1. RESULTS-1. Validation of chlorophyll algorithms
Evaluation of phytoplankton functional types algorithms for the complex optical waters of Hudson Bay
Pierre Larouche, Mehmet Yayla, Michel Starr
Maurice Lamontagne Institute, Fisheries and Oceans Canada, Mont-Joli, Québec, G5H 3Z4, Canada;
RESULTS-1. Validation of chlorophyll algorithms
ABSTRACT
Existing Phytoplankton Functional Type (PFT) algorithms have been developed using data collected mostly from open ocean, Case-1 waters. In order to evaluate their potential use in the more optically-complex Hudson Bay waters, we used a series of in situ measurements of size fractionated phytoplankton biomass and pigments composition derived from HPLC analysis together with satellite image matchups (MERIS, MODIS). Two published algorithms were tested: PHYSAT [Alvain et al., 2005, 2008] that evaluates phytoplankton dominant groups and a three-component model [Brewin et al., 2010] that evaluates phytoplankton size class proportions. Results indicate that both models need to be locally adapted to the research area in order to obtain an acceptable accuracy. Comparative analysis of the mismatch between the Brewin et al., 2010 model output and in situ size distributions helped to understand the cause of the algorithm shortcomings and to evaluate if it is possible to adjust its parameters to take into account the optical particularities of Hudson Bay.
Figure 3: Validation of OC4Me and MERIS Algal-2 (Neural network) algorithms.
(a) All years and regions combined for OC4Me, RMSE: 0.53 mg.m-3 ; (b) All years and regions combined for Algal-2, RMSE: 0.65 mg.m-3 ; (c) Hudson Bay stations only for OC4Me RMSE: 0.49 mg.m-3 ; (d) Hudson Bay stations only for Algal-2 RMSE: 0.50 mg.m (e) Hudson Strait stations only for OC4Me RMSE: 0.57 mg.m-3 ; (f) Hudson Strait stations only for Algal-2 RMSE: 0.79 mg.m-3
(a)
(b)
(c)
(d)
(e)
(f)
Both algorithms provide higher correlation for the Hudson Bay stations, with a consistent positive bias (overestimation) which suggests need for a local adaptation of the algorithm coefficients.
In the Hudson Strait (+Foxe Basin) the correlation between the in situ and estimated chl concentrations is lower, particularly for the Algal-2 (neural network) algorithm.
All regions combined, OC4Me (standard empirical algorithm) is found to give more accuracy in estimating the in situ chl concentration.
RESEARCH AREA, MATERIAL AND METHODS
Hudson Bay is a large inland sea for which optical properties are highly affected by freshwater flux from a large watershed resulting in light absorption being dominated by colored dissolved organic matter. In situ data collected during the MERICA (2003 to 2006) and ARCTICNET (2005 and 2010) projects are used to validate and evaluate existing remote sensing PFT and size distribution algorithms in the Hudson Bay. Data consisted of size-fractionated (<5 and 5-20 and >20µm) chlorophyll values (fluorometric method), HPLC pigments measurements and manual species identification.
We selected two PFT remote sensing modeling approaches for this study:
An abundance-based approach for size classification, based on chlorophyll concentration [Brewin et al., 2010; Uitz et al., 2006; Vidussi et al., 2001]
A spectral approach, aiming for species classification [PHYSAT method, Alvain et al., 2005; 2008]
ESA-Envisat’s MERIS data have been used to validate the Brewin et al., 2010 algorithm. PHYSAT method has been validated with MODIS reflectance spectra. We used the BEAM-VISAT interface for data extraction and image processing.
(Standard atmospheric correction is used for both sensors)
Figure 1. Study area and sampling stations. The red circle shows the MERICA stations.
RESULTS-2. Application of PHYSAT model
Only 6 stations with reflectance spectra and species distribution matchups: not sufficient to build local thresholds.
Stations with no dominant species have been successfully classified by this method, whereas the classification of the stations with dominant specie(s) were less satisfactory
Station
Dominant Group
PHYSAT output
sta745
Chrysophyceae
Nanoeuc.
sta750
none
sta765
sta770
sta707
Bacillariophyceae
sta706
Chrysop./Flagel.
Table 2. PHYSAT results
The application of the method and an adaptation of the threshold values to HB system is only feasible if more local data on species distribution can be gathered.
Figure 4: Application of PHYSAT method: locating the nLw* (anomaly) spectra of 6 Hudson Bay stations with respect to global thresholds.
RESULTS-3. Size distribution (Brewin et al., 2010 model)
Analysis of diagnostic pigments (Vidussi et al. 2001; Uitz et al., 2006) is the basis for the Brewin et al., 2010 model In order to apply this method successfully, the following basic equation should be coherent with the in situ data:
where [C]= chlorophyll, [W] = {1.41; 1.41; 1.27; 0.35; 0.6; 1.01; 0.86} and [P] = {fucoxanthin; peridinin; 19_-hexanoyloxyfucoxanthin; 19_-butanoyloxyfucoxanthin; alloxanthin; chlorophyll-b and divinyl chlorophyll-b; zeaxanthin}.
Fig. 5 shows a high correlation between in situ chl and the chl value reconstructed from Vidussi/Uitz equation output indicating that the method could be applied in Hudson Bay.
The deviation from x=y line –especially for 2010 data- suggests a recalibration of the equation coefficients for local application.
Source for the discrepancy is probably the distinctive light absorption properties and pigments of Hudson Bay phytoplankton species (see poster by Xi et al.)
DATA
Figure 5: Validation of the basic equation with
in situ pigment data. (N=141, r2=0.86)
Figure 6: Application of Brewin et al., 2010 method with (a) in situ chl; (b) Meris OC4Me chl data (N=23)
(a)
(b)
Fig. 6 shows that when the measured in situ chl concentration is used as input, the method provides more accurate results than when satellite remote sensing chlorophyll (OC4Me) is used. This is because the method relies primarily on the accurate estimation of chlorophyll concentration from remote sensing data.
In order to obtain more accurate results, the coefficients of the Brewin et al., 2010 method could be calibrated for local estimations in the Hudson Bay system, as suggested by Fig. 5.
The Brewin et al., 2010 method estimates size fractionation from chl concentration. Originally size thresholds are <2µm for picoplankton, 2–20µm for nano- and >20µm for microplankton. Our comparison was made with >5µm data as a proxy to nano+microplankton (>2µm) with potential effect on the accuracy on the comparison.
in situ & satellite
chl matchups
size fractd in situ & satellite chl matchups
All in-situ samples with pigment
analysis
Species analyzed in situ/ satellite matchups
(PHYSAT) N 31 23
141 6
in situ min
(mg.m-3)
0.14
0.034
0.21
in situ max (mg.m-3)
3.0
5.45
0.25
MERIS OC4Me min (mg.m-3)
0.16
0.30 -
MERIS OC4Me max (mg.m-3)
2.86
0.87
MERIS Algal-2 min (mg.m-3)
0.28
0.39
MERIS Algal-2 max (mg.m-3)
3.08
0.80
Figure 2. Size fractionation
(where3 size classes available)
All matching-up data, N=23
Table 1. Structure and range of the data & matchups
This work was made possible with the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), the Department of Fisheries and Oceans Canada and the Canadian Space Agency. We are grateful to the crews of the Canadian Coast Guards Ships for their dedication supporting science activities. Special thanks to Michel Gosselin for providing the Arcticnet size fractionated chlorophyll data.
ESA, ENVISAT mission and MERIS teams are acknowledged for providing and processing MERIS data. NASA Goddard Space Flight Center and are acknowledged for providing and processing MODIS data
ACKNOWLEDGEMENTS
CONCLUSIONS
None of the operational chl-a algorithms provide accurate estimations for Hudson Bay.
The accuracy of satellite chlorophyll estimations should thus be improved in order to obtain useful remote sensing information on PFT and size distribution.
An extensive remote sensing reflectance spectra & in situ species analysis (cell count) data would be necessary to locally adapt and validate PHYSAT method.
The Brewin et al., 2010 method was found to be potentially applicable in Hudson Bay since the diagnostic pigments model (Vidussi et al. 2001; Uitz et al., 2006) provides good correlation between the chlorophyll and pigment data collected.
Local calibration of the Vidussi/Uitz equation could be performed by using multiple regression analyses on in situ chlorophyll and pigment data, without requiring relatively rare satellite matchup data.