retrieval of phytoplankton size classes from hyperspectral light absorption measurements wp7...
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Retrieval of phytoplankton size
classes from hyperspectral light
absorption measurements
WP7Emanuele ORGANELLI
BIOCAREX MeetingVILLEFRANCHE SUR MER
24 JANUARY 2014
Objective and First Output
Objective:
Exploiting hyper-spectral measurements of optical properties
to identify changes in the phytoplankton community structure
at the BOUSSOLE site.
Published paper:
Organelli E., Bricaud A., Antoine D., Uitz J. (2013). Multivariate approach for the retrieval of phytoplankton size structure from measured light absorption spectra in the Mediterranean Sea (BOUSSOLE site). Applied Optics, 52(11), 2257-2273.
Partial Least Squares regression (PLS)
PLS: INPUT and OUTPUT
INPUT VARIABLES
Fourth-derivative of
PARTICLE (ap(λ)) or
PHYTOPLANKTON (aphy(λ))
light absorption spectra
(400-700 nm, by 1 nm)
OUTPUT VARIABLES (in mg m-3)
[Tchl a]
[DP] ([Micro]+[Nano]+[Pico])
[Micro] (1.41*[Fuco]+1.41*[Perid])a
[Nano] (1.27*[19’-HF]+0.35*[19’-BF]
+0.60*[Allo])a
[Pico] (1.01*[TChl b]+0.86*[Zea])aa Coefficients by Uitz et al. (2006). J. Geophys. Res., 111, C08005
Multivariate technique that relates, by regression, a data matrix of
PREDICTOR variables to a data matrix of RESPONSE variables.
Plan of the work
1. INPUT and
OUTPUT
2. TRAINING 3. TEST
Organelli et al.
(2013)
REGIONAL data set for PLS training
Data: HPLC pigment and light absorption (ap(λ) and aphy(λ))
measurements from the first optical depth.
MedCAL data set (n=239): data from the Mediterranean Sea only
MedCAL-trained models
1 model each output
variable
Models were trained
including leave-one-
out (LOO) cross-
validation technique
[Tchl a] measured(a)
0.0 1.0 2.0 3.0 4.0 5.0 6.0
[Tch
l a]
pred
icte
d
0.0
1.0
2.0
3.0
4.0
5.0
6.01:1
[Tchl a] measured0.0 1.0 2.0 3.0 4.0 5.0 6.0
[Tch
l a]
pred
icte
d
0.0
1.0
2.0
3.0
4.0
5.0
6.0
[Micro] measured0.0 0.5 1.0 1.5 2.0 2.5 3.0
[Mic
ro]
pred
icte
d
0.0
0.5
1.0
1.5
2.0
2.5
3.01:1
[Nano] measured0.0 0.5 1.0 1.5 2.0
[Nan
o] p
redi
cted
0.0
0.5
1.0
1.5
2.0
[Pico] measured0.0 0.1 0.2 0.3 0.4 0.5 0.6
[Pic
o] p
redi
cted
0.0
0.1
0.2
0.3
0.4
0.5
0.6
MedCAL aphy(λ)-models
[Micro] measured(e)
0.0 0.5 1.0 1.5 2.0 2.5 3.0
[Mic
ro]
pred
icte
d
0.0
0.5
1.0
1.5
2.0
2.5
3.01:1
[Nano] measured(g)
0.0 0.5 1.0 1.5 2.0
[Nan
o] p
redi
cted
0.0
0.5
1.0
1.5
2.0
[Pico] measured(i)
0.0 0.1 0.2 0.3 0.4 0.5 0.6
[Pic
o] p
redi
cted
0.0
0.1
0.2
0.3
0.4
0.5
0.6
MedCAL ap(λ)-models
R2=0.97RMSE=0.10
R2=0.90RMSE=0.10
R2=0.87RMSE=0.08
R2=0.88RMSE=0.02
R2=0.96RMSE=0.11
R2=0.91RMSE=0.11
R2=0.86RMSE=0.08
R2=0.88RMSE=0.02
MedCAL-trained models: TESTING
BOUSSOLE time-series (NW Mediterranean
Sea): monthly HPLC pigment and light
absorption measurements at the first optical
depth in the period January 2003-May 2011
(n=484).
[Tchl a] measured
0.01
0.1
1
[Tchl a] measured(a)
0.01 0.1 1
[Tch
l a]
pred
icte
d
0.01
0.1
1
1:1
MedCAL aphy(λ)-models
MedCAL ap(λ)-models
[Micro] measured(e)
0.0010.01 0.1 1
[Mic
ro]
pred
icte
d
0.001
0.01
0.1
1
1:1
[Nano] measured(g)
0.0010.01 0.1 1
[Nan
o] p
redi
cted
0.001
0.01
0.1
1
1:1
[Pico] measured(i)
0.0010.01 0.1 1
[Pic
o] p
redi
cted
0.001
0.01
0.1
1
1:1
[Micro] measured0.001
0.01 0.1 1
0.001
0.01
0.1
1
1:1
[Nano] measured0.001
0.01 0.1 1
0.001
0.01
0.1
1
1:1
[Pico] measured0.001
0.01 0.1 1
0.001
0.01
0.1
1
1:1
R2=0.91RMSE=0.17
R2=0.75RMSE=0.14
R2=0.66RMSE=0.12
R2=0.54RMSE=0.046
R2=0.91RMSE=0.17
R2=0.75RMSE=0.13
R2=0.65RMSE=0.12
R2=0.52RMSE=0.047 Good retrievals of Tchl a, DP (not
showed), Micro, Nano and Pico
Similar performances of ap(λ) and
aphy(λ) trained models
Boussole time-series from MedCAL-trained models
Micro
Nano
Pico
Tchl a
Seasonal dynamics of algal size structure at BOUSSOLE
Tchl a
Spring bloom (from mid-March to mid-April)
Low concentrations from June to October
Increase in Winter
Micro-phytoplankton
Max in Spring bloom (from mid-March to mid-
April)
Low concentrations during the rest of the year
Nano- and Pico-phytoplankton
Recurrent maximal abundance in late Winter
and early Spring
Increase in Summer and from October to
December
The PLS approach gives access to the analysis of SEASONAL DYNAMICS of
algal community size structure using optical measurements (absorption).
Retrieval of algal biomass and size structure from in vivo hyper-spectral
absorption measurements can be achieved by PLS:
High prediction accuracy when PLS models are trained and tested with a
REGIONAL dataset (MedCAL and BOUSSOLE);
The dataset assembled from various locations in the World’s oceans
(GLOCAL) gives satisfactory predictions of Tchl a and DP only.
Summary
Main advantage of PLS approach is the INSENSITIVITY of the fourth-
derivative to NAP and CDOM (new analyses reveal it!) absorption
properties that means:
Prediction ability is very similar for ap(λ) and aphy(λ) PLS trained models
This opens the way to a PLS application to total absorption spectra
derived from inversion of field or satellite hyperspectral radiance
measurements
Work to be done (1)
Step 1:
Inversion of in situ HYPER-spectral reflectances. A two-
year time-series (2012-2013) of radiometric measurements
collected at high-frequency (every 15 min) by the buoy at
BOUSSOLE is available for inversion. Validation of retrieved
TOTAL light absorption spectra (399-600 nm with 3 nm
increments) must be performed by comparison with in situ
absorption data (CDOM + particles).
Work to be done (2)Step 2:
Test performances of PLS models when spectral
resolution is reduced. It can be performed with particulate
absorption spectra.
1. To develop PLS models using in situ data within the range
400-700 nm but with 3 nm increments.
2. To develop PLS models using in situ data with 1 nm
increments but within the 400-600 nm range.
3. To develop PLS models using in situ data with 3 nm
increments within the 400-600 nm range
(combination of 1 and 2).
4. Comparison with PLS models (400-700 nm with 1 nm
increments) already published (Organelli et al., 2013).
Work to be done (3)
MERCI!!!
!
Step 4:
Application of the NEW PLS models on the total light
absorption spectra retrieved from inversion of hyper-
spectral reflectances (Step 1).
Step 3:
Training PLS models basing on TOTAL light absorption
measured in situ (399-600 nm with 3 nm increments).
If PLS models are trained with a global dataset...
GLOCAL data set (n=716): HPLC pigment and phytoplankton light absorption measurements (aphy(λ)) from various locations of the
world’s oceans (Mediterranean Sea included).
[Pico] measured(e)
-0.1 0.0 0.1 0.2 0.3 0.4 0.5
[Pic
o] p
redi
cted
-0.1
0.0
0.1
0.2
0.3
0.4
0.51:1
[Nano] measured(d)
0.0 0.5 1.0 1.5 2.0
[Nan
o] p
redi
cted
0.0
0.5
1.0
1.5
2.01:1
[Tchl a] measured(a)
0.0 1.0 2.0 3.0 4.0 5.0 6.0
[Tch
l a]
pred
icte
d
0.0
1.0
2.0
3.0
4.0
5.0
6.01:1
[Micro] measured
0.0 1.0 2.0 3.0 4.0
[Mic
ro]
pred
icte
d
0.0
1.0
2.0
3.0
4.01:1
[DP] measured
0.0 1.0 2.0 3.0 4.0 5.0
[DP
] p
redi
cted
0.0
1.0
2.0
3.0
4.0
5.01:1
[Tchl a] measured(a)
0.0 1.0 2.0 3.0 4.0 5.0 6.0
[Tch
l a]
pred
icte
d
0.0
1.0
2.0
3.0
4.0
5.0
6.01:1
GLOCAL aphy(λ) Trained -models
R2=0.94RMSE=0.11
R2=0.93RMSE=0.08
R2=0.89 RMSE=0.06
R2=0.76RMSE=0.03
R2=0.94RMSE=0.10
...but when we test the models...
Good retrievals of Tchl
a and DP
Overestimation of
Micro
Underestimation of
Nano and Pico
GLOCAL aphy(λ)-models
[Tchl a] measured0.001
0.01 0.1 1
[Tch
l a]
pred
icte
d
0.001
0.01
0.1
1
1:1
[DP] measured
[DP
] pr
edic
ted
0.01
0.1
1
[Pico] measured0.001
0.01 0.1 1
[Pic
o] p
redi
cted
0.001
0.01
0.1
1
1:1
[Micro] measured[M
icro
] pr
edic
ted
0.001
0.01
0.1
1
[Nano] measured0.001
0.01 0.1 1
[Nan
o] p
redi
cted
0.001
0.01
0.1
1
1:1
R2=0.42RMSE=0.044
R2=0.48RMSE=0.13
R2=0.70RMSE=0.23
R2=0.91RMSE=0.17
R2=0.93RMSE=0.14
How to explain differences?
Amplitude and center
wavelength of absorption
bands in the fourth–
derivative spectra at the
BOUSSOLE site are:
Similar to those of the
other Mediterranean
areas.
Different to those of the
Atlantic and Pacific
Oceans.