A-SCOPE: AUTOMATING FLUORESCENCE MODELING IN SUPPORT OF FLEX
Jochem Verrelst(1), Juan Pablo Rivera(1), Christiaan van der Tol(2), Federico Magnani(3) Gina Mohammed(4), Jose
Moreno(1)
(1)
Image processing Laboratory (IPL), University of Valencia, Spain, [email protected], [email protected],
[email protected]
(2)
: University of Twente, Netherlands, [email protected]
(3)
University of Bologna, Italy, [email protected]
(4)
P&M Technologies, Sault Ste Marie, Ontario, Canada, [email protected]
ABSTRACT
In support of ESA’s Earth Explorer 8 candidate mission
FLEX (FLuorescence EXplorer), a Photosynthesis
Study has been initiated to quantitatively link
fluorescence to photosynthesis. This led to the
development of A-SCOPE, a graphical user interface
software package that integrates multiple biochemical
models into the soil-vegetation-atmosphere-transfer
model SCOPE. Its latest version (v1.53) has been
successfully verified and was subsequently evaluated
through a global sensitivity analysis. By using the
method of Saltelli [4], the relative importance of each
input variable to model outputs was quantified through
first order and total effect sensitivity indices. Variations
in leaf area index (LAI) and chlorophyll content are
mostly impacting the reflectance and fluorescence
signal. Non-driving variables that can be safely set to
default values have been identified and will facilitate
consolidating SCOPE into an operational and invertible
model.
1. INTRODUCTION
The FLuorescence EXplorer (FLEX), a candidate
mission for ESA’s Earth Explorer 8, will be the first
space mission optimized for estimation of terrestrial
vegetation fluorescence at a global scale. The mission is
proposed to fly in tandem with ESA’s Copernicus
Sentinel-3 satellite. On board FLEX, the Fluorescence
Imaging Sensor (FLORIS), will measure the radiance
between 500 and 800 nm with a bandwidth between 0.1
nm and 2 nm, providing images with a 150 km swath
and 300 m pixel size. This information will improve the
methods for the estimation of classical biophysical
parameters, as well as the introduction of fluorescencerelated products, e.g., fluorescence yield. This will
allow a more thorough study of vegetation physiological
status such as actual photosynthetic activity. Eventually,
this information will improve our understanding of the
way carbon moves between plants and the atmosphere
and how it affects the carbon and water cycles.
Several scientific and industrial studies have been
initiated by ESA to establish scientific benchmarks for
the FLEX mission. One of the scientific studies is the
Photosynthesis Study that considers the potential of
fluorescence for quantifying photosynthesis, vegetation
health, and stress status. To this end, the study involves
development of a soil-vegetation-atmosphere-transfer
(SVAT) model to quantitatively link fluorescence to
photosynthesis. This model will eventually facilitate
fluorescence retrievals from space, e.g., through
inversion against FLEX observations.
Among SVAT models available, the SCOPE (SoilCanopy Observation, Photosynthesis and Energy
Balance) model [7] was selected as baseline model. This
model has been improved and extended with leaf
biochemical sub-models. To facilitate the usability of
SCOPE, and to automate the generation of multiple
simulations, the model has been subsequently integrated
into the framework of the scientific Automated
Radiative Transfer Models Operator (ARTMO)
software package [8], hereafter referred to as
Automated-SCOPE or ‘A-SCOPE’.
Once the (A-)SCOPE model has been successfully
verified, a following requirement is to consolidate
SCOPE into an operational, invertible model that
enables retrieval of the full fluorescence signal from
FLEX observations. Because SCOPE is fundamentally
designed as an energy budget model, its large number of
input variables currently makes it less suitable to be
implemented into an operational processing scheme.
Therefore, a strategy to simplify SCOPE is desirable. A
global sensitivity analysis would be a suitable method to
quantify the relative importance of each input parameter
to model outputs, and can help set safe default values
for those input parameters which are less influential in
driving the fluorescence signal. The objectives of this
paper are twofold: first, it presents the verification of the
latest A-SCOPE model, and second, it presents a
variance-based global sensitivity analysis (GSA) that
quantifies the relative importance of input variables to
fluorescence outputs. Results are subsequently
discussed in view of simplifying the model to make it
implementable into an operational FLEX processing
chain.
5th INTERNATIONAL WORKSHOP ON REMOTE SENSING OF VEGETATION FLUORESCENCE , 22-24 APRIL 2014, PARIS (FRANCE)
2. SCOPE AND A-SCOPE
2.1. SCOPE
SCOPE allows the combined simulation of radiative
transfer including fluorescence, photochemistry, and the
energy balance at canopy level. The model links visible
to thermal infrared radiance spectra (0.4 to 50 μm) as
observed above the canopy to the fluxes of water, heat
and carbon dioxide, as a function of vegetation
structure, and the vertical profiles of temperature.
During the course of the study several improvements
have been implemented, which led in turn to the latest
and final model version with the undertaken study:
SCOPE v1.52. Starting from the published v.121 [7],
the most important modifications are:
• Introduction of the leaf optical model FLUSPECT.
SCOPE uses this model to calculate the irradiance to
fluorescence conversion matrices.
• Update of FLUSPECT with separate fluorescence
spectra for PSI and PSII.
• Hemispherically integrated fluorescence added as
output.
• Coupling with MODTRAN output files.
• The option to include C3 and C4 vegetation
• Empirical calibration of Pulse Amplitude
Modulation (PAM) to the relative light saturation of
photosynthesis as measured with gas exchange
measurements and modeled with [2] under typical
diurnal conditions (referred to as the TB12 model)
and during drought (referred to as TB12-D).
• Addition of the leaf level photosynthesis and
fluorescence model according to [9] and
developments by [3], referred as MD12 model. The
MD12 model contains parameters that are not in the
empirical TB12 model: qLs for fraction of functional
reaction centres and kNPQs for sustained
photoprotection.
• New fluorescence outputs added (Ftotal, Fyield).
2.2. A-SCOPE
ARTMO is a graphical user interface (GUI) software
package that provides all necessary tools for running a
suite of plant radiative transfer models (RTMs), both at
the leaf level and at the canopy level. Its modular
architecture enables relatively easy incorporation of new
RTMs into its structure. SCOPE has therefore been
implemented into the ARTMO framework, known as ASCOPE.
Essentially, A-SCOPE allows the user to: (1)
configure and run SCOPE in a user-friendly way with
options to insert single values, ranges, or imported
external input datasets; (2) simulate and store a massive
quantity of spectra based on a look-up table (LUT)
approach in a relational database; (3) plot groups of
simulated spectra or fluxes (single values) in the same
plotting window with color gradients as a function of
2
input parameters; (4) export simulated spectra and
associated meta-data to a text file for further processing.
Within A-SCOPE, interfaces have been developed
for each SCOPE sub-model, namely: (1) weather
conditions, (2) leaf parameters, (3) leaf biochemical, (4)
canopy parameters, (5) soil parameters, and (6) angular
geometry (Figure 1).
Figure 1: SCOPE's main module.
Each of the sub-models has to be parameterized. This
can be done by accepting the default fixed values,
modifying the value(s), or inserting a range of values.
For a given biochemical model, the corresponding
interface will appear when opening the ‘Leaf
Biochemical’ sub-model. For instance, the MD12 input
window is shown in Fig. 2.
Figure 2: SCOPE's leaf biochemical module: Von
Caemmerer-TB12.
3. VERIFICATION OF A-SCOPE V1.52
During the course of the study, the model was subjected
to various verification rounds, each time producing
improvements. Inputs and outputs were tested on their
validity and consistency. No anomalies were detected in
v1.52 and its performance was successfully validated
against field data. (A-)SCOPE delivers a wide range of
outputs that can be grouped according to the following
output types: aerodynamic resistances, fluxes, radiation,
reflectance spectrum, the surface temperature
distribution in the canopy, fluorescence, vertical profiles
of fluxes and sunlit fractions. A few examples for
simulations as a function of chlorophyll a-b content
(Cab) ranges and leaf area index (LAI) inputs are shown
in Fig. 3.
3
5th INTERNATIONAL WORKSHOP ON REMOTE SENSING OF VEGETATION FLUORESCENCE , 22-24 APRIL 2014, PARIS (FRANCE)
4. GLOBAL SENSITIVITY ANALYSIS
50
0.45
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LAI
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LAI
Net photosynthesis of canopy (µmol*m-2s -1)
fraction of radiation in observation direction
Dimensionless
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Cab
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Fluorescence per simulation for
wavelength of 640 to 850 nm,
with 1 nm resolution, for PSI only
Fluxes: Fluorescence yield (Fyield)
-3
3.2
x 10
5.5
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Fluorescence yield (W*W -1)
4.5
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LAI
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LAI
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10 27.5 45 62.5 80
Cab
Figure 3: Examples of A-SCOPE outputs as a function of LAI
and Cab (chlorophyll a-b): reflectance (a), flux: net
photosynthesis of canopy (b), flux: fluorescence yield (c),
fluorescence: PSI only (d).
Although the various outputs provide additional
valuable information, within the context of FLEX we
are mostly interested in fluorescence outputs. Fig. 4
provides the fluorescence profiles for C3 and C4 species
as a function of Cab and LAI for the three implemented
biochemical models.
Collatz-TB12-D
Collatz-TB12
C3
5
3
3
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5.5
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fluorescence
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fluorescence
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fluorescence
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Wavelength (nm)
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10 27.5 45 62.5 80
Cab
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LAI
4.5
Wm -2µm-1sr-1
MD12
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Wavelength (nm)
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850
0.5
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10 27.5 45 62.5 80
Cab
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Wavelength (nm)
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10 27.5 45 62.5 80
Cab
850
C4
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5
fluorescence
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Wavelength (nm)
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Wm -2µm-1sr-1
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LAI
fluorescence
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Wm -2µm-1sr-1
LAI
fluorescence
Wm -2µm-1sr-1
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0
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4.5
LAI
7
6
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10 27.5 45 62.5 80
Cab
0
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700
750
Wavelength (nm)
800
850
10 27.5 45 62.5 80
Cab
650
700
750
Wavelength (nm)
800
850
Figure 4: Fluorescence outputs as generated by the three
biochemical models for C3 [top] and C4 [bottom] species.
TB12-D and TB12 yield largely the same profiles. The
MD12 model, however, led to considerably lower
fluorescence profiles for C3. But this model consists of
more variables, meaning that it may have to be finetuned to deliver the same profiles. Conversely, for C4
the MD12 fluorescence outputs were considerably more
pronounced than those in TB12, but in principle finetuning regarding MD12 variables (β, kNPQs and qLs)
to account for C4 specifications is required. Overall,
MD12 appears to represent leaf physiology most
rigorously, but the availability of all three models
affords flexibility for a given situation or research
context.
10
27.5
45 62.5
Cab
80
Sensitivity analysis evaluates the relative importance of
each input parameter and can be used to identify the
most (and least) influential variables in determining the
variability of model outputs. This step is crucial for
moving toward a simplified SCOPE model. In contrast
to a local sensitivity analysis that evaluates one factor at
a time, a global sensitivity analysis (GSA) explores the
full input parameter space, and the contribution of each
input parameter to the variation in outputs is averaged
over the variation of all input parameters, i.e., all input
parameters are tested together. In variance-based
methods the output variance is decomposed to the sum
of contributions of each individual input parameter and
the interactions (coupling terms) between different
parameters [5]. Based on the pioneering work of Sobol'
[6], these variance-based sensitivity measures are
calculated:
1. The first order sensitivity index Si measures and
quantifies the sensitivity of model response Y to the
parameter Xi (without interaction terms).
2. The total effect sensitivity index STi measures the
whole effect of the variable Xi, i.e. the first order
effect as well as its coupling terms with the other
input variables.
First order (Si) and total effect (STi) are typically shown
together as they provide complementary information.
The method was further refined by allowing
computation of S i and S T i simultaneously [4]. Its
scheme reduces the model runs to N(k + 2), where N is
number of samples and k is number of variables. The
analysis has been run over the SCOPE biochemical,
leaf, canopy and geometry variables, thereby keeping
soil and weather variables constant. Each variable was
sampled 2000 times according to the sequence of
Sobol'. Of primary interest was analysis of their impact
on fluorescence output, but in principle any output can
be analysed. Prior to applying GSA to fluorescence
outputs, the validity of the method had to be assessed.
To this end, S i and S T i results of reflectance output
were first generated. Both indices were normalized to
facilitate interpretation (Fig. 5). S T i results are
interpreted since they provide information on the total
impact. The generated patterns are in accordance with
published results obtained by PROSAIL [1], with e.g.,
Cab being a driving variable in the visible region. Other
driving variables are LAI, leaf dry matter content, leaf
water content, and solar zenith angle.
Reflectance Si
Reflectance STi
120
120
100
100
80
80
Total SI [%]
Fluxes: Net photosynthesis of
canopy
First Order SI [%]
Reflectance: Fraction of radiation in
observation direction
60
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Wavelength [nm]
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Wavelength [nm]
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2400
5th INTERNATIONAL WORKSHOP ON REMOTE SENSING OF VEGETATION FLUORESCENCE , 22-24 APRIL 2014, PARIS (FRANCE)
Chlorophyll AB content [ug cm-2]
120
Dry matter content [g cm-2]
leaf thickness parameters [[]]
100
First Order SI [%]
leaf water equivalent layer [cm]
senescent material fraction [fraction]
80
maximum carboxylation capacity (at optimum temperature) [umol m-2 s-1]
Ball-Berry stomatal conductance parameter []
60
Extinction coefficient for Vcmax in the vertical (maximum at the top). 0 for uniform Vcmax []
Respiration = Rdparam*Vcmcax []
40
Leaf area index [m2 m-2]
leaf width [m]
20
solar zenith angle [deg]
observation zenith angle [deg]
0
650
700
750
azimuthal difference between solar and observation
angle [deg]
Wavelength
[nm]
800
850
Figure 5: GSA first order and total sensitivity indices for
SCOPE reflectance.
Subsequently, S i and S T i results for fluorescence
outputs were generated, i.e., the broadband fluorescence
signal and the flux Fluorescence yield (Fyield), which is
the hemispherically and spectrally integrated
fluorescence at the top / total absorbed PAR by leaves
(Fig. 6). Results are presented for TB12-D only.
Regarding fluorescence output, the driving variables are
LAI, Cab and maximum carboxylation capacity (Vcmo)
― and they represent more than 90% of S T i .It
underlines the necessity to accurately quantify LAI, Cab
in order to be able linking with the photosynthesisrelated variables. Of secondary importance are solar
zenith angle, and dry matter content, though the latter
plays a major role in determining Fyield. Non-driving
variables are: azimuth difference, observation zenith
angle, respiration, leaf width, and leaf water content,
which suggest that these latter variables can be safely
set to default values.
Fluorescence STi
120
100
100
80
80
Total SI [%]
First Order SI [%]
Fluorescence Si
120
60
40
40
20
0
60
20
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700
750
Wavelength [nm]
800
0
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650
700
Fyield Si
750
Wavelength [nm]
800
850
Fyield STi
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50
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35
Total SI [%]
First Order SI [%]
25
20
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14
Figure 6: GSA first order and total sensitivity indices for
SCOPE TB12-D fluorescence [top] and the flux fluorescence
yield [bottom]. Variable labels are the same as those in Fig. 5.
5. CONCLUSION
GSA proved to be an effective method to identify the
relative importance of input variables for a given model
output. Considering that both surface reflectance and
vegetation fluorescence emission will be the key level-1
products delivered by FLEX, GSA analysis for these
outputs over the (A-)SCOPE v1.53 model (TB12-D) led
to the following findings: Driving variables: maximum
4
carboxylation capacity (Vcmo), dry matter content, leaf
area index, chlorophyll content, solar zenith angle.
Non-influential
variables:
Azimuth
difference,
observation zenith angle, respiration, leaf width, leaf
water content, leaf thickness.
Although not shown, TB12 led to consistent results
but with less impact of Vcmo. For MD12 qLs and
kNPQs are the main driving variables of fluorescence
outputs. It is also to be remarked that results may vary
depending on the required output.
These results suggest that by setting the least
influential variables to fixed values, the SCOPE model
can be considerably simplified. This is desirable when
moving towards the next phase: implementing the
model into an inversion or assimilation processing
chain. As a first step, the simplified model will be
integrated into a FLEX End-to-end simulator that
enables simulation of scenes as if generated by FLEX.
ACKOWLEDGMENTS
This work was undertaken within ESA’s
“FLEX/Sentinel-3 Tandem Mission Photosynthesis
Study” (ESA No. 400106396/NL/AF).
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