 has obvious differences between the downscaled and TROPOMI πππππ₯25 values
while the other four sites have the downscaled πππππ₯25 values close to the TROPOMI πππππ₯
25 values,
as shown in Figure 3-5. These small improvements could be mostly because the landscapes over
the available tower flux sites are relatively homogeneous so that the downscaled πππππ₯25 values do
not differ greatly from original values before downscaling. Besides, LAI and CI can affect the
magnitude of simulated GPP (Chen et al., 2012; Liu et al., 2018). Errors in the input data such as
55
LAI and CI to BEPS used to evaluate πππππ₯25 downscaling through simulated GPP would also have
considerable influence on the accuracy of simulated GPP and hence on the πππππ₯25 evaluation.
Table 3-2 Statistical evaluation results of the downscaled π½ππππππ based on GPP simulated
with π½ππππππ before and after downscaling against GPP derived from tower flux
measurements
Site Code πππππ₯25 Source R2 RMSE MAE IOA
US-Bi2
TROPOMI 0.91 3.69 2.37 0.92
LCC-Downscaled 0.92 2.95 1.94 0.95
NDVI-Downscaled 0.91 3.25 2.09 0.94
US-Los
TROPOMI 0.84 1.75 1.32 0.94
LCC-Downscaled 0.87 1.47 1.10 0.95
NDVI-Downscaled 0.85 1.81 1.37 0.93
US-Rpf
TROPOMI 0.84 1.86 1.43 0.94
LCC-Downscaled 0.82 1.80 1.38 0.94
NDVI-Downscaled 0.81 1.92 1.47 0.94
US-Tw4
TROPOMI 0.81 1.80 1.43 0.89
LCC-Downscaled 0.82 1.59 1.37 0.93
NDVI-Downscaled 0.80 2.47 2.18 0.84
US-WCr
TROPOMI 0.79 2.75 2.15 0.92
LCC-Downscaled 0.81 2.53 1.96 0.94
NDVI-Downscaled 0.79 2.64 2.04 0.93
All Sites
TROPOMI 0.80 2.49 1.74 0.93
LCC-Downscaled 0.85 2.15 1.55 0.95
NDVI-Downscaled 0.80 2.47 1.83 0.94
*Unit of RMSE and MAE: gC/m2/d
56
3.3.3.4 GPP responses to πππππ₯25 before and after downscaling
The percentage change of GPP estimates before and after downscaling is related to the standard
deviation of downscaled πππππ₯25 in 0.1Λ pixels, LAI, and the landcover. GPP was estimated at each
1 km pixel using LCC-downscaled πππππ₯25 and then averaged to 0.1Λ pixels, named as the distributed
GPP. GPP estimated using TROPOMI-πππππ₯25 data, i.e., one value for each 0.1Λ pixel, named as the
lumped GPP, was compared with the distributed GPP and thus the percentage change was
obtained. The standard deviation of LCC-downscaled πππππ₯25 was calculated at each 0.1Λ pixel. At
all 0.1Λ pixels of sites, GPP estimations at 0.1Λ resolution decrease by 2-7% after πππππ₯25
downscaling using the LCC method, as shown in Table 3-3.
Table 3-3 The summary of GPP responses to π½ππππππ before and after downscaling
Site Code πππππ₯25 ππ·
πππππ₯25 ππ·
πππππ₯25Μ Μ Μ Μ Μ Μ Μ Μ
Percentage
Change Mean LAI
US-Bi2 37.39 0.32 -2.14 2.29
US-Los 6.54 0.21 -2.03 4.09
US-Rpf 10.76 0.30 -2.16 1.76
US-Tw4 76.29 0.65 -5.33 1.47
US-WCr 8.12 0.23 -7.21 3.00
*SD: Standard Deviation, Unit of πππππ₯25 : Β΅mol/m2/s
The distributed GPP estimates were lower than the lumped GPP at all sites because of the nonlinear
relationship between GPP and πππππ₯25 . As shown in Figure 3-10, the blue curve represents GPP
responses to changes of πππππ₯25 in the BEPS model. The black line is the straightened version of
the blue curve between πππππ₯25 β SD and πππππ₯
25 + SD. The black straight line provides quick
estimations of distributed GPP because the downscaled πππππ₯25 is most located in that range. The
GPP estimates were retrieved using environmental data on the day of year 124 at US-WCr site
with πππππ₯25 ranging from 0 to 80 Β΅mol/m2/s. As πππππ₯
25 gets higher, GPP estimates grow slower.
The downscaled πππππ₯25 within the 0.1Λ pixel fluctuates in a certain range with a mean value equal
to the πππππ₯25 value of the 0.1Λ pixel. However, the downscaled πππππ₯
25 lower than the mean value
57
exerts a greater influence on the change of GPP than the downscaled πππππ₯25 higher than the mean
value. The red point is the middle point of the black straight line, indicating the mean value of
distributed GPP. Therefore, a decrement of GPP estimates can be observed after downscaling and
thus the overestimation of lumped GPP can be corrected.
Figure 3-10 GPP responses to π½ππππππ in the BEPS model. The Y-axis represents the GPP
estimates. Other inputs are kept consistent, using data on the day of year 124 at US-WCr
site. The X-axis represents π½ππππππ values changing from 0 to 80 Β΅mol/m2/s. The blue curve
shows the GPP responses to change of π½ππππππ . The black line is the straightened version of
the curve between mean π½ππππππ β SD and π½ππππ
ππ + SD. The blue point represents lumped
GPP, simulated using π½ππππππ at the 0.1Λ resolution. The red point represents distributed, the
mean of GPP simulated based on the LCC-downscaled π½ππππππ within the 0.1Λ pixel.
58
3.3.4 Applying the downscaling method to regional and global scales
As discussed in Chapter 3.4.3, the LCC downscaled πππππ₯25 achieves better performance in GPP
simulation, indicating LCC as a feasible way to downscale the πππππ₯25 dataset. Therefore, the LCC-
downscaling method was further applied from site to regional and global scales. For the global
map, as shown in Figure 3-12, the distributions of πππππ₯25 values in the original and the downscaled
πππππ₯25 map show similar patterns because the downscaling considers the spatial distribution of
LCC within each TROPOMI pixel while the average πππππ₯25 for the pixel is unchanged. . However,
the global averaged intra-pixel standard deviation is 6.72 Β΅mol/m2/s, showing the heterogeneity in
TROPOMI πππππ₯25 pixels and the spatial variation of the downscaled πππππ₯
25 map, as shown in
Figure 3-11. For the regional maps of North America, as shown in Figure 3-13, Figure 3-14, and
Figure 3-15, details can be observed in the enlarged portions of regional maps. For example, the
spatial distribution and variation of πππππ₯25 can be clearly captured in the subfigure f) in each figure.
The seasonal variation patterns of πππππ₯25 are also shown in the comparison of the three figures. The
downscaled πππππ₯25 maps preserve the values of the coarse-resolution πππππ₯
25 dataset and
simultaneously contain the within-pixel spatial information of LCC.
Figure 3-11 Spatial distribution of standard deviation of downscaled π½ππππππ values in
TROPOMI π½ππππππ pixels on the day of year 200.
59
Figure 3-12 Comparison of global π½ππππππ maps on the day of year 200. a) The 0.1Β°Γ0.1Β°
TROPOMI π½ππππππ map; b) The downscaled 1 kmΓ1 km π½ππππ
ππ map.
60
Figure 3-13 Comparison of π½ππππππ maps of North America on the day of year 150. a) The 0.1Β°Γ0.1Β° TROPOMI π½ππππ
ππ map; b)
The downscaled 1 kmΓ1 km π½ππππππ map; c) and d) Partially enlarged details of the red labeled region in a) and b); e) and f)
Partially enlarged details of the red labeled region in c) and d).
61
Figure 3-14 Comparison of π½ππππππ maps of North America on the day of year 200. a) The 0.1Β°Γ0.1Β° TROPOMI π½ππππ
ππ map; b)
The downscaled 1 kmΓ1 km π½ππππππ map; c) and d) Partially enlarged details of the red labeled region in a) and b); e) and f)
Partially enlarged details of the red labeled region in c) and d).
62
Figure 3-15 Comparison of π½ππππππ maps of North America on the day of year 250. a) The 0.1Β°Γ0.1Β° TROPOMI π½ππππ
ππ map; b)
The downscaled 1 kmΓ1 km π½ππππππ map; c) and d) Partially enlarged details of the red labeled region in a) and b); e) and f)
Partially enlarged details of the red labeled region in c) and d).
63
3.4 Conclusion
Due to the nature of the heterogeneous land surface, the intra-pixel heterogeneity and the scaling
effect have been widely discussed in previous studies (Chen, 1999; Chen et al., 2013; Garrigues et
al., 2006). However, the heterogeneity in πππππ₯25 derived from remotely sensed data has not yet
been studied. In this chapter, the intra-pixel heterogeneities of a πππππ₯25 dataset derived from sun-
induced chlorophyll fluorescence by (Liu, 2019) are quantitatively investigated. A spatial scaling
method to downscale the πππππ₯25 dataset to a resolution appropriate for comparison with flux
measurements at eddy covariance towers was developed. The spatial scaling ratio (SSR) is
calculated as the proportion of the informative factor in the 1 km pixel and the averaged
informative factor in the TROPOMI pixel. Two separate downscaling factors, leaf chlorophyll
content (LCC) and normalized difference vegetation index (NDVI) were used to provide useful
information at a higher spatial resolution for downscaling. The seasonal variation patterns and
intra-pixel heterogeneities of πππππ₯25 were presented and analyzed. The downscaled πππππ₯
25 with the
two factors mentioned above were evaluated by adopting the downscaled πππππ₯25 into BEPS for
GPP simulation and by validating the GPP simulation results with GPP derived from EC
measurements. The other parameters of BEPS except πππππ₯25 were kept consistent for the evaluation
of downscaled πππππ₯25 . The bias of GPP estimates, temporal patterns of GPP, and statistical analysis
of simulated GPP were presented. Based on the SSR with better GPP estimates, the first global 1
km πππππ₯25 map was produced.
The main conclusions of this chapter are: 1) Intra-pixel heterogeneities of the πππππ₯25 dataset
retrieved from the TROPOMI SIF data at 0.1-degree resolution were significant at selected 5
locations. The seasonal variation pattern of downscaled πππππ₯25 differed considerably from those at
the original coarse resolution at some sites. When adopting the coarse πππππ₯25 data into terrestrial
ecosystem models, the intra-pixel heterogeneity will induce bias in simulation results over tower
flux sites with footprints much smaller than the pixel size. 2) A spatial scaling algorithm was
developed and LCC-downscaled πππππ₯25 achieve the best results in terms of matching simulated
GPP with tower-measured GPP. The downscaling processes were implemented at five sites with
available GPP measurements in 2018. The spatial scaling ratios derived from LCC and NDVI were
experimented and the LCC-downscaled πππππ₯25 improved the GPP simulation most. LCC has been
tested as the proxy of the photosynthetic capacity (Croft et al., 2017). Thus LCC is more
64
physiologically related to plant photosynthetic process than NDVI, indicating that a factor with
tighter connections with plant photosynthetic capacity can better support πππππ₯25 downscaling. On
the contrary, if the informative factor for downscaling is not physiologically connected with plant
photosynthesis, the downscaling will cause error propagation, leading to unreliable results. 3) The
first 1 km πππππ₯25 map was produced following the LCC downscaling method. Within-pixel details
of πππππ₯25 can be observed after the downscaling.
Overall, intra-pixel heterogeneities indeed exist within the πππππ₯25 dataset derived from TROPOMI
SIF measurements. The spatial scaling algorithm is reliable and LCC can provide a feasible method
for downscaling πππππ₯25 datasets and studying the intra-pixel spatial distribution pattern of the
πππππ₯25 dataset. Since LCC datasets are not widely available, NDVI can also be used for
downscaling πππππ₯25 as the first-order approximation, but because NDVI is a surrogate of canopy
structure (e.g., LAI) and leaf greenness, it is not as ideal as LCC, which represents a leaf trait that
is closely linked to leaf photosynthetic capacity.
65
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69
Chapter 4
Summary
4.1 Main conclusions
1) Heterogeneities exist within the pixels of the π½ππππππ product derived from TROPOMI sun-
induced chlorophyll fluorescence measurements. A downscaling algorithm was designed for
downscaling the π½ππππππ dataset.
The intra-pixel heterogeneity of the πππππ₯25 dataset was explored at five tower flux sites in the USA.
Obvious heterogeneity was observed within the TROPOMI pixels over these sites. A downscaling
algorithm was designed to downscale πππππ₯25 by multiplying the TROPOMI πππππ₯
25 with a spatial
scaling ratio (SSR) at a higher resolution. The SSR considered informative downscaling factors,
such as leaf chlorophyll content (LCC) and normalized difference vegetation index (NDVI) in this
study. The SSR of each pixel was calculated as the proportion of the downscaling factor at that 1
km pixel and the averaged downscaling factor at that TROPOMI pixel at 0.1Β° resolution.
2) Leaf chlorophyll content (LCC) can be a feasible method for downscaling the π½ππππππ data
from 0.1Β° Γ 0.1Β° to 1 km Γ 1 km. The LCC-downscaled π½ππππππ data achieve better GPP
simulation results.
The downscaled πππππ₯25 data were evaluated by using the πππππ₯
25 as inputs to the Boreal Ecosystem
Productivity Simulator (BEPS). The GPP simulation with LCC-downscaled πππππ₯25 data was
improved with better statistical metrics. As a proxy of plant photosynthetic capacity (Croft et al.,
2017), remotely sensed LCC can be informative at a higher spatial resolution for downscaling the
πππππ₯25 dataset. The improvement in GPP simulation after downscaling with LCC is not very large
because some of the tower flux sites selected in this study have downscaled πππππ₯25 values close to
the values of the TROPOMI pixels.
3) Downscaling with a factor having tight physiological connections with π½ππππππ can achieve
satisfying results, such as chlorophyll content in this study. Otherwise, it will introduce other
factors, such as NDVI, which may cause error propagation.
70
Although some studies have indicated the correlations between NDVI and πππππ₯25 (Jin et al., 2012;
Zhou et al., 2014), weak physiological connections exist between those two parameters. NDVI
captures the greenness of the vegetation canopy, which is not only influenced by leaf optical
properties by also canopy structure, while πππππ₯25 indicates the maximum photosynthetic capacity
of leaves. The NDVI-based SSR improved the GPP estimates at some sites but worsened the
estimates at other sites, indicating interference of the variation in the canopy structure in the
downscaling process.
4) The first global 1 km π½ππππππ map was produced based on the LCC-downscaling method
satisfactorily tested at sites.
The global downscaled 1 km πππππ₯25 map can capture details and intra-pixel spatial distribution of
πππππ₯25 . The downscaled map comprehensively considers the accurate numerical values of the
coarse-resolution πππππ₯25 dataset derived from TROPOMI solar-induced chlorophyll fluorescence
data and the within-pixel spatial information of LCC derived from Sentinel-2 data. The
downscaling method eliminates the mismatch between satellite-based πππππ₯25 datasets and the
footprint of tower fluxes and this new map will further enhance accurate global GPP estimates.
5) The correlations between PRI and LUE are not as pronounced as expected, though
statistically significant. The PRI-LUE correlations need to be further explored for the
purpose of downscaling π½ππππππ .
The PRI-LUE correlations were experimented for each plant functional types using historical eddy
covariance measurements from 190 sites and for each of the five AmeriFlux sites available for
2018. The MODIS ocean band 11 (centered at 531nm) covers the signal band of PRI (Drolet et al.,
2005; Drolet et al., 2008; Middleton et al., 2016) and band 10 (centered at 488nm), band 12
(centered at 551nm), and band 13 (centered at 667nm) were tested as the reference band of PRI.
However, weak correlations were observed and the PRI-LUE points were widely dispersed from
their regression line. However, the PRI-LUE relationships are still statistically significant (p<0.05)
at most individual sites and for most plant functional types, suggesting that PRI contains
information for πππππ₯25 that may be useful for its downscaling in some ways which need to be
further explored.
71
4.2 Limitation of current work and plan for further work
1) The design of the spatial scaling algorithm
In this study, a spatial scaling factor was adopted in the downscaling process, which was derived
as the proportion of the informative downscaling factor of the 1 km pixel and the averaged factor
of the TROPOMI pixel. However, the surface heterogeneity originates from two sources of
ecosystem heterogeneity, endogenous (biotic) and exogenous (abiotic) one (Chen et al., 2013).
Other factors, such as land cover, will be considered in further optimization of the spatial scaling
algorithm. Besides, temporal filtering will also be introduced to exclude outliers and generate the
seasonal variation of the downscaled πππππ₯25 .
2) The evaluation of the downscaled π½ππππππ
The downscaled πππππ₯25 data were evaluated indirectly by using the downscaled πππππ₯
25 as the input
to the BEPS model for GPP simulation. The simulated GPP data were then compared with EC
GPP measurements for evaluation. Although in the GPP simulation all other parameters except
πππππ₯25 were consistent, the GPP simulation results were not only affected solely by πππππ₯
25 , but also
soil moisture, leaf area index, etc. (Liu et al., 1999; Liu et al., 1997). The LCC-downscaled πππππ₯25
improved the GPP simulation while some overestimations and underestimations were observed.
In further research, ground-based πππππ₯25 data or πππππ₯
25 products at higher resolutions when
available can be used to directly evaluate the accuracy of the downscaled πππππ₯25 .
3) The establishment of PRI-LUE correlations
The correlations of PRI-LUE for nine plant function types and for five sites were weak and could
not generate reliable GPP estimates through the LUE model (Monteith, 1972). Due to the lack of
an ideal reference band of PRI among MODIS reflectance bands, some substituting bands were
used. Many other complicating factors affect the signal of PRI (Gitelson et al., 2017a) and were
not considered in this study. In order to use remotely sensed PRI, careful consideration of the
optional definitions such as LUE formulation is also needed (Gitelson et al., 2017b). As discussed
in Section 2.4.1 and Section 2.4.2, the PRI retrieval and PRI-LUE correlations need to be improved
in further work. Besides, the Fluorescence Explorer (FLEX) mission of the European Space
Agency will be launched in the near future and the fluorescence imaging spectrometer (FLORIS)
72
including a PRI band will be on board. The PRI band has a bandwidth of 500-600 nm and a spectral
resolution of 3 nm (Coppo et al., 2017), which will significantly enhance the quality of remotely
sensed PRI. With the FLEX PRI, better PRI-LUE correlations may be derived for various purposes
including πππππ₯25 downscaling.
73
4.3 References
Chen, J.M., Chen, X., & Ju, W. (2013). Effects of vegetation heterogeneity and surface topography
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