FLEX / SENTINEL-3 TANDEM MISSION PHOTOSYNTHESIS STUDY – AN
INVESTIGATION OF STEADY-STATE CHLOROPHYLL FLUORESCENCE AND
PHOTOSYNTHESIS IN TERRESTRIAL VEGETATION
Gina Mohammed(1), Alexander Ač(2), Fabrice Daumard(3), Matthias Drusch(4), Alexander Gallé(5,6),
Yves Goulas(3), Federico Magnani(7), Zbyněk Malenovský(2,8), Jose Moreno(9), Julie Olejníčková(2),
Dan Pernokis(1), Uwe Rascher(6), Juan Pablo Rivera(9), Christiaan van der Tol(10),
Wouter Verhoef(10), Jochem Verrelst(9), Antonio Volta(7)
(1)
P & M Technologies, 66 Millwood St., Sault Ste. Marie, Ontario, Canada P6A 6S7,
Email:[email protected]; Email: [email protected]
(2)
Global Change Research Centre AS CR, Belidla 986/4a, 603 00 Brno, Czech Republic,
Email: [email protected]; Email: [email protected]; Email: [email protected]
(3)
Laboratoire de Météorologie Dynamique, Ecole Polytechnique, 91128 Palaiseau cedex, France,
Email: [email protected]; Email: [email protected]
(4)
Mission Science Division (EOP-SME), European Space Agency, ESTEC, Postbus 299, 2200 AG Noordwijk,
The Netherlands, Email: [email protected]
(5)
Bayer CropScience NV, Innovation Center, Technologiepark 38, 9052 Zwijnaarde, Belgium,
Email: [email protected]
(6)
Institute of Bio- and Geosciences, IBG-2: Plant Sciences, Forschungszentrum Jülich GmbH, Leo-Brandt-Straße,
52428 Jülich, Germany, Email: [email protected]; Email: [email protected]
(7)
Forest Ecology Lab, Department of Agricultural Sciences, University of Bologna, via Fanin 46, I-40127 Bologna,
Italy, Email: [email protected]; Email: [email protected]
(8)
School of Biological Sciences, University of Wollongong, Northfields Ave., 2522 Wollongong, NSW, Australia,
Email: [email protected]
(9)
Laboratory for Earth Observation, Department of Earth Physics and Thermodynamics, Faculty of Physics, University
of Valencia, C/Dr. Moliner, 50, 46100 Burjassot, Valencia, Spain,
Email: [email protected], Email: [email protected]; Email: [email protected]
(10)
Department of Water Resources, Faculty of Geo-Information Science and Earth Observation (ITC), University of
Twente, Hengelosestraat 99, P.O. Box 217, 7500 AE, Enschede, Netherlands,
Email: [email protected]; Email: [email protected]
ABSTRACT
The FLuorescence EXplorer (FLEX) would be the first
space mission optimised for remote observation of
steady-state, solar-induced chlorophyll fluorescence
(SIF) in terrestrial vegetation. Within the European
Space Agency’s Phase A/B1 assessment, the
Photosynthesis Study considers the potential of SIF for
quantifying photosynthesis, and assessing vegetation
health and stress status. This report is a broad overview
of the main elements and key findings of this study. The
study has two components: The first developed a
process-based model to quantitatively link steady-state
fluorescence yield to photosynthesis; the other
component evaluated the potential of steady-state
fluorescence as an indicator of vegetation physiological
stress, without requiring calculation of photosynthetic
rates. This dual approach ensures that the full range of
capabilities of the fluorescence signal might be
exploited in a spaceborne mission. The modelling
activity
integrated
state-of-the-art
modules
representative of physiological processes at the
molecular, leaf, and canopy levels to feed the
Automated SCOPE (A-SCOPE) tool. SCOPE – the Soil
Canopy Observation, Photosynthesis Energy fluxes
model, originally developed by C. van der Tol and
colleagues – links top of canopy observations of
radiance with land surface processes, and includes
modules dedicated to chlorophyll fluorescence.
A-SCOPE is a new Graphic User Interface software
package that provides a seamless link between inputs
and outputs required for running SCOPE. The SCOPE
model was expanded to include novel functionalities
and features, such as new leaf biochemical routines for
C3 and C4 species. Outputs include fluorescence and
reflectance spectra, among other products. For the other
major component of the study – the use of SIF in stress
detection – we focused on the stresses of plants induced
by water deficit, low or high temperature extremes, and
nutrient (nitrogen) insufficiency. A random-effects
meta-analysis was done for studies of passively (solarinduced) and actively (laser-induced) measured
chlorophyll fluorescence in detecting stress effects.
Water stress tended to produce a decline in red and farred fluorescence at leaf and canopy levels. The clearest
indicator of temperature stress was the ratio of red to
far-red fluorescence, which declined consistently even
when combining chilling and heat stress measurements.
The ratio was also an effective indicator of nitrogen
`
deficiency, showing a pattern of increase across the
studies examined. Results of the meta-analysis were
used for an analysis of knowledge gaps and to suggest
priorities for future FLEX-based research and
applications. A conceptual framework for the use of
space-based solar-induced fluorescence in stress
detection has been proposed, consisting of the major
steps from goal-setting to conclusions, and also
considering sources of variability & error and
requirements for expertise.
1.
INTRODUCTION
The FLuorescence EXplorer (FLEX) is a candidate
mission in the European Space Agency’s Earth Explorer
8 program, currently undergoing Phase A/B1
assessment [3]. The purpose of FLEX is to measure
steady-state, solar-induced fluorescence (SIF) in
terrestrial vegetation, and it is proposed as a tandem
mission with ESA’s Sentinel-3 satellite.
In that context, the Photosynthesis Study was initiated
in 2012 with the goals of:
(i) developing, testing, and implementing a processbased model to derive photosynthesis from FLEX;
(ii) assessing the potential to use steady-state solarinduced chlorophyll fluorescence as an indicator of
stress effects.
As a photosynthesis indicator, SIF may be envisaged for
use in:
• tracking vegetation responses to stress, and
providing indication of resilience and recovery
from natural or human-induced perturbations;
• quantifying photosynthetic efficiency, i.e., by
serving as a proxy for light use efficiency in
models of gross primary productivity (GPP) and
global carbon models;
• quantifying photosynthetic rate directly through
various
algorithms
relating
SIF
and
photosynthesis.
Photosynthesis is a highly complex process consisting
of many biophysical sub-processes and chemical
reactions [11]. These internal processes are constantly
adjusting their efficiencies in response to the
environment and the overall physiological status of the
plant. Consequently, actual photosynthetic efficiency in
green tissues varies considerably. While these
mechanisms are fairly well understood at the leaf level
and even to a degree at the canopy level, the process
understanding at biome and continental scales is poor,
especially as there is a lack of indicators responsive to
the dynamics of photosynthesis.
Estimating actual photosynthesis from numerical
simulations therefore implies the use of a complex
modelling approach. A key challenge in fluorescence
modelling has been the accurate simulation of light re-
absorption and its impact on fluorescence emission. For
this purpose, empirical methods have been proposed to
correct the fluorescence emission spectrum, but these
can provide results that differ from outputs of physically
based models.
Recognizing the challenges of modelling SIF and
photosynthesis, the study therefore also seeks to identify
simple SIF indicators for the detection of physiological
strain – as an adjunct to the modelling activities.
2.
APPROACH
2.1 Modelling component
Objectives of the modelling component of the study
were to:
(i) conduct a thorough review of models/modules
which link steady-state fluorescence to photosynthesis
at the leaf and canopy levels;
(ii) test & validate promising models/modules against
existing experimental datasets;
(iii) decide on the components of a consolidated
photosynthesis model with modules representing
chloroplast/leaf biochemistry, radiative transfer (RT)
within the leaf, and RT through the canopy;
(iv) implement the consolidated model in a user-friendly
Graphic User Interface (GUI) format, with user
documentation;
(v) perform an initial validation test and error
quantification of the consolidated model;
(vi) perform local (Jacobian) & global (Monte Carlo)
sensitivity testing; and
(vii) investigate whether it is possible to develop
simpler algorithms (e.g., for relating F-GPP) based on
the model.
An initial validation test of the consolidated model was
carried out using a canopy-based dataset of winter
wheat (Triticum turgidum durum, cultivar Daker) from a
measurement campaign conducted in 2010 in Avignon,
France. The time period (March-June) covered roughly
from the end of tillering phase to the end of grain filling.
This site was equipped with a moveable crane (height
21 m), which moved between contiguous field plots.
Vegetation radiance was recorded with the TriFLEX
instrument, a fluorosensor that uses two identical
spectrometers (HR2000+, Ocean Optics, Dudenin, FL,
USA) to record simultaneously the vegetation radiance
and irradiance spectra in the chlorophyll emission band
from 630 nm to 815 nm with a spectral resolution of 0.5
nm (FWHM) and a 0.09 nm/pixel encoding resolution.
A third HR2000+ spectroradiometer (FWHM ~2 nm)
measured alternatively vegetation radiance and solar
irradiance on a broader spectral range from 300 nm to
900 nm. The three spectrometers looked down to
`
vegetation at a nadir angle with a field of view of 2 m
diameter at ground level. Retrieval of reflectance and
fluorescence radiances (F687, F760) were done using
specific models that apply in the small spectral region
around each respective band as described in [1].
2.2 SIF indicators of stress effects
Objectives of the investigation on SIF stress indicators
were to:
(i) conduct a comprehensive literature review and metaanalysis of studies investigating the responses of steadystate fluorescence to the stresses of water deficit,
temperature extremes, and nitrogen deficiency;
(ii) analyze knowledge gaps regarding SIF and the
above stresses;
(iii) propose a conceptual framework for applications of
SIF in stress detection from space.
3.
RESULTS & DISCUSSION
3.1 Model/module selection
Examples of modules linking steady-state fluorescence
to photosynthetic processes at the leaf level are listed in
Tab. 1. Modules that represent radiative transfer at the
leaf and canopy levels are itemized in Tab. 2.
Evaluation of the models in light of the needs of the
study led to selection of a few candidate modules for indepth testing and validation.
A database of 65 datasets assembled for the project
provided data for use in the testing and validation stage.
For testing of leaf-level models, requirements for the
datasets were that they contain quantitative data of
fluorescence and photochemical yields under variable
conditions. Since short- and long-term (i.e., seasonal)
processes could affect yields in different ways, it was
desirable to test models against both short-term
measurements under controlled conditions and longterm measurements under natural conditions.
Module testing and validation revealed that the MD12
leaf module performed best in a goodness-of-fit
comparison between observed photochemical yields
versus predictions by the five top model candidates
(Fig. 1). The MD12, developed by F. Magnani and
colleagues [4, 6], performed well in comparisons
against both short-term and long-term data, hence, it
was chosen as the leaf level module for the consolidated
model. MD12 contains photosynthesis routines for C3
and C4 vegetation, using equations from Farquhar et al.
[2] for C3 and from Von Caemmerer & colleagues for
C4 [18, 19].
For the leaf RT module, Fluspect was selected. This
model is based on the leaf optical model PROSPECT
and allows propagation of fluorescence spectra
originating in PSI and PSII to the leaf surfaces. Fluspect
is a relatively simple model that operates very quickly
due to its fast layer doubling algorithm for RT inside the
leaf. A modification to Fluspect was made in the project
to permit separate contributions of PSI and PSII.
Table 1. Module candidates (process-based examples) linking steady-state fluorescence and photosynthetic processes.
Name/
acronym
Processes
Details
Reference
Language
Code
avail.
Description
avail.
PI contacts
SG95
ETR / Fs
PSII, SS
[10]
Algorithm
--
Full
R.J. Strasser, Univ Geneva, CH
RS98
ETR / Fs
PSII, SS
[9]
Algorithm
--
Full
A. Rosema, EARS, NL
MD09
ETR, A / Fs
PSII, SS
[5]
MatLab
Yes
Full
F. Magnani, UNIBO, IT
MD12
ETR, A / Fs
PSII, SS
[4]
Algorithm
Yes
Full
F. Magnani, UNIBO, IT
TV09
ETR, A / Fs
PSII, SS
[14]
MatLab
Yes
Full
C. van der Tol, ITC, NL
TB12
ETR, A / Fs
PSII, SS
[12]
MatLab
Yes
Full
C. van der Tol, ITC, NL
Processes: A, gross photosynthesis; ETR, electron transport rate; Fs, solar-induced fluorescence.
Details: SS, steady state fluorescence; PSII, only photosystem II contribution to fluorescence.
`
Table 2. Module candidates (examples) for radiative transfer at the leaf or canopy scale. Code and full descriptions
are available for all models.
Name/
acronym
Processes
Level
/Type
Functional
module
Details
References
Language
PI contacts
Fluor
MODleaf
PAR, Chl / Fs,
FR
Leaf
RT
RS98
VFS, AL
[7, 8]
Basic
Y.Goulas, CNRS, FR
Fluspect
PAR, Chl / Fs,
FR
Leaf
RT
RS98
VFS, AL
[16]
Visual Basic
(Fortran,
MatLab)
W.Verhoef, ITC, NL
SCOPE
1.21
PAR, Chl / Fs,
LAI, T, RH,
CO2, N%, Ψ,
GPP, FR,
ρ, canopy
temperature
Leaf/
TOC,
RT
TV08,
MD09
FFS, SL
[5, 15]
MatLab
W.Verhoef, ITC, NL
SCOPE
1.34
PAR, Chl / Fs,
LAI, T, RH,
CO2, N%, Ψ,
GPP, FR,
ρ, canopy
temperature
Leaf/
TOC,
RT
TV08,
MD09,
Fluspect
VFS, AL
-
MatLab
W.Verhoef, ITC, NL
Processes: PAR, photosynthetically active radiation; T, temperature; FR, fluorescence ratio; Fs, solar-induced fluorescence;
GPP, gross primary production; ρ, reflectance; Ψ, soil water potential or content; LAI, leaf area index;
Chl, chlorophyll content; RH, relative humidity(air); N%, leaf N concentration; CO2, air CO2 concentration.
Level/Type: RT, radiative transfer/energy balance; TOC, top of canopy.
Details: FFS, fixed fluorescence spectrum; VFS, variable fluorescence spectrum; SL, symmetric leaf; AL, asymmetric leaf.
Figure 1. Model goodness-of-fit comparison. Comparison of observed photochemical yields vs. predictions by the five
models in Populus x euroamericana and Arbutus unedo leaves. The dashed line corresponds to the 1:1 relationship.
For upscaling to top of canopy, the SCOPE model was
selected. The SCOPE (Soil, Canopy Observation,
Photochemistry and Energy fluxes) model is a vertical
1-D integrated radiative transfer and energy balance
model [15]. It links visible to thermal infrared radiance
spectra as observed above the canopy to the fluxes of
water, heat, and carbon dioxide, as functions of
vegetation structure and the vertical profiles of
temperature. Output of the model is the spectrum of
outgoing radiation in the viewing direction and the
turbulent heat fluxes, photosynthesis, and chlorophyll
fluorescence.
`
The SCOPE model consists of separate modules, which
can be used individually or in combination, allowing
interchange flexibility. For this project, SCOPE
underwent various technical updates to improve its
overall operation (e.g., improved modality, simulation
options, inclusion of a verification input & output
dataset) and documentation [13]. The current version is
SCOPE v1.53.
3.2 A-SCOPE
To facilitate the use of SCOPE, a Graphic User
Interface, A-SCOPE, was developed by the University
of Valencia that automatically runs the whole package
of modules [17]. A-SCOPE makes it convenient to input
and store data, plot groups of spectra, and export data
files for further processing. A comprehensive user
manual and installation guide (A-SCOPE v1.53) has
been prepared to accompany the software.
In addition to the main module MD12 for leaf-level
physiology, the model package also offers the option to
choose alternative leaf-level modules [12], which are
empirically-calibrated algorithms requiring fewer
parameters.
3.3 Model sensitivity tests and preliminary
validation
Local and global sensitivity tests performed with the
consolidated model using Jacobian and Monte Carlo
approaches, respectively [13, 17], indicated which are
the driving and non-driving variables for fluorescence in
the model. This allows certain variables to remain fixed
in order to simplify and speed up the running of the
model.
The Jacobian sensitivity analysis was done for five
combinations of climate zones and plant functional
types. The overall finding was that whilst sensitivity did
vary seasonally with both parameters, irradiance was the
main driver.
Global sensitivity testing indicated the following as
major driving variables: LAI, Chlorophyll content,
Fraction of functional reaction centres (MD12 model),
Maximum carboxylation capacity, Ball-Berry stomatal
parameter, Solar zenith angle, Dry matter content, Leaf
water content, Leaf thickness, and Senescent material
fraction.
A preliminary validation test of SCOPE v1.53 indicated
that the model is able to simulate GPP. It is also able to
simulate the O2-A band feature, but there may be a
tendency to overestimate fluorescence in the O2-B
band. This will require further testing with additional
datasets and fine-tuning of parameter settings.
3.4 Steady-state fluorescence and stress
detection
Results of the meta-analysis of the literature dealing
with water deficit, temperature extremes, and nitrogen
deficiency found that:
•
water deficit generally produces a decline in
red and far-red fluorescence at leaf and canopy
levels;
•
both types of temperature stresses indicate a
decrease in the ratio of red to far-red
fluorescence (stress to control ratio);
•
nitrogen deficit produces an increase in the red
to far-red ratio.
Information gaps with respect to the use of SIF in
detection of physiological strain include:
•
influence of canopy structural complexity;
•
optimal temporal resolution for discriminating
between acclimatory responses and damaging
strain;
•
impact of combined stressors;
•
detectability across
functional types.
species
and/or
plant
Potential sources of variability or errors for the use of
SIF in detection of stress effects were also reviewed.
In general, sources of variability may be categorized as
arising from vegetation factors, environmental factors
(including stresses), and instrumental or data processing
factors (Fig. 2). In remote sensing of SIF, clearly it is
more practical to control instrumental or data processing
methods than vegetation and environmental factors,
necessitating the availability of adequate supporting and
ground-truthing information to assist investigations.
`
Figure 2. Sources of variability in remote SIF measurements and interpretation.
3.5 Conceptual framework for stress detection
In order to use remote SIF for the detection of stress,
there are various components to consider. Major
elements of a conceptual framework (Fig. 3) include:
•
identification of overall goal and target
application(s);
•
prioritization of vegetation sites for analysis;
•
identification of likely stresses, timeframes,
sources of error;
•
selection of SIF indicators;
•
supporting measures (calibration, validation,
processing, interpretation);
In order to support planning for the FLEX mission and
the function of the conceptual framework, a number of
actions and studies relevant to applications are
recommended. These include:
•
prioritization of the applications of greatest
interest to users in government and resource
policymaking, resource management, and the
scientific community;
•
identification of the most responsive SIF
indicators for diverse applications, exploiting
the full spectral emission that would be
provided by FLEX;
•
evaluation, prioritization, and establishment of
vegetation sites for space-based monitoring
and ground-based, airborne, and other
interpretative and ancillary assessments to
support calibration and validation activities;
•
identification and development of suitable
sensors and physiological tools to corroborate
and interpret space-based SIF retrievals;
•
determination of ameliorative measures and
approaches to account for potential sources of
variability and error associated with major
applications of interest;
•
further developments in modelling to account
for 3-D canopy effects on SIF signals.
`
Purpose Application
Conclusions;
Actions
Supporting
measures,
Cal/Val ground, air,
tower, satellite
Options
Conceptual
Framework
Vegetation,
Site - Species,
PFT, Biome,
Ecosystem
Mapping
Limiting
factors
SIF indicators,
Other spectral
features
Stressors,
Timeframes,
Sources of
error
Figure 3. Conceptual framework for the use of remote SIF in stress detection.
4.
SUMMARY & CONCLUSIONS
The Photosynthesis Study has developed and tested an
integrated 1-D vertical model linking steady-state
fluorescence and photosynthesis. The model, which is
an advancement of SCOPE, comprises modules to
represent biochemical processes at the leaf level, and
radiative transfer at the leaf and canopy levels. The
modular structure of SCOPE allows convenient
exchange of modules as advances in physiological
modelling become available. The model is optimized
for user friendliness with the newly created
A-SCOPE Graphic User Interface, which facilitates data
input, handling, storage, and output management. Model
sensitivity testing has been completed, and validations
are on-going.
As a complementary stream of investigation, the study
considered stress applications of steady-state
fluorescence for detection of physiological strain due to
water deficit, temperature extremes, and nitrogen
deficiency. A random-effects meta-analysis found clear
benefits of the red and far-red fluorescence bands for
stress detection. Therefore, the capability to detect this
information is anticipated to be a key benefit of the
FLEX mission over other current missions, as its
FLORIS sensor will be optimized for retrieval of the
full emission spectrum.
Finally, a conceptual framework is presented which
depicts key elements for applying remote SIF in stress
detection. Information gaps and needs were also
considered. Priorities for further study include:
consideration of the full range of potential applications;
identification of the most suitable SIF indicators for
different situations; understanding potential sources of
variability and errors; establishment of ancillary
technologies; and characterization and establishment of
site networks for remote- and ground-based
assessments. Development of calibration and validation
strategies and tools is a pivotal area that demands
focussed attention, so it is promising to see heightened
interest on this topic from the fluorescence scientific
community.
ACKNOWLEDGMENTS
The FLEX/Sentinel-3 Tandem Mission Photosynthesis
Study gratefully acknowledges funding from the
European Space Agency, ESTEC Contract No.
4000106396/12/NL/AF.
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