Contribution of the Sagres site (Portugal) to the upgrade and validation of the algorithms for the MERIS 4th Reprocessing John Icely*1, 2, Sónia Cristina2,3,Sergei Danchenko2,3, Bruno Fragoso1,2,3 , Priscila Goela2,3, Gerald Moore4 , Alice Newton2,5

In collaboration with Tamito Kajiyama2, Carolina Sá6, Carolina Beltrán, Vanda Brotas6

*E-mail: john.icely@gmail.com

1Sagremarisco Lda., Vila do Bispo, Portugal; 2Center for Marine and Environmental Reseach (CIMA), University of Algarve, Faro, Portugal; 5Facultad de Ciencias del Mar y Ambientales,University of Cadiz, Spain; 4Bio-Optika, UK; 5NILU-IMPEC, Norway; 6MARE-Marine Sciences and Environmental Research Centre, Faculdade de Ciências, Universidade de Lisboa, Portugal.

European Space Agency Living Planet Symposium, Prague, Czech Republic , 9-13 May 2016

Rationale

References

Data and Methods Results

European Space Agency “Technical Assistance for the Validation of MERIS Marine Products at Portuguese and Oceanic and Coastal Sites” ESA contract number (21464/708/I-OL - CALL ID 1048228); “MERIS Validation and Algorithm 4th reprocessing – MERIS Validation Team (MVT) “ (CONTRACT NO: ARG/003-D25/1406/SAGREMARISCO)Sónia Cristina and Priscila Costa Goela are funded by PhD grants from the Portuguese FCT(SFRH/BD/78354/2011 and SFRH/BD/78356/2011, respectively); Alice Newton is funded by EU FP7 project DEVOTES (grant no. 308392), and John Icely is funded by EU FP7 AQUA-USER (grant no. 607325), and Horizon 2020 AquaSpace (grant no. 633476).

Acknowledgements

Rem

ark

s

Standard and Regional API 1 and API 2 algorithms

The matchup analysis for the Standard and Regional API 1 and API 2 algorithms shows:for API1 a tendency for overestimation of MERAPI1 and MLP(Rrs

MER) and MLP(RrsSITU) with respect to TChlaREF;

for API2 a tendency for underestimation of the MERAPI2 and slightly overestimation of the regional products with respect to TChlaREFABS and TChlaREF

HPLC;an increased accuracy of the regional data product with respect to standard deliverables for API1 and API 2;a wide applicability range of the regional algorithm for API1 and API2 along the Portuguese coast;

The inter-comparison analysis of pigment concentration measurements proposed was successfully conducted. Results are encouraging, but there is space forimprovement, namely:

SGM: consider the inclusion of internal standard and an extended period for acetone extraction;FCUL: test extraction with acetone and consider measuring standard mixes in between samples. A new HPLC has now been set up, limits of detection and

quantification have to be evaluated and compared to the old instrument.

The match-up analysis from Sagres for the MERIS sensor provides a useful resource for the validation of products from theOcean Land Colour Instrument on the recently launched Sentinel-3A satellite.

y=0.6899x+0.3903R²=0.93064

y=0.4369x+0.6013R²=0.814440

1

2

3

4

5

6

7

8

0 2 4 6 8 10

Chla(ug/L)

ChlaDHI(ug/L)

FieldsamplesTChla

SGM

FCUL

Linear(SGM)

Linear(FCUL)

y=0.7287x+0.2033R²=0.81738

0

1

2

3

4

5

6

7

0 1 2 3 4 5 6 7 8

FCULChla(ug/L)

SGMChla(ug/L)

FieldsamplesTChla

Sagres data has been used to produce a regional total chlorophyll a product (TChla) using an inversion scheme based on Multi-Layer Perceptron(MLP) neural nets, where the regional results are expressed as MLP(Rrs

SITU) and MLP(RrsMER), and where the quantities in parentheses indicate MLP

inputs from the Medium Resolution Imaging Spectrometer (MERIS) and in situ remote sensing reflectance (Rrs). Additionally, an inter-comparison hasbeen implemented for the determination of algal pigments by High Pressure Liquid Chromatography (HPLC) to guarantee consistency andcomparability of Chla databases for validation of satellite products for the Portuguese coast at two laboratories involved in ocean colour validationactivities in Portugal. The overall aim has been to: quantify single laboratory uncertainties; quantify differences between the two laboratories,considering both HPLC methods and extraction procedures.Further sampling has continued at Sagres since 2012 to validate the bio-optical models for the MERIS 4th reprocessing, with focus on the applicabilityof the OC4Me algorithm for Sagres waters [2]. Recent and historical (data set collected between 2008 and 2012 [3]) in situ data will be reprocessed assoon as the MERIS 4th reprocessing protocol is available to update the bio-optical characterization of Portuguese waters at the Sagres site. Most of thehistorical data has now been published in peer reviewed journals [1,4-6].

Reference Quantities Tested Quantaties

Algal Pigment Index 1 (API 1)

in situ TChla (TChlaREF) MERIS Standard API1 (MERAPI1)Regional MLPs

MLP(RrsMER) MLP(Rrs

SITU)

[TChla] = [MVChla] + [DVChla] +[Chlidea] + [Pheopba] + [Pheopta]Determined by High-PerformanceLiquid Chromatography (HPLC) usingmethodology from [7].

Determined through a polynomialregression based on multipleband ratios of MERIS normalizedwater-leaving reflectances (ρw) [8].

Computed using inversion schemes based onMulti-Layer Perceptron (MLP) neural nets. Theapplicability range of the regional algorithm wasevaluated through a novel detection scheme[9]. Different sets of center wavelengths (413,443, 490, 510, 560 and 665 nm) were tested toderive the regional TChla concentrations fromspace derived values for Rrs.

Algal Pigment Index 2 (API 2)

in situ TChla (TChlaREFHPLC) MERIS Standard API2 (MERAPI2)

Regional MLPs

MLP(RrsMER) MLP(Rrs

SITU)

[TChla] = [MVChla] + [DVChla] +[Chlidea] + [Pheopba] + [Pheopta]Determined by High-PerformanceLiquid Chromatography (HPLC) usingmethodology from [7]. This standard product is estimated with two

neural nets NN. The first NN computesbottom-of-the-atmosphere (BOA) Rrs valuesby removing the atmospheric radiometriccontribution. The second NN utilizes theBOA Rrs to derive the coefficient ofabsorption of phytoplankton pigments at442 nm (aph(442)). The final API2 product isthen computed as:

MERAPI2 = A.aph(442)B; using the same regression coefficients asthe in situ aph.

The regional MLP for retrieving the dataproduct equivalent to API2 has been trainedwith the in situ data collected at the Sagres site(instructions for independent implementationby users are provided as supplemental materialhttp://ocportugal.org/sites/default/files/api2Sgr.pdf).In both cases, Rrs at 490, 510 and 560 nm wereselected as input channels, in agreement withthe reference study [5, 6].

in situ aph (TChlaREFABS)

The API2 in situ equivalent algalpigment index, TChlaREF

ABS, wasestimated, converting aph(442) intoAPI2.

API2=A.aph(442)B; with power-law regressioncoefficients A=21.0 and B=1.04derived from field measurements inthe German Bight and Norwegianwaters [10]. More details about thedetermination are in [11].

Matchup Analysis:Radiometric measurements and water samples: collected between 2008 and

2012 (Stations A, B and C) off Sagres (Fig. 1);Standard API1 and API 2 data: delivered by MERIS orbiting sensor (Table 1);Equivalent quantities: regional inversion scheme using as input both MERIS and

in situ Rrs values (respectively RrsMER and Rrs

SITU, Table 1).Extraction procedure: MERIS Level 2 products and the criteria for the matchup

selection are the same as those presented in [5].

Table 1. Reference and tested quantities used in this study. Fig. 2. Comparison between standard MERAPI1 and equivalent productscomputed with regional algorithms: (a) MLP(Rrs

MER); (b) MLP(RrsSITU); (c) with the

TChlaREF (red, green and blue dots represent Stations A, B and C, respectively).

There is an overestimation of the MERAPI1,MLP(Rrs

MER) and MLP(RrsSITU) with respect to

TChlaREF (Fig. 2). Tested products are lessaccurate at Station A near the coast,whereas improve in Stations B and C.

Possible causes for the reduced quality ofdata products at Station A:

MERAPI1 and MLP(RrsMER): adjacency effects

and limits of the aerosol models, Fig. 2(a-b);MLP(Rrs

SITU): increased complexity ofseawater optical properties, Fig. 2(c);

Fig. 4. Region of applicability of the MLP(RrsMER) for API1 and API2.

(a) (b) (c)There is an underestimation of the

MERAPI2 (especially at higher concentrations)Fig. 3(a,d) and slightly overestimation ofthe regional products Fig. 3(b,c,e,f) withrespect to TChlaREF

ABS and TChlaREFHPLC.

In general, the matchup analysis withTChla revealed higher uncertainties forMERAPI2, MLP(Rrs

MER) and MLP(RrsSITU), as

detailed in Fig. 3 (d-f).

The best agreement between data setswas obtained with MLP(Rrs

SITU) with respectto TChlaREF

ABS and TChlaREFHPLC Fig. 3 (c-f).

Novelty Index Applicability Range

The green shading is where most of the Rrs spectra arewithin the MLP(Rrs

MER) applicability range–cf. Fig. 4.

The regional MLP developed for retrieving the API1 andAPI2 show good applicability in the Atlantic off theIberian Peninsula.

Matchup Analysis

Fig. 3. Comparison between MERISAPI2 and results obtained by applying theregional MLP for the Sagres region (red, green and blue dots representStations A, B and C, respectively).

Novelty Index Applicability Range:A novelty detection scheme [9,12] was used to verify the regional MLP

algorithms for API1 and API2 applicability range. The adopted applicability rangeis based on a novelty index η presented in published works [6, 9].

Intercalibration ExerciseData and Methods Results

Inter-comparison between theHPLC methods used by Sagremarisco(SGM) and MARE (FCUL) Laboratorieswas conducted to guaranteeconsistency and comparability ofChla databases.

12 batches of samples werecollected at three different locations(Fig.5). In addition, two differentmixtures of standard pigments wereanalyzed in each laboratory.

Table 3. Summarized results for the intercalibration exercise.

Mixture of Standards Natural SamplesPrecision Accuracy Precision Accuracy

Coefficient of variation <3% for all the analysed pigments for DHI and SGM.

For FCUL, coefficient of variation <3% for higher concentration mixture, higher uncertainties (<20%) for lower concentration mix.

Considering equally the laboratories for the

reference: Absolute Percent Difference (APD) <9 % for DHI,

on average across all pigments. APD for SGM and FCUL ~10% higher on average

than DHI

Considering the DHI measurements as reference:

FCUL presented better results on average (APD<19%); SGM had higher differences: althoughpresenting APD<16% for Tchla, the APD across allsamples was ~36%, mainly due to discrepancies inTChlc, Car and diadinox.

Coefficients of variation reaching a maximum of

15% for all pigments and methods (except for

Diatox, Perid., and Zeax. for the FCUL method.)

Considering equally the laboratories for the reference:

APD between 8 and 19%, with average similar values for both

sized filters for DHI and FCUL, with SGM having better results

for the larger sized filters.

Considering the DHI measurements as reference:

APD >25% for both SGM and FCUL.

Laboratory HPLC MethodStationary

phase

Column Detector and Monitoring WL

Ps Lc Dc Tc

Manufacturer and model

WL (nm)

FCUL Zapata et al. (2000) C8 3.5 150 4.6 25Shimadzu SPD-M10A VP-DAD

430, 448, 454

SGMWright & Jeffrey

(1997)C18 3 150 4.6 25 Agilent 1200 436, 450

DHIVan Heukelem & Thomas (2001)

C8 3.5 150 4.6 60Shimadzu SPD-M10A VP-DAD

222*, 450, and 665

Differences retrieved for SGM and FCUL, considering only TChl a:

An underestimation was observed for both methods compared to DHI (slightly more accentuated for FCUL with higherChl concentrations) (Fig.6).

Fig. 6. Linear correlation analysis for TChl a determined by DHI, SGM and FCUL (left) and SGM and FCUL (right).

Fig. 5. Geographic location where thesamples were collected.

[1] Goela, P.C., Icely, J., Cristina, S., Newton, A., Moore, G., Cordeiro, C., 2013. Specific absorption coefficient of phytoplankton off the Southwest coast of the Iberian Peninsula: A contribution to algorithm development for ocean colour remote sensing. Continental Shelf Research, v. 52, pp. 119-132. DOI: 10.1016/j.csr.2012.11.009 ISSN: 0278-4343[2] https://earth.eo.esa.int/ocs/envisat/meris/documentation [3] Icely, J., Cristina, S., Goela, P., Moore, G., Danchenko, S., Zacarias, M., Newton, A., 2013. Summary of the project outputs between 2008-2012 for Technical Assistance for the Validation of MERIS Marine Products at Portuguese Oceanic and Coastal Site. Final Report, Annex A 81 pp., Data - Tables 102 pp., Annex B - Dissemination and Education 6pp. and copies of posters & publications. [4] Goela, P.C., Danchenko,S. Icely, J., Lubian, L.M., Cristina, S., Newton, A., 2014. Using CHEMTAX to evaluate seasonal and interannual dynamics of the phytoplankton community off the South-west coast of Portugal. Estuarine, Coastal and Shelf Science, v. 151, pp. 112-123. DOI: 10.1016/j.ecss.2014.10.001 ISSN: 0272-7714 [5] Cristina, S., Moore, G. F., Goela, P.R.F.C., Icely, J.D., Newton, A., 2014. In situ validation of MERIS marine reflectance off the southwest Iberian Peninsula: assessment of vicarious adjustment and corrections for near-land adjacency. International Journal of Remote Sensing, 35(6), 2347-2377. [6] Cristina, S., D'Alimonte, D., Goela, P.G., Kakiyama, T., Icely, J., Moore, G., Fragoso, B.D.D., Newton, A., 2016. Standard and Regional Bio-optical Algorithms for Chlorophyll a Estimates in the Atlantic off the Southwestern Iberian Peninsula. Geoscience and Remote Sensing Letters, IEEE, DOI: 10.1109/LGRS.2016.2529182.[7] Wright S., Jeffrey, S. 1997. Phytoplankton Pigments in Oceanography. UNESCO, Paris, ch. High-resolution hplc system for chlorophylls and carotenoids of marine phytoplankton, . 327–341. [8] Morel ,A., Antoine, D. 2007. Pigment index retrieval in case 1 waters. Laboratoire d’Océanographie de Villefranche, Algorithm Theoretical Basis Document PO-TN-MEL-GS-0005. [9 ]D’Alimonte, D.,Zibordi, G., Kajiyama, T., Berthon, J.-F. 2014. Comparison between MERIS and regional high-level products in European seas. Remote Sensing of Environment, vol. 140, 378 – 395. [10] Doerffer R., Schiller, H. 2007. The MERIS Case 2 water algorithm. International Journal of Remote Sensing, 28, 3-4, 517–535. [11] Goela, P.G., D'Alimonte, D., Cristina, S., Kakiyama, T., Icely, J., Moore, G., Fragoso, B. and Newton, A., 2016. Algal Pigment Index 2 in the Atlantic off the Southwest Iberian Peninsula: comparison of standard and regional algorithms, Ocean Science (in prep).[12] Bishop, C. M.1994. Novelty Detection and Neural Network Validation. IEE Proc. Vision and Image & Sig. Proc.,141, 217–222.

Table 2. HPLC Methods used in the intercalibration exercise.

Figure 1. Sagres study site with sampling stations A, B and C , source: [1].