AQUAculture USEr driven operational Remote Sensing information services

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1 AQUAculture USEr driven operational Remote Sensing information services Deliverable 3.1 Regional optical algorithms report WI, PML, NIVA, GRAS, SGM AQUA-USERS is funded under the European Community s 7 th Framework Program (Theme SPA : Stimulating development of downstream services and service evolution, Grant Agreement N o )

2 Task 3.1: Consolidation of regional optical inversion algorithm Deliverable 3.1: Regional optical algorithms report Lead beneficiary WI Contributors PML, NIVA, GRAS, SGM Due date 31/07/2015 Actual submission date Dissemination level PU Change record Issue Date Change record Authors /14 Initial outline WI /15 Contributions by GRAS GRAS /15 Contributions by PML, NIVA, SGM and WI PML, NIVA, SGM, WI /15 Editing WI Consortium No Name Short Name 1 Water Insight BV WI 2 Stichting VU-VUMC VU/VUmc 3 Plymouth Marine Laboratory PML 4 Fundação da Faculdade de Ciências da Universidade de Lisboa FFCUL 5 Norsk institutt for vannforskning NIVA 6 DHI GRAS GRAS 7 DHI DHI 8 Sagremarisco-Viveiros de Marisco Lda SGM To be cited as Poser, K., Peters, S., Martinez Vicente, V., Sørensen, K., Ledang, A.-B., Huber, S. and Icely, J. (2015) Regional optical algorithms report, AQUA-USERS Deliverable D3.1, EC FP7 grant agreement no: , 108p. Copyright 2015, the members of the AQUA-USERS consortium. All rights reserved. 2

3 Task objective (from DoW) Develop algorithms that produce water quality information targeted at the regions that host the aquaculture sites Scope of this document The main focus of Task 3.1 is on validating water quality retrieval algorithms for the study areas of AQUA-USERS. For each of the areas (DK, NL, NO, PT) one partner is responsible for identifying gaps and open questions in the available algorithms, for identifying and collating data available for validation and for performing and documenting the validation activities. For the UK areas, PML has validated algorithms in place already; therefore no additional validation activities are performed, but results for the data in the AQUA-USERS archive are presented. The task has been split into two parts: algorithms for MERIS for archive processing and algorithms for the sensors for the NRT phase (case study 3). According to the original plan, Sentinel-3 OLCI is supposed to be used for the NRT phase; however, due to the uncertain launch date of Sentinel-3, additional sensors may need to be considered (including MODIS, VIIRS and possibly MSI and Landsat 8). The validation of WQ algorithms for MERIS is presented in this document. Validation of additional sensors will be performed as part of Task 6.3 (NRT case study 3: daily management using S3 + WISP data) and possibly Task 7.5 (Future sensors and evolution of trends in value adding). Abstract Information on water quality is of pivotal importance for aquaculture. One of the main objectives of the AQUA-USERS project is to make water quality data derived from satellite observations available to the aquaculture sector. As aquaculture operations are often located in coastal areas with complex water types, finding suitable algorithms still poses a challenge, despite the progress that has been made in the last decades in developing water quality algorithms for complex waters. Therefore validation studies have been performed for the five study areas of AQUA-USERS to test and validate suitable processors for deriving water quality parameters from MERIS observations. The water types and specific challenges in the different areas vary considerably - from the highly turbid and very dynamic waters of the Wadden Sea to the highly absorbing waters in the Norwegian fjords strongly influenced by adjacency effects from land. For all areas, in-situ measurements have been collected from various sources for validation of satellite data. Several candidate processors, selected based on knowledge of the areas and previous experience, were tested and compared per area based on the in-situ data. From the results conclusions and recommendations were drawn for processing water quality data within AQUA-USERS and beyond. 3

4 List of abbreviations Abbreviation Description a pig a bp a y Algal_1 Algal_2 APD b p C2R CC CDOM Chl-a FR FUB/WeW HPLC K d ICOL IOPs Pigment absorption Bleached particle absorption Absorption by yellow substance MERIS standard chlorophyll product for case 1 waters MERIS standard chlorophyll product for case 2 waters Absolute percentage difference Particle scattering Case-2 Regional Processor CoastColour Processor Coloured dissolved organic matter Chlorophyll-a concentration Full resolution MERIS processor for coastal waters High Performance Liquid Chromatography Attenuation coefficient Improved Contrast between Ocean and Land Processor Inherent Optical Properties L1, L2, L3 Level 1, Level 2, Level 3 (MERIS processing levels) MAE Mean absolute error MERIS Medium Resolution Imaging Spectrometer MLP Multilayer Perceptron (a type of Neural Network) MR Mean ratio NAP Non-algal particles NN Neural Network QC Quality control R(0) - Irradiance reflectance RL w Water leaving reflectance RMSE Root mean squared error RPD Relative percentage difference RR Reduced resolution Rrs Remote sensing reflectance SOOP Ships of Opportunity SPM Suspended particulate matter 4

5 TChl TSM WISP YS Total chlorophyll Total Suspended Matter Water optics Iterative Suite of Processing algorithms Yellow substance List of related documents Short Description Date D3.3 Deliverable 3.3: HAB detection methods report 03/03/2015 D3.5 Deliverable 3.5: Aquaculture indicators report 01/11/2014 D4.3 Deliverable 4.3: Aquaculture indicators software In progress D5.1 Deliverable 5.1: IS data quality control 01/12/2014 5

6 Table of contents 1 Introduction Regional optical inversion algorithm Regional optical algorithms Overview of algorithms tested in AQUA-USERS Denmark Description of region Overview of processors Validation data and methods Results Conclusion Netherlands Description of region Overview of processors Validation data Validation methods Results Conclusion Norway Description of region Overview of the processors Validation data Results Conclusion Portugal Description of region Overview of processors Validation data Results Conclusion UK Description of region Overview of processors Validation data

7 7.4 Results Conclusion Conclusions Acknowledgments References Appendix: Algorithm flags

8 1 Introduction The AQUA-USERS project aims at providing the aquaculture industry with relevant and timely information from Earth Observation (EO) combined with in-situ measurements. The key purpose of AQUA-USERS is to develop, together with users, a software system that brings together satellite information on optical water quality and temperature with in-situ observations also on optical water quality, temperature and ecological parameters. Additionally, the application will collect relevant weather prediction data and met-ocean data (wind, waves etc.) from external data sources as well as other model data. The application will include a decision support system which will link the observed environmental conditions to a set of (user-defined) possible management options as well as an evaluation of earlier management decisions. The purpose of Work Package (WP) 3 is to define, develop and refine methods to generate the core information on which the AQUA-USERS application will be based: - Water quality products will be derived from EO data with algorithms specifically developed for the regions that host the aquaculture sites (Task 3.1 this document); - The accuracy of available sea surface temperature (SST) products meeting the user requirements (see D2.1) are evaluated (Task 3.2); - Methods for Harmful Algal Bloom (HAB) detection will be developed based on optical satellite information and supporting evidence (Task3.3); - Appropriate supporting information with excellent spatiotemporal coverage will be obtained from models, including those that drive MyOcean and weather forecasts (Task 3.4); - Site-specific indicators for aquaculture suitability will be developed with multivariate statistical analysis (Task 3.5); - The decision support methodology will be developed (Task 3.6). This document first gives an introduction to water quality remote sensing and introduces the different optical inversion algorithms used within AQUA-USERS. Then for the five test regions (Denmark DK, Netherlands NL, Norway NO, Portugal PT, United Kingdom UK), an overview is given of the region, the algorithms used, the available validation data, the validation procedure and finally the validation results and conclusions are presented. 8

9 2 Regional optical inversion algorithm Information on water quality is of pivotal importance for aquaculture. One of the main objectives of the AQUA-USERS project is to make water quality data derived from satellite observations available to the aquaculture sector. Monitoring of some important ecological water quality parameters is feasible based on optical observations from close range and remote sensors (Gons 1999, Sathyendranath 2000). With the advent of medium to high resolution satellites with spectral band sets optimized for optical water quality monitoring, operational satellite monitoring of challenging near coastal waters, lakes and reservoirs is within reach (Dekker, Vos & Peters 2002, Odermatt et al. 2012, Alikas & Reinart 2008). Compared to open oceanic waters where the only relevant optical active component is phytoplankton (also termed case-1 waters), most coastal and inland waters contain significant amounts of other optical active components such as non-algal particles (NAP) and coloured dissolved organic matter (CDOM). Also the variation in optical diversity and amount of phytoplankton is much larger. In case-1 waters, optical phytoplankton concentration estimations, by means of the proxy Chlorophyll-a have reached a state of maturity (O Reilly et al. 1998, Müller et al. 2015). Based on global datasets (Campbell & Reilly 2006), semi-empirical algorithms were designed and fitted to the data that explain the observed variability to a high degree. In contrast to case-1 waters, the optical properties of so-called case-2 waters are significantly influenced by other constituents such as mineral particles, CDOM, or microbubbles, whose concentrations do not co-vary with the phytoplankton concentration. They display discolouring because of additional absorption by NAP and CDOM. Their brightness changes with the backscattering of suspended particles. Optical complexity stems from the fact that absorption curves and sometimes also backscattering curves may have spectral similarity leading to potential ambiguity (Hommersom et al. 2011). Over the years many case-2 algorithms have been proposed to derive one or more optically active components from the observed spectrum. Discussions about algorithm typology, (dis-) advantages, working ranges, local validity vs global validity etc. can be found in recent publications by Odermatt et al. (2012), Blondeau-Patissier et al. (2014) and Brando et al. (2012). Various methods are available for non-linear multivariate model inversion such as neural networks (Doerffer & Schiller 2007, Doerffer 2010) and optimization/curve fitting (e.g. Maritorena, Siegel & Peterson 2002). Another approach is based on look-up tables such as proposed by Van der Woerd & Pasterkamp (2008). Recently iterative schemes have been developed (Yang et al. 2011, Peters et al. 2015). While there is a large number of algorithms available, so far none of them has been proven to be valid over a wide range of complex water types. Therefore, for coastal areas it always advisable to test and validate suitable algorithms before applying them. These validation activities are presented for the five study areas in AQUA-USERS in this document. 2.1 Regional optical algorithms In the following, the processors used in the different studies are introduced. For some areas, local calibration or variations of these processors are used; these are described in the respective chapters MERIS standard products During the lifetime of the MERIS sensor aboard the ENVISAT satellite ( ), the standard algorithms applied to the MERIS data were updated several times (1 st 3 rd reprocessing). The current 9

10 dataset is based on the 3 rd reprocessing using the MEGS (MERIS Ground Segment) 8.1 processor released in For chlorophyll-a, two different algorithms are implemented in MEGS8.1: Algal_1 for case 1 waters and Algal_2 for case 2 waters. Algal_1 uses the OC4 algorithm in the formulation by Morel et al. (2007), a Maximum Band Ratio algorithm which uses the highest of three band ratios to calculate Chl concentrations over a wide concentration range (MERIS Quality Working Group 2011) (Eq.1 and Eq.2): where i Eq. 1 n 1 4 j a n an R log10 Chl 0 log R j max R443, R490, R i 510 Eq. 2 with the coefficients a 0 = , a 1 = , a 2 = , a 3 = , a 4 = In the case-2 water branch, water quality products (namely Algal_2, Yellow_subs and Total_susp) are simultaneously retrieved using a neural network algorithm with a special atmospheric correction procedure for case-2 water applied. This algorithm is based on the C2R processor (see section 2.1.2). The atmospheric correction procedure is based on a neural network, which is trained with simulated reflectances. The basic idea is to associate water leaving reflectances and path reflectances with top of atmosphere reflectances for a large number of different cases of solar and viewing angles, concentrations of different aerosols, concentrations of optical components in water and wind speeds for simulated sky and sun glint. It should be noted that some of the analyses in the following chapters are still based on data from the 2 nd reprocessing (MEGS7.4 processor) MERIS Case 2 Regional (C2R version 1.6.2) An inversion technique based on neural networks (NN) trained with simulated reflectances is used in the MERIS C2R processor to simultaneously retrieve case 2 water constituents (Doerffer & Schiller 2007; Doerffer & Schiller 2008). Prior to the retrieval, a separate NN module performs atmospheric correction, using MERIS L1b geometric and reflectance data. The radiative transfer model HydroLight TM (Mobley 1994) used for the simulations is trained on a large dataset collected mainly in the North Sea, as well as in the Baltic Sea, Mediterranean Sea and North Atlantic. The dataset covered Chl-a values between mg m -3 and on TSM values between g m -3 (Palmer et al. 2015). One special feature of the C2R processor is the combination of a forward NN and a backward NN, which allows testing if the measured spectrum is within the scope of the training set (Doerffer & Schiller 2007). The C2R processor outputs the inherent optical properties (IOPs) of the water of interest, but also the constituents themselves. The IOPs are used to derive the mass concentrations of Chl-a and TSM. Within C2R, Chl-a is calculated with the absorption coefficient of phytoplankton pigments at 443 nm (a_pig_443) as follows (Eq. 3): Chl - a 21* a_pig_443^1.04 Eq. 3 TSM is derived from the total scattering at 443 nm (b_tot_443) as follows (Eq. 4): 10

11 TSM 1.73* b_tot_443 Eq. 4 In contrast to Chl-a and TSM, Kd_490 is a parameter that can be directly derived from remote sensing data. For processing, the Visat BEAM C2R processor version was used with all the default settings. For more details see Doerffer & Schiller (2007) FUB/WeW (FUB) The FUB/WeW processor was developed by the German Institute for Coastal Research (GKSS) 1, Brockmann Consult 4 and Freie Universität Berlin (FUB) 2. The FUB processor is designed for European coastal waters and uses MERIS Level 1b top-of-atmosphere radiances to retrieve the concentration of the optical water constituents (Schroeder et al. 2007). The FUB processor is also based on artificial NN architecture and trained with simulated data. The concentrations used to train the NN ranged from mg m -3 for Chl-a and from g m -3 for TSM. FUB version 2.2 with default settings was used for processing the MERIS data of Danish waters in the Baltic Sea. More details about the processor are given in Schroeder et al. (2007). With the FUB processor there is no option to retrieve inherent optical properties (IOPs), instead the bio-optical constituents are directly provided. Moreover, it is not possible to retrieve Kd_490 with this algorithm CoastColour (CC) The CoastColour project 3 launched by the European Space Agency provides concentrations of optical water constituents derived from different Case 2 algorithms over a global range of coastal waters. For algorithm development, a large range of concentrations was used from 17 different sites around the globe. A detailed description of the different datasets is given here: However, we could not find the exact data range that was used for training and calibrating the algorithm from literature. CoastColour (CC) provides data via an on-demand processing system from which we obtained L2W products (Version 2) (Note that it is also possible to process the data within Visat BEAM). The L2W products contain IOPs, concentrations of water constituents and other optical water parameters (Brockmann Consult et al. 2014). Yet, we were not able to find the formulas describing how the IOPs were translated into mass concentrations as for the C2R processor (see Section 2.1.1). Two different algorithms are used for the CC processing scheme: first an NN-based inversion approach is used that was developed for coastal waters; then the Quasi Analytical Algorithm (QAA) is applied to retrieve the actual IOPs (Lee et al. 2002). CC provides for each pixel three chlorophyll-a concentrations: i) Chl-a is calculated with the clear water OC4 algorithm (O Reilly et al. 1998), ii) Chl-a is derived with a NN for coastal waters (chl_nn) and iii) a blending between the two values (i+ii) is done based on the TSM concentration, which defines the relative portion of the chl_nn (Brockmann Consult et al. 2014). In this study, a comparison with in-situ measurements showed that the chl_nn product developed particularly for coastal waters performs best. Therefore, we used this product in all the analyses described hereinafter. 1 (accessed March 2015) 2 (accessed March 2015) 3 (accessed March 2015) 4 (accessed March 2015) 11

12 2.1.5 WISP algorithm The WISP algorithm (Peters et al. 2015) is a semi-analytical approach that uses an iterative scheme to calculate Chl-a, TSM and CDOM. Its parameterisation is based on the generally accepted SIOP functions used in Hydrolight and in the Coastcolour Round Robin Simulations (Nechad and Ruddick 2012). It uses an iterative scheme, which makes use of separate algorithms to calculate Chl-a, TSM and CDOM. This allows the use of band ratios for Chl-a determination and single band algorithms for TSM and CDOM calculation. The forward model is based on look-up tables and the 4th degree polynomial formulation for the reflectance function proposed by Park and Ruddick (2005). One attractive aspect of the iterative approach is that sub-algorithms can be automatically adapted to the concentration ranges calculated in the previous iteration. From literature we know that low Chl-a concentrations in waters with low TSM are best detected using blue-green band ratios, while in other cases the red-nir band ratio provides better results. Similar choices for spectral bands are known for TSM algorithms. The WISP algorithm is therefore one of the first algorithms that adapts itself to various conditions Improved Contrast between Ocean and Land (ICOL) processor In addition to testing different processors for deriving water quality information from MERIS images, some case studies also evaluated the use of the Improved Contrast between Ocean and Land (ICOL) processor (Santer & Zagolski 2009, Santer 2010). ICOL corrects for the adjacency effect (increased radiance due to scattering and reflection of photons) in MERIS. Adjacency effects have been extensively studied by Santer and Schmechtig (2000), using 5S model calculations to demonstrate that the influence of adjacency is the strongest close to shore but can still be significant up to 20 km offshore, depending on the relative contributions of Rayleigh and aerosol scattering. Where Raleigh scattering dominates, the effects are seen further offshore owing to the higher scale height of Rayleigh scattering compared to that of aerosol scattering. This effect should be corrected by ICOL (Santer, 2010; Santer and Zagolski, 2009), which computes the TOA signal by removing the signal from the adjacent land pixels, thereby providing a product that can be used in ocean colour processors. ICOL is freely available and implemented in the BEAM VISAT software. 2.2 Overview of algorithms tested in AQUA-USERS Area Water quality algorithms Other validation DK NL CC, C2R, FUB (original and regionally calibrated) CC, C2R, WISP Atmospheric correction: CC, C2R, FUB, Adjacency correction with ICOL NO Algal_2 and TSM, C2R, FUB, CC PT Algal_1, Regionally adjusted algorithm ICOL, Vicarious calibration UK Chl-a OC5 (Gohin et al., 2005), CDOM - adg_443_qaa and IOP (Lee et al., 2006), Non organic TSM (Rivier et al., 2012), Kd(490) (Mueller et al., 2000). Table 1: Overview of algorithms tested in the different regions 12

13 3 Denmark 3.1 Description of region The aquaculture facilities in Denmark are located in the western Baltic Sea in the transition area between the North Sea-Skagerrak and the Baltic Sea proper. Most marine aquaculture sites are found in the waters between the north-western and south-western Baltic Sea, but since recently a test facility is also operated near Bornholm (Figure 1). Figure 1: Location of marine aquaculture in Denmark. The region has a high variability in physical, chemical and biological parameters both on spatial (horizontal and vertical) and temporal scales. Being the transition between the oligosaline Baltic Sea proper and the saline North Sea, the waters are characterized by a more or less sharp pycnocline separating the north-going low-saline surface waters from the south-going more saline bottom waters. Close to the coasts and in the fjords (Danish word for estuary) freshwater input does also play a role in stratification. Periodically, strong winds interrupt the stratification this happens in particular during autumn and wintertime. Besides salinity, stratification is also caused by vertical temperature variations caused by both the different origins of the surface and bottom waters and insolation heating the surface waters. Vertical distribution of nutrients and chlorophyll (Chl) follow the stratification. The nutrient concentrations of the surface waters reach their minimum after the spring bloom and do not increase persistently until autumn when nutritious bottom waters are mixed by wind, meaning that primary production is dominated by regenerated production. Shorter periods of nutrient increase do occur after heavy rainfall (near shore and in estuaries) and entrainment of bottom water. The bottom waters are nearly always nutrient rich. The surface Chl follows the usual temperate seasonality with a spring bloom, a pre-summer decline, and one or more maximums during summer-autumn. Blooms that are noxious to aquaculture are 13

14 particularly common in late winter and post the diatom spring bloom. The blooms are formed by flagellates like Dictyocha, Chrysochromulina and Pseudochattonella. The latest large bloom took place in 2011 and was formed by Pseudochattonella farcimen (Eikrem et al. 2009) Eikrem. Fortunately, the bloom occurred before the juvenile fish were inoculated into the fish cages. Earlier blooms of Chrysochromulina and Pseudochattonella in the 1980s and 1990s have caused major fish mortality owing to oxygen depletion and/or algal toxins. During summer-autumn other harmful species are also observed but for many years no events hazardous to aquaculture have occurred. Figure 2: Pseudochattonella farcimen (Eikrem et al., 2009) Eikrem. Photo from Overview of processors Three neural network-based processors for optical Case 2 waters (FUB, C2R, and CoastColour cf Section 2.1) were tested to retrieve bio-optical water quality indicators for the Skagerrak, Kattegat and the Danish part of the Baltic Sea. All three processors are available as plugins in the Visat BEAM software developed by Brockmann Consult 4. With the processors, we derived chlorophyll-a concentration (Chl-a), the diffuse attenuation coefficient at 490 nm (Kd_490) and total suspended matter (TSM) from MERIS full-resolution data which were acquired in 2007, 2009 and We did not use Improved Contrast between Ocean and Land (ICOL) processing, since previous research showed no improvements of the results for the processors tested in this study (e.g., Woźniak et al. 2014). To evaluate the quality of the retrieved water quality indicators and to decide which processor is best suited for the Danish waters in the Baltic area, the retrieved indicators were compared with in-situ measurements. 3.3 Validation data and methods For validation, in-situ measurements of Chl-a, TSM and Kd sampled in the Danish waters in 2007, 2009 and 2011 were used (OverfladevandsDAtabasen ODA). The Chl-a and TSM samples represent mean values of the samples taken in the first 0-2 m of the water column. Kd represents the mean of the entire radiometric profile. The sampling locations are presented in Figure 3. For each measurement, one MERIS pixel corresponding to the geographic location of the in-situ sample was extracted (= match-up). We restricted the match-ups to samples taken max. three hours before or after the MERIS overpass. Once all the match-ups were compiled, the data were quality checked. All MERIS pixels with certain flags were thereby removed as specified in Table (accessed March 2015) 14

15 Processor Removed Flag Explanation C2R FUB CC atc_oor case2_whitecaps land sunglint suspect suspect atm_out l1b_suspect l1b_land l1p_cc_coastline l1p_cc_glintrisk l2r_cc_reflec_suspect Output path radiance reflectance or transmittance from atmospheric correction is out of range. Set if wind speed is higher than 12 m/s. Note that white capping starts at wind speeds around 7 m/s. Large white patches, which may have a significant influence on the reflectance of the ocean, occur at wind speeds above 11 m/s. In coastal waters, the foam coverage can be influenced also by the concentration of organic material and thus may be formed even at lower windspeeds. Pixel is over land Sunglint detected in pixel Pixel is suspect Pixel is suspect Unrecognized output from atmospheric correction Pixel is suspect (flag provided in the L1b product) Pixel is over land (flag provided in the L1b product) Pixel is over coastline (flag provided in the L1P product) Sun glint detected in pixel (flag provided in the L1P product) Pixel is suspect (flag provided in the L2R product) Table 2: MERIS pixels with the listed flags were removed from the match-up database. Information for the C2R processor is gathered from Doerffer & Peters 2006, for FUB from and for CC from Information on CC products can be found at (accessed March 2015). for C2R processed data, all records flagged as atc_oor (atmospheric correction neural network out of range), case2_whitecaps, land, sunglint or suspect were removed. For CoastColour, pixels flagged as l1p_cc_coastline, l1p_cc_glintrisk, l1b_land, l1b_suspect or l2r_cc_reflec_suspect were removed and finally, only FUB data without suspect or atm_out flags were used for further analysis. 15

16 Figure 3: Sampling locations of in-situ data (not all variables were sampled at all the locations). Data of stations located closer than 300 m to the shore or in water shallower than -3 m were removed. In addition to the flag values, also distance to coast and bathymetry were taken into account. The data analysis revealed that satellite data recorded closer than 300 m to the coast or in less than 3 m deep waters were impacted by external factors. Therefore, these match-ups were also excluded to minimize potential adjacency effects and influence from the seabed. An example of the impact of coastal pixels on MERIS-derived Kd is given in Figure 4. Figure 4: Scatter plot showing in-situ versus MERIS C2R Kd data (original uncalibrated output) and the impact of coastal MERIS measurements on Kd 490. Data represent the inner Danish waters incl. Kattegat and Skagerrak of 2007, 2009 and 2011 (n = 884; only match-ups flagged as suspect were removed otherwise all data are shown). 16

17 The quality control of the data also revealed a higher uncertainty in satellite data collected during winter months. At high latitudes, in these months there is often not enough light available for the satellite sensor to take reliable measurements. Therefore, we removed all the data collected in January, February, November and December. Processors Parameter Match-up dataset (2007/2009/2011) C2R FUB CC Chl-a TSM Kd All match-ups (n) Quality checked match-ups (n) All match-ups (n) Quality checked match-ups (n) All match-ups (n) Quality checked match-ups (n) Table 3: The number of match-ups per parameter and processor. Numbers are given for the complete match-up datasets as well as for the quality checked (QC) datasets. QC datasets consist of all the match-ups without the match-ups meeting the criteria (flags, date, distance to coast, bathymetry) described in section 2.3. These QC datasets were used in the end for calibration. The numbers in Table 3 show that there is a large difference between all match-ups and the quality checked (QC) ones but also between the different processors. The FUB and CC processors flag data more conservatively than C2R. For example for TSM derived with the CC processor, out of 144 match-ups only 17 remain after the removal of points with insufficient quality, although before QC the CC processor provided the largest amount of data Quantitative and qualitative assessment Differences between satellite-derived and in-situ-measured water quality indicators were quantified using different statistical error metrics: mean absolute error (MAE, Eq. 5) and root mean square error (RMSE, Eq. 6): MAE n i 1 y sat N x insitu, n 2 i 1( ysat xinsitu ) RMSE, Eq. 6 N where x insitu is the field measurement, y sat is the satellite estimation, N is the total number of observations (valid pixels). Besides the quantitative assessment, the results were also qualitatively evaluated by plotting time series for each year and some selected stations. This qualitative evaluation helps to see how well the satellite and in-situ-derived time series follow each other; for instance, algal blooms are clearly visible in the temporal profiles in the form of chlorophyll peaks. Evaluating time series allows seeing the agreement between different temporal profiles, which would be difficult to recognize in a purely quantitative evaluation. Eq. 5 17

18 3.3.2 Calibration to local conditions Since the waters around Denmark are rather complex and bio-optical water constituents derived from general algorithms are often not able to represent this complexity, it is possible to calibrate the variables derived from the processors with in-situ samples, in order to better match the regional conditions. There are two possibilities to do this: 1) with least-squares regression analysis between in-situ measurements and satellite-derived data, and then using the coefficients of the regression equation to modify the retrieved satellite-based values or 2) by using the IOPs directly together with the in-situ data. The C2R and CC processors output the IOPs but not the FUB algorithm. As an example, we show the calibration procedure for C2R Chl-a, but the same procedure applies to derive TSM. To derive Chl-a mass concentration, the total scattering at 443 nm is used. For Chl-a, the C2R algorithm outputs the absorption coefficient for phytoplankton (a_pig_443) which is used to calculate Chl-a concentration. Within Visat Beam Chl-a concentrations are calculated according to Eq *a_pig_443^1.04 Eq. 7. We tested if an adjustment of the coefficients to the conditions in the Danish waters would reduce the difference between in-situ and satellite-derived Chl-a (Figure 5a); the results of this analysis are termed calibrated type Beam. Further, we also applied an exponential model to the data (termed C2R calibrated - Figure 5b). a b Figure 5: Scatterplots of the pigment absorption coefficient (a_pig_443) derived from C2R against in-situ chlorophyll-a. The power regression equation used in the C2R processor is shown in red and a slightly modified version is graphed in blue (a); the exponential equation matching best the data is shown in the figure to the right (b). 3.4 Results The comparison between satellite-retrieved water constituents and in-situ data revealed that the three algorithms are able to provide Chl-a concentrations in the right value range, the mean and median Chl-a concentrations were estimated more or less correctly, but Kd and in particular TSM are highly underestimated (Figure 6). Only two points of the C2R match-up dataset exceed the data range of the calibration for Chl-a and TSM. Chl-a varies between 0.3 and 50.6 µg/l (mean = 2.9, median = 1.4 µg/l). Woźniak et al. (2014) report for the entire Baltic Sea values between µg/l (mean = 3.73 and median = 2.5) calculated from 470 samples. TSM concentrations vary between 1.7 and 96.3 g/m 3 with a mean and median value of 13.6 and 9.7 g/m 3, respectively (C2R match-up dataset, n = 38); they are rather high as compared to other values published for the Baltic Sea (Woźniak et al. 2014). 18

19 Figure 6: Boxplots of the bio-optical constituents derived from MERIS (QC match-up datasets) and corresponding in-situ observations for 2007, 2009 and 2011 (ODA dataset). Mean values are shown as red and outliers as black dots. (Note that the FUB processor does not output Kd). The figures show agreement between satellite and in-situ values in data range, mean and median values for Chl-a, but neither for TSM nor for Kd Chlorophyll-a concentration Statistics for the quantitative comparison between in-situ and satellite-derived Chl-a are summarised in Table 4. The results show that the C2R algorithm performs best in terms of a mean absolute error (MAE) of 1.62 µg/l, whereas the products of the CC algorithm have the lowest Root Mean Square Error (RMSE) with a value of 2.96 µg/l (Table 4 columns showing original values). The highest RMSE was calculated for the FUB products. This indicates that large errors were more numerous in FUB data than in the C2R and CC products. The results of the current study do not support previous findings, where best results for the Baltic Sea were achieved using the FUB processor (e.g., Woźniak et al. 2014; Kratzer & Vinterhav 2010). Our results are likely to be related to the location as we are in the transition zone between North Sea and Baltic Sea and none of the algorithms is specifically calibrated on such properties. Chl-a (µg/l) C2R (n=183) FUB (n=101) CC (n=117) original calibrated original calibrated original (nn alg.) calibrated MAE RMSE Table 4: Validation results of chlorophyll-a. Original refers to the original uncalibrated C2R/FUB/CC processed data; MAE: Mean Absolute Error; RMSE: Root Mean Square Error. exponential model with the IOP a_pig_443. Figure 7 (top row) presents the scatter plots of the uncalibrated data. Well visible are the large errors for higher Chl-a values between satellite-derived and in-situ values for the FUB data (Figure 7 b). 19

20 a b c d e f Figure 7: Scatter plots of in-situ against MERIS-derived Chl-a for the three processors. The lower row shows the calibrated data. A qualitative approach to compare Chl-a data with different processing or sources is to look at time series. For a selection of stations, the locations of which are shown in Figure 8, we plotted the temporal course of Chl-a for 2007, 2009 and 2011 (Figure 9). We selected stations for which we have the best coverage of in-situ measurements. Well visible is the typical rapid increase of algae in early spring (spring bloom) in all the years, followed by a period with low chlorophyll concentrations. In late summer-early autumn, another increase in Chl-a can be observed but mainly in 2007 at the stations ARH and NOR409. The blooms vary in degree of magnitude for different stations, but also for different years. Especially pronounced is the spring bloom in 2011 at the station VEJ This is also the Pseudochattonella bloom we referred to in the introductory section 3.1. All satellitederived products underestimate the peak of this bloom. However, the maximum value of more than 60 µg chlorophyll-a/litre lies also outside the calibration range of the C2R and FUB algorithms algorithms (for CC we were not able to find the precise calibration range - see section 2.1). 20

21 Figure 8: Five measurement locations used for the time series plots shown in Figure 9 and Figure 10. As described under section 3.3.2, to adjust the standard products to the regional conditions, we applied calibration factors to the data. After calibration, the errors are reduced for all products, but most markedly for the C2R processed Chl-a concentrations (Table 4 see columns with calibrated values). The C2R MAE decreased from 1.62 to 1.04 and the RMSE from 3.06 to 1.6 µg chlorophyll/l. Because of the best statistics after calibration, we decided to use the C2R Chl-a product for further analysis. The time series with calibrated C2R values are presented in Figure 10. As can be seen in the figure, the calibrated values are more stable as compared to the original values. The values derived with the type Beam calibration (power equation as used in the default setting but with other coefficients) are on the other hand systematically lower than the in-situ measured chlorophyll-a concentrations. 21

22 Figure 9: Comparison of time series of Chlorophyll-a from the C2R, CC and FUB processors (uncalibrated) and in-situ measurements for different stations in the Danish waters of the Baltic Sea incl. Kattegat and Skagerrak and for the years 2007, 2009 and The locations of the stations are given in Figure 8. C2R: Case 2 Regional version 1.6.2, CC: CoastColour V2, FUB/WeW version 2.2. DOY: Day of Year.

23 Figure 10: Time-series of chlorophyll-a concentration derived from MERIS with the C2R processor; shown are original values as well as calibrated values (see description under section 3.3) and in-situ measurements for selected stations for the years 2007, 2009 and The locations of the stations are given in Figure 8. C2R: Case 2 Regional version 1.6.2, DOY: Day of Year. 23

24 An example of the chlorophyll-a concentrations in the Danish waters on 24 March, 2011 is shown in Figure 11. The MERIS data was processed with the C2R algorithm and a regional calibration was applied to it. The map shows the spring bloom of phytoplankton. As soon as light and nutrients become abundant in spring and at the same time predation from zooplankton is still low, phytoplankton begins to grow rapidly. Such spring blooms occur every year, but the Pseudochattonella sp. algal bloom in the coastal waters around Funen (also see D3.3 HAB detection methods report), which is visible on the map, was an extraordinary event. The extremely high concentrations in March 2011 are also well visible in the time series plots (Figure 9 and Figure 10), especially for the station VEJ with a peak of more than 60 µg chlorophyll/l. Denmark Funen Sweden Figure 11: Chlorophyll-a concentration of the Danish waters on 24 March 2011 derived from MERIS Full Resolution data. The C2R processor was used for the retrieval and a calibration based on in-situ data was applied to better represent the regional conditions Attenuation coefficient Kd Statistics for the quantitative comparison between in-situ and satellite-derived Kd are summarised in Table 5. Kd was derived with the C2R and the CC processors, FUB does not ouput Kd. For the CCretrieved Kd, the MAE is slightly lower (0.15) than for the C2R Kd (0.18). For both processors, the calibration to the local environment reduces the errors, in particular the results of the CC processor are substantially improved: the MAE is reduced from 0.15 to 0.07 m -1 (Table 5 see columns with calibrated values). Kd (m -1 ) C2R (n=544) CC (n=343) original calibrated original calibrated MAE RMSE Table 5: Validation results of Kd. Original refers to the original uncalibrated C2R/CC Kd data, Calibrated refers to the calibrated data (see section 3.3). MAE: Mean Absolute Error; RMSE: Root Mean Square Error. (Note that the FUB processor does not output Kd).

25 The correlation between in-situ measured and uncalibrated C2R and CC retrieved Kd is 0.83 and 0.84, respectively (Figure 12a and b). Both processors generally underestimate low Kd values and CC overestimates larger values in almost all cases where Kd > 1. The effect of the calibration the improvement is larger for CC Kd is well visible in the scatter plots (Figure 12c, d). a b c d Figure 12: Scatterplots of in-situ against MERIS-derived Kd with the C2R (figs. a,c) and CC (figs. b,d) processors (top row: uncalibrated values, bottom row: calibrated values). A qualitative way to compare Kd data with different processing or from different sources is to look at time series. For a selection of stations, the locations of which are shown in Figure 13, we plotted the temporal course of Kd for 2007, 2009 and The uncalibrated data together with in-situ Kd are presented in Figure 14. From the figure we can see that Kd retrieved from satellite observations is distinctively lower than the in-situ-measured values for most of the months, only the peaks observed in springtime are generally overestimated (slightly more by CC). 25

26 Figure 13: Locations of Kd in-situ sampling stations presented in Figure 14 and Figure 15. For Kd derived from the C2R processor we also applied the calibration to local measurements. The results are presented in Figure 15. In most cases, Kd derived from MERIS matches now the in-situ values. The largest difference between in-situ and satellite Kd is observed for the station FYN for all three years. Among the five stations, this station is closest to the coast and in vicinity to many small islands. Possibly, this had an effect on the satellite measurements, for instance in form of neighbouring effects. 26

27 Figure 14: Comparison of time series of Kd from the C2R processor, the CC processor and in-situ measurements for different stations in the Danish waters of the Baltic Sea incl. Kattegat and Skagerrak and the years 2007, 2009 and The locations of the stations are given in Figure 13. C2R: Case 2 Regional version 1.6.2, CC: CoastColour V2.

28 Figure 15: Time series of Kd from the C2R processor (original and calibrated) compared to in-situ measurements for different stations in the Danish waters of the Baltic Sea incl. Kattegat and Skagerrak and the years 2007, 2009 and The locations of the stations are given in Figure 13. C2R: Case 2 Regional version 1.6.2, calib. lm.: calibrated with the coefficients of a linear model.

29 An example of the attenuation coefficient at 490 nm in the Danish waters on 24 March, 2011 is shown in Figure 16. The MERIS data was processed with the C2R algorithm and a regional calibration was applied to it. In general, coastal waters exhibit higher Kd values than deep waters off the coast. On the map, highest values can be found in the Wadden Sea (west coast) with Kd values up to 5 m -1. A high attenuation coefficient means that the water is relatively opaque. Comparing the Kd map to the Chl-a map (Figure 11), the spatial correlation between the two products is obvious. The more phytoplankton (Chl-a = proxy for phytoplantkon or algae) in the water, the lower the transparecy of the water becomes. Denmark Sweden Figure 16: Kd map of the Danish waters on 24 March 2011 derived from MERIS Full Resolution data. The C2R processor was used for the retrieval and a calibration based on in-situ data was applied to better represent the regional conditions Total Suspended Matter Statistics for the quantitative comparison between in-situ and satellite-derived TSM are set out in Table 6. For the CC-processed data, we obtained the smallest errors (MAE = 8.16 and RMSE = 9.3). Yet, the CC dataset contains only 17 points, which makes it difficult to calculate significant statistics. By calibrating the data to the regional conditions, the errors were roughly cut in half for all three processors. TSM (g/m 3 ) C2R (n=38) FUB (n=27) CC (n=17) original calibrated original calibrated original calibrated MAE RMSE Table 6: Validation results of TSM. Original refers to the original uncalibrated C2R/FUB/CC data, calibrated refers to the calibrated data (see section 3.3). MAE: Mean Absolute Error; RMSE: Root Mean Square Error. Looking at the scatter plots of in-situ and uncalibrated satellite-derived TSM, all the plots show that MERIS clearly underestimates TSM (Figure 17 a,b,c). Highest correlation we obtained for the C2R dataset (r = 0.94), but this is related to the larger data range of this dataset which encompasses values up to 100 g/m 3, whereas the CC dataset only encompasses values up to 31 g TSM/m 3 (Figure 17a and c). After calibrating the data to the regional conditions, the mismatch between in-situ and satellite TSM is reduced as can be seen in Figure 17d,e and f.

30 a b c d e f Figure 17: Scatter plots of in-situ against MERIS-derived total suspended matter derived with the C2R (a,d), FUB (b,e) and CC (c,f) processors (top row: uncalibrated values, bottom row: calibrated values). Also when looking at TSM time series for some selected stations (the locations of the stations are mapped in Figure 18), the clear underestimation of uncalibrated satellite TSM is evident (Figure 19). In general, all the stations show high variability in TSM. The station RKB59 exhibits the largest range, the data spans from 8.3 to 140 g TSM/m 3. This station is located in the Limfjord, right after Thyborøn Canal, which leads to the North Sea (Figure 18). Accordingly, the station is highly influenced by varying current conditions related to both wind and tidal effects and the narrow and shallow nature of the area results in very complex sediment movements. The station was included to examine the performance under known difficult conditions. 30

31 Figure 18: Locations of in-situ stations presented in Figure 19 and Figure 20. For the same stations presented in Figure 19, we calibrated the C2R TSM concentrations and plotted the time series together with in-situ data. The calibration to the regional conditions helps to bring satellite-derived TSM more into line with the in-situ values, as shown in Figure 20. However, the satellite has difficulties to reproduce the variability in the in-situ data (see for example station VIB in 2007). In most cases, peaks with high values are still underestimated. For calibration, there were only very few match-up points available with TSM values exceeding 25 g/m 3. The lack of accordance between in-situ and satellite TSM for higher values might arise from the unbalanced match-up dataset. 31

32 Figure 19: Time series of total suspended matter from the C2R, CC and FUB processors compared to in-situ measurements for different stations in the Danish waters of the Baltic Sea incl. Kattegat and Skaggerak and the years 2007, 2009 and The locations of the stations are given in Figure 18. C2R: Case 2 Regional version 1.6.2, CC: CoastColour V2, FUB/WeW version 2.2. DOY: Day of Year.

33 Figure 20: Time series of TSM from the C2R processor (original and calibrated) compared to in-situ measurements for different stations in the Danish waters of the Baltic Sea incl. Kattegat and Skaggerak and the years 2007, 2009 and The locations of the stations are given in Figure 18. C2R: Case 2 Regional version 1.6.2, calib. lm.: calibrated with the coefficients of a linear model. DOY: Day of Year. 33

34 An example of the TSM concentrations in the Danish waters on 24 March, 2011 is shown in Figure 21. The MERIS data was processed with the C2R algorithm and a regional calibration was applied to it. The highest TSM concentrations are mapped along the west coast of Denmark with extremely high values of up to 120 g/m 3 in the Wadden Sea. The Wadden Sea is a highly dynamic system located in the intertidal zone which explains the high concentrations. The spatial patterns visible in the Kd map (Figure 16) can also be discerned in the TSM map, which makes sense, because the more TSM in the water, the lower the transparecy of the water will be and the higher the attenuation coefficient Kd becomes. Denmark Sweden Figure 21: Total suspended matter of the Danish waters on 24 March 2011 derived from MERIS Full Resolution data. The C2R processor was used for the TSM retrieval and a calibration based on in-situ data was applied to better represent the regional conditions. 3.5 Conclusion Comparing the products of the three processors with in-situ data revealed that except for Chl-a, the bio-optical water constituents are largely underestimated regardless of which processing scheme was used. Calibrating the output products to the regional conditions with in-situ measurements helped to reduce this underestimation. After calibration, the comparison between in-situ and satellite-derived values revealed best results for CC Kd and TSM products, but for Chl-a the C2R product achieved lowest errors. Despite the fact that the CC processor produced best results for two constituents (Kd and TSM), we have chosen to use the calibrated C2R processed data in the AQUA- USERS project. The selection is based on the overall performance of all three constituents. The flagging of CC is too restrictive especially taking into account the many cloud covered days we already have at the northern latitudes. These two factors together reduce the number of useful images significantly. Interestingly, even though theoretically FUB should be best capable to retrieve bio-optical constituents in the complex Danish case 2 waters because the algorithm was calibrated on a dataset with similar waters as we used in this study. However, the FUB products did not convince with better statistics, quite the opposite, we computed the largest errors for the FUB data. The results we obtained are promising, taking into consideration the complex conditions of case 2 waters in the transition zone between North and Baltic Sea (inclusion of relative shallow areas, several stations very close to land, narrow fjords with highly dynamic variation due to freshwater inflow with sediments/nutrients etc.) and the limited amount of light in early spring/late autumn due to low sun elevations. In the near future, we expect considerable improvements by receiving state-

35 of-the-art data products from ESA s Sentinel-3 missions and new algorithms and processing schemes that are regularly upgraded and enhanced. 35

36 4 Netherlands 4.1 Description of region Shellfish aquaculture in the Netherlands takes place in the Wadden Sea and the Eastern Scheldt areas (Figure 22). In both areas, bottom culture is the prevalent form of cultivation. Figure 22: Main shellfish production areas in the Netherlands The Wadden Sea is a shallow intertidal area that is sheltered from the North Sea by a series of barrier islands. North Sea water enters the Wadden Sea via tidal inlets between the islands, and fresh water enters via river discharge. Typically for tidal flat areas, the Wadden Sea shows very high concentration ranges of total suspended matter (<1 - > 1000mg/l) due to sediment resuspension and chlorophyll-a (<1-90mg/m³) due to nutrient input from rivers and land runoff (Hommersom et al. 2010). Both TSM and Chl-a show patterns of temporal variability. Chl-a concentrations exhibit a clear seasonal pattern with very low concentrations in winter (just above 0mg/m³) and higher concentrations in summer (5-50mg/m³), and highest concentrations (up to 90mg/m³) during a spring phytoplankton bloom occurring approximately between week 10 and 20 (Tilmann et al. 2000). Inter-annual variability is high as well (Cadée and Hegeman 2002). TSM concentrations also show a seasonal pattern with higher concentration in autumn and winter (30-120mg/l) and lower ones in spring and summer (25-85mg/l). The most distinct temporal pattern for TSM, however, is the tidal variation, which is much higher in magnitude than the seasonal variation (Cadée 1982). The dominant spatial structures of the Wadden Sea are tidal channels and tidal flats. At high tide, when the tidal flats are inundated, water quality in the Wadden Sea as a whole can be observed by satellites sensors with the spatial resolution of MERIS. At low tide, however, the exposed tidal flats hamper the retrieval of water quality. At low tide, surfacing tidal flats can have sizes of tens of meters to some kilometres square with only small channels between them with up to tens of meters remaining. These are too small for detection of water quality with MERIS. Only in the deep tidal inlets between the islands that are one to three kilometres wide and some kilometres long water quality can be observed during low tide with MERIS (Hommersom et al. 2010). The Eastern Scheldt (Oosterschelde) is a tidal basin in the southwest of the Netherlands. It is separated from the North Sea by a storm surge barrier to protect a large part of the Netherlands from flooding. This barrier, completed in 1986, has strongly influenced the dynamics in the Eastern 36

37 Scheldt by reducing water flow and tidal height difference. TSM concentrations reach up 10mg/l in the eastern and northern parts with no pronounced annual pattern, whereas in the southern and western part they reach up to 30mg/l with a seasonal pattern. Chl-a concentrations are lowest in winter (just above 0mg/m³), higher in summer (up to 20mg/m³), and highest during spring with values up to 50mg/m³. Secchi depth in the Eastern Scheldt varies between 1.5 and 6m (Wetsteyn & Kromkamp 1994). As large areas of the Eastern Scheldt are rather shallow (<5m), the high transparency means that bottom visibility can influence remote sensing of water quality. Although shellfish production is mainly restricted to the Wadden Sea and Eastern Scheldt, it is also considered as important to include the adjoining areas of the North Sea in this analysis as algal blooms developing in the North Sea can severely influence the production areas. For example, 10 million kg of mussels were found dead in the Eastern Scheldt in This event has been traced to a large bloom of Phaecystis occurring in the nearby North Sea and being transported towards the mussel beds. Most likely the following sedimentation of the alga detritus led to anoxia causing the massive mussel death (Peperzak and Poelman 2008). 4.2 Overview of processors For the Dutch area, two different validation exercises were performed: validation of atmospheric correction and validation of water quality parameter retrieval. As there is a number of water quality algorithms available that operate on remote sensing reflectances (such as the WISP algorithm), for these algorithms it is necessary to perform atmospheric correction first in a separate step. The three neural networks FUB, C2R and CC were all developed for retrieval of water quality parameters, however, they all perform atmospheric correction separately, so that they can also be used as atmospheric correction components when using other WQ algorithms. Therefore, the atmospheric correction component of these algorithms have been validated against in-situ reflectance measurements available in the MERMAID. Additionally, it has been evaluated whether using the ICOL processor to correct for adjacency effects improves the results of the atmospheric correction. Three different in-water algorithms were compared, namely CoastColour (CC), Case-2 Regional (C2R) and the WISP algorithm. Both neural network algorithms use their accompanying atmospheric correction algorithm while the WISP algorithm uses Rrs data from the C2R algorithm as input. In first instance the WISP algorithm was run with the established North Sea SIOPs published in Van der Woerd and Pasterkamp (2008). In second instance, the algorithm was tuned per region (by adapting the literature values for SIOPs) to optimise the performance. This exercise was only partially successful because of the noisy character of the data Validation data In-situ reflectance measurements (MERMAID) For validation of the atmospheric correction schemes, data from MERMAID (MERis Matchup In-situ Database - were used. An overview of the data sets is given in Table 7. Some of the data sets cover different areas, for this analysis only data from the Dutch, Belgian, German and Danish coastal areas are used. As can be seen in Figure 23, very few data are available for the Wadden Sea and none for the Eastern Scheldt. Therefore, also data from adjoining areas (German and Belgian coastal areas) were used. As these areas are quite different in their optical properties from the actual study areas, this study can only be considered as a screening, not an in-depth analysis of the most suitable atmospheric correction scheme for the Wadden Sea and Eastern Scheldt. 37

38 Data set name PI Parameters Region Number of spectra Helgoland MUMMTrios SIMBADA WaddenSea Roland Doerffer Kevin Ruddick Pierre-Yves Deschamps Annelies Hommersom ρ wn (λ), ρ wn_isme (λ *, E s (λ) North Sea 1722 ρ wn (λ), ρ wn_isme (λ), E s (λ), Chl European waters 433 ρ wn (λ), ρ wn_isme (λ), E s (λ) World wide 510 ρ wn (λ), Chl, IOP, TSM Wadden Sea (NL, DE and DK) 5 Table 7: Overview of datasets from the MERMAID database used in this analysis - ρ wn (λ) - In-situ normalised water reflectance at band λ, ρ wn_isme (λ) - in-situ water reflectance, computed using MERIS solar irradiance, E s (λ) -Sea level solar illumination at band λ The instruments and specific protocols used for in-situ optical measurements vary between the datasets. They are described in detail by Barker (2011). Figure 23: Overview of data sets used for validation of atmospheric correction schemes National in-situ monitoring network (MWTL) For validation of optical models, in-situ measurements of Chl-a and TSM from the Dutch national monitoring network were used. The Dutch national monitoring programme MWTL (in Dutch: Monitoring Waterstaatkundige Toestand des Lands Milieumeetnet rijkswateren) provides measurement data for a large number of sampling stations with regular sampling schemes since the 38

39 1970s. Figure 24 shows the location of the stations for which data is the operational period of MERIS ( ). A number of physical, chemical and biological parameters is measured at these stations; here we use the measurements of Chl-a and TSM. Both are measured in samples pumped from 1m depth. Chl-a is determined using HPLC and TSM using gravimetric measurements. Information on these data is available from the Helpdesk Water ( The data were retrieved via the Waterbase public web service ( A number of stations that are normally excluded from this type of validation analysis (because they are either too close to the coast or in highly dynamic environments) were included to study the potential of realistic concentration retrieval in areas relevant for Dutch aquaculture. Figure 24: Location of in-situ measurement stations of the MWTL measurement network 4.4 Validation methods Validation of atmospheric correction methods For the validation of atmospheric correction methods, match ups were defined as in-situ and satellite measurements taken on the same day. From the processed satellite images, the calculated reflectances for all available spectral bands were extracted from the pixel in which the in-situ 39

40 measurement was taken. Table 8 shows which flags were applied to the reflectances to remove data of low or unknown quality. Processor Removed Flag Explanation C2R FUB CC AGC_FLAGS.INVALID L1_FLAGS.GLINT_RISK L1_FLAGS.BRIGHT L1_FLAGS.INVALID L1_FLAGS.SUSPECT L2R_FLAGS.INPUT_INVALID L2R_FLAGS.L2R_INVALID Combines flags L1_FLAGS.INVALID, AGC_FLAGS.LAND and AGC_FLAGS.CLOUD_ICE 'Input invalid' pixels (ll1p_flags.cc_land (ll1p_flags.cc_cloud and not ll1p_flags.cc_cloud_ambiguous) ll1p_flags.cc_snow_ice ll1p_flags.invalid) 'L2R invalid' pixels (quality indicator > 3 ll1p_flags.cc_cloud) Table 8: Flags applied for validation of atmospheric correction schemes for the different processor For validation of modelled reflectances, three (sets of) statistics are applied: correlations per spectral band, Chi-square and the spectral angle. The first set of statistics looks at the performance of the algorithms in terms of deviations of modelled vs measured reflectance value per spectral band. For each algorithm and each spectral band, the following statistics are calculated: coefficient of determination (R²) slope and intercept of the linear regression equation To evaluate the performance of an atmospheric correction scheme, not only the results for single spectral bands are of importance, but also how well the overall spectral shape matches the in situ data. Two statistics are computed to evaluate the ability of the atmospheric correction methods to match the shape of the measured spectra: The Chi-square test (Χ²) is used to measure the goodness of fit between in-situ and satellite derived spectra, taking into account both the shape and the intensity. So, one Χ² value is calculated per match-up and to compare the results of the different algorithms, the average Χ² is calculated (Eq.8). Χ 2 = Σ (Rmodel-Rinsitu) 2 ) / Rinsitu Eq. 8 A statistic that compares the spectral shapes while ignoring the absolute values is the spectral angle Dennison et al. (2007). The spectral angle is also calculated per match-up and to compare the results of the different algorithms, the average spectral angle is calculated (Eq.10). 40

41 Eq. 9 Eq Validation of water quality parameter retrieval Validation was performed by comparing satellite-derived data with in-situ measurements of the water quality parameters Chl-a and TSM. This comparison was performed over the entire data set as well as for a five separate regions which are quite different with respect to their water quality properties and dynamic: Wadden Sea (WZ), Dollard (DL), Eastern Scheldt (ES), Western Scheldt (WS) and North Sea (NZ). The evaluations are all done on point samples which pose some serious limitations to the final assessment of the usability of the images. In practice, a satellite image will result in a map of e.g. the spatial structure of a CHL bloom or a TSM plume. Noisiness in the image, and even erroneous patterns and pixels because of disturbances such as cloud shadows will affect the point validation maximally, while they may be easily identified by evaluating spatial structures and eliminated either by smart programs or by visual interpretation. To illustrate the findings of the study, results will be presented as scatterplots (from log transformed data), time series (normal data) and results from a statistical study to see what combination of L1 and L2 flags might help to identify suspect pixel values. Differences between satellite-derived and in-situ measured water quality indicators were quantified using different statistical error metrics: Mean absolute error (MAE) MAE 1 Sati ISi n n i Root mean squared error (RMSE) Eq. 11 RMSE n 2 i 1 ( Sati ISi ) n Eq. 12 Bias Bias n i 1 Sat IS n i i Correlation coefficient (R) and coefficient of determination (R²) Slope and intercept of linear regression Number of points (n) These statistics are computed both in linear and log space. Eq

42 4.5 Results Atmospheric correction The comparison of the processors for atmospheric correction mainly serves as a basis for deciding which processor to use in conjunction with the WISP algorithm. It is therefore not an in-depth analysis, but rather a screening to establish the relative merits of the different processors and make an informed decision for further processing. Figure 25 and Figure 26 show scatter plots of all processed spectra per processor for two selected spectral bands (665nm and 709nm respectively) typically used in algorithms for Chl-a. Overall, no great difference is discernible between the different processors and with or without ICOL. All processors show a clear correlation and seem to be reasonably close to the 1:1 line, but also show some scatter around the regression line as well as some serious outliers. The outliers seem to be the same ones for most processors. This could indicate either a problem with the in-situ spectra or in the raw satellite images as all processors use the same level 1 MERIS products. 42

43 Figure 25: Scatterplots satellite-derived vs in-situ Rrs for the spectral band at 665nm for the three processors C2R, CC and FUB/WeW each with and without ICOL 43

44 Figure 26: Scatterplots satellite-derived vs in-situ Rrs for the spectral band at 709nm for the three processors C2R, CC and FUB/WeW each with and without ICOL Figure 27 shows plots of some sample spectra of the different data sets comparing the in-situ spectrum to the satellite-derived spectra for the different processors. The different magnitudes and shapes of the spectra show that the water types encountered in the study area are very diverse. 44

45 Figure 27: Sample spectra satellite vs in-situ for all three processors with and without ICOL. Upper left: from Simbada dataset (P. Deschamps), Upper right: from Helgoland dataset (R. Doerffer), Lower left and right: from MuMM-TRIOS dataset (K. Ruddick) Table 9 shows the results for all statistical measures calculated for the three atmospheric correction schemes C2R, CC and FUB, each in combination with ICOL and without it. It is evident from the table that the processors all perform on a similar level. C2R performs best with respect to X² and also performs best with respect to correlations and linear regression in the spectral bands between 510nm and 665nm. The use of ICOL with C2R is inconclusive, some statistical measures improve whereas others deteriorate; overall the performance with and without ICOL is very similar. CC performs best (on par with FUB + ICOL) on the spectral angle and also performs with respect to correlation and linear regression parameters in the spectral bands up to 560nm. Overall, ICOL slightly improves the performance of CC. FUB/WeW performs best with respect to spectral angle (on par with CC) and also performs best in the lower spectral bands (412nm to 490nm). Overall, ICOL improves the performance of FUB/WeW. It should be noted that FUB/WeW does not provide reflectances for all spectral bands (it omits the spectral bands at 681nm, 753nm, 778nm and 865nm), which can be a limiting factor for in-water retrieval algorithms that make use of these bands. C2R + ICOL C2R CC + ICOL CC FUB + ICOL Mean X² Mean spectral angle FUB 45

46 R² (412nm) Slope (412nm) Intercept (412nm) R² (443nm) Slope (443nm) Intercept (443nm) R² (490nm) Slope (490nm) Intercept (490nm) R² (510nm) Slope (510nm) Intercept (510nm) R² (560nm) Slope (560nm) Intercept (560nm) R² (620nm) Slope (620nm) Intercept (620nm) R² (665nm) Slope (665nm) Intercept (665nm) R² (681nm) Slope (681nm) Intercept (681nm) R² (709nm) Slope (709nm) Intercept (709nm) R² (753nm) Slope (753nm) Intercept (753nm) R² (778nm) Slope (778nm) Intercept (778nm) R² (865nm)

47 Slope (865nm) Intercept (865nm) Table 9: Results of the atmospheric correction validation. For each statistical measure, the best performing processor is marked in green, the second best in light green and the third best in yellow. The statistical measures based on individual spectral bands (R², slope and intercept) are only ranked for those spectral bands that all processors provide. Based on these results, C2R without ICOL was chosen as atmospheric correction scheme before in water processing with the WISP algorithm in the Dutch coastal zone. Overall, C2R slightly outperformed CC and performed similar to FUB/WeW. As FUB/WeW is missing a number of spectral bands, C2R was chosen. As ICOL does not clearly improve the results, it was decided to refrain from applying it Water quality parameter retrieval Analysis of match-ups 47

48 Figure 28: Scatter plots of C2R (top), CC (middle) and WISP (bottom) Chl-a (left) and TSM (right) results. On the horizontal axis the MWTL measurements are plotted, on the vertical axis the algorithm results. Evaluation of CHL and TSM results by satellite is traditionally done by comparing the results in a pixel (here 300 m) to an in-situ observation (here water samples at 1 m depth taken from a boat). The first issue to take into consideration by doing this is looking at possible biases in the data. It is known that the water samples from the MWTL measurement programme have several biases: 48

49 The sampling is done weekly, two-weekly, monthly or even less frequently, therefore the sampling of single events or events with a short periodicity is incomplete. The sampling is done when the ship arrives; the time of sampling is not constant, leading to uncertainties in areas with high spatial and temporal dynamics. There is no sampling at sea at wind speeds higher than 4 m/s. The intra-lab and inter-lab accuracy of CHL and TSM measurements is not known (to the present team) so systematic biases are unknown. The basis for calculating concentrations from satellite observations is the spectrum per pixel recalculated to the sea surface (BOA or Bottom of Atmosphere) after removing atmospheric affects. Biases in the BOA data can be caused by: Incorrect radiometric correction at satellite. The data used for this research originates from the 3 rd reprocessing obtained from the CalValus on-demand processing system. Reportedly, the 3 rd reprocessing results are not always reliable (Mueller et al. 2015). Incorrect atmospheric correction: here we use two versions of Neural Network atmospheric correction designed by R. Doerffer (Doerffer & Schiller 2007, Brockmann Consult et al. 2014), namely C2R which was trained mostly on North Sea data and CC which was trained on a worldwide dataset of coastal waters. From the differences in training datasets one might assume that C2R would perform best in open North Sea Waters and CC would perform better in challenging conditions close to the coast and in extreme case-2 waters. Incorrect handling of the adjacency effect: the combination of high reflecting targets close to relatively dark water pixels and the presence of a more or less scattering atmosphere leads to additional signal in the red-nir region, possibly causing overestimation of TSM in turbid waters and errors in the CHL estimation, also in the more turbid waters. The effect may be set off by the fact that in extremely turbid waters the contrast between water and land maybe less, depending on the presence of vegetation on land. All scatterplots in Figure 28 are very noisy, explanations can be found in many factors: Time differences between satellite and in-situ observations in a highly dynamic environment 300 m pixels lead to mixed pixels Low CHL values at high TSM (and CDOM) will suffer from a limited signal-to-noise ratio of MERIS under circumstances with non-optimal light conditions in parts of the year and at increased haziness Visual inspection shows that the amount of noise in MERIS FR data is higher than RR data. This observation is in accordance with the results of a study on theoretical detection limits for water quality information from MERIS for highly absorbing water types by GLaSS (2015). When evaluating the unfiltered log-log scatter plots (at 300 m resolution) in Figure 28 some points come forward: 1) For such a challenging area including stations in the open North Sea and stations very close to the coast (within 1-2 km) in the Eems Dollard, Oosterschelde, Westerschelde and Waddenzee, the results of this study are encouraging 2) Values for CHL range from 1 to above 100; values for TSM range from 1 to above 200 3) Frequency seems to be constant over the ranges 4) There are obvious outliers (C2R CHL and TSM below 1; CC CHL between 1 and 10; WISP CHL below 10 and TSM above 100) 49

50 5) There is some similarity in the shape of the scatterplots of CHL-C2R and CHL-WISP. 6) All TSM log-log plots indicate a more or less linear relationship between in-situ and satellite derived TSM vales 7) C2R and CC seem to be overestimating the low CHL values, WISP and CC seem to agree with in-situ CHL measurements around 10; C2R is overestimating in this area 8) CC is the only algorithm that sufficiently captures the high peaks in CHL 9) C2R underestimates TSM for the low and high values, while CC is underestimating TSM over the whole range 10) WISP (because it was tuned per region) has improved performance for TSM over the whole range, although it features high spikes in the region above ) Even with substantial tuning per region, the CHL by WISP algorithm (based on C2R atmospheric correction) has problems to achieve a linear relationship with the in-situ observed CHL. Since the patterns are similar to C2R but different from CC one might speculate that the advanced atmospheric correction of CC (probably compensating better for the adjacency effect (Doerffer 2010)) improves the possibility to obtain correct CHL estimates at higher values. So a recommendation for operational use of the WISP algorithm would be to consider the CC atmospheric correction as a basis for improved concentration estimations Time series analysis To illustrate the results and the differences per algorithm per region, we will present some illustrative time series plots. 50

51 Figure 29: Station Bochtvwtm is deep in the Dollard estuary in a highy dynamic area. The water is expected to be extremely turbid with high concentrations of CDOM. Chl concentrations are best approximated by the WISP algorithm but the phenology is not very clear in all results. 51

52 Figure 30:Station Bochtvwtm is deep in the Dollard estuary in a highy dynamic area. The water is expected to be extremely turbid which is evidenced by TSM values up to 250 and above. None of the algorithms reproduces the full range of values. CC seems to perform best. 52

53 Figure 31:At station Noordwijk 10 (which is the most frequently sampled station in the North Sea) the differences in CHL retrieval are not very big between the algorithms but WISP seems to retrieve the peak blooms a bit better. C2R fails to retrieve the lower Chl values while is affected by quite severe selection of valid points. 53

54 Figure 32: At station Noordwijk 10 TSM values are quite low which is captured by all algorithms. WISP algo shows some high spikes which should be filtered out in further processing 54

55 Figure 33: At station Zoutkplzgt in the Wadden Sea the Chl concentrations can be quite high (up to 40) while the TSM concentrations peak at 200. This is one of the very challenging stations in the area. Chl concentration retrieval seems to work best with the CC algorithm, WISP is overestimating probably because C2R was used as atmospheric correction. 55

56 Figure 34: TSM concentrations Zoutkplzgt are best retrieved by the WISP algorithm (in terms of range) but with substantial noise and overestimation. 56

57 Figure 35: At station Hammot in the Eastern Scheldt a strange inversion of the time series phenology occurs for both the C2R and the WISP algorithm CHL results and the TSM results as well. This is not the case for the CC algorithm and can presently only contributed to (probably) an improved adjacency correction in the CC neural network approach. 57

58 Figure 36: Although the WISP algorithm achieves the most realistic TSM range, the phenology should be studied further for this station Analysis of the effect of flag and mask setting In order to study the potential effect of using pre-defined flags provided by the neural network processors we have defined a number of flag combinations into 6 different masks. Since the flags are proprietary per neural network realisation and different of definition, C2R in water processing has to 58

59 be combined with C2R flags and CC with CC flags. The WISP algorithm is based on the C2R atmospheric correction processing, hence the L1 and L2a flags of C2R are used. Mask Algo / Flags L1 C2R_AGC C2R_Case_2 CC_L1P CC_L2R CC_L2W 0 CC C2R WISP 1 CC 1,2, C2R 16,64 1,2 8 WISP 16,64 1,2 2 CC 1,2, C2R 16,64,128 1,2, ,128 WISP 16,64,128 1,2, CC 1,2,4, , C2R 16,64,128 1,2,4,8,16, ,128 WISP 16,64,128 1,2,4,8,16, CC 1,2,4, C2R 16, 64, 128 1,2,8, , 128 WISP 16, 64, 128 1,2,8, CC 4, 16, 32, 64, , 4096, 8192 C2R 4, 16, 32, 64, 128 1,2,8, , 128 WISP 4, 16, 32, 64, 128 1,2,8, CC 1,2, C2R 1,4 4,8 8 WISP 1,4 4,8 Table 10: Definition of the masks used to process the results of the CC, C2R and WISP algorithms. The flagnumbers refer to flags as listed in appendix 1. Per mask and processing step (l0->l1, l1>l2a atmospheric correction and L2a-> L2b water products) the flags are identified that were deemed to have positive influence on the final result by flagging our obvious errors such as out of training range, cloud shadows, glint, etc. The full overview of the flags corresponding to the flag numbers can be found in appendix 1. For the processing of CC data, the proprietary flag sets were used, while for the processing of WISP and C2R data, the flag sets of C2R were used (WISP in water retrieval was based on C2R atmospheric correction). 59

60 C2R all CC all WISP all C2R dl CC dl WISP dl C2R nz CC nz WISP nz C2R os CC os WISP os C2R ws CC ws WISP ws C2R wz CC wz WISP wz Table 11: Number of observations per mask per area. Table 11 shows quite clearly that different flag settings reduce the potential number of observations but to various extents. The first difference that is noticeable, is the difference in the number of starting values between CC and C2R/WISP. Evidently, CC masks already quite some points at the early stage, maybe because of more stringent land mask. The masks based on C2R flag sets let most observations pass, except the mask 3 that results in approximately a ¼ reduction. Masks based on the CC flag sets have a more severe effect. Masks 1, 2, 3 and 4 give approximately a 40% reduction and mask 5 gives a 60% reduction. Use of flags to reduce the number of suspect points will clearly have a profound effect on the number of results coming out of operational processing. The question is, whether the user will be more interested in high quality results at lower frequencies or vice versa Effect of tuning the WISP algorithm with area specific inherent optical properties Figure 37 shows the differences in two version of the WISP algorithm. The first version uses the published SIOP values (Van der Woerd & Pasterkamp 2008) for the North Sea to derive CHL and TSM. The second version (Blue diamonds) shows the results after tuning the algorithm (in log-log-space) to the MWTL observations. This type of tuning is quite easy to do because the parameterisation of the algorithm is given by an external text file that can be easily modified by hand or by optimisation routines. The comparison is unequal because the pre-tuned WISP results are calculated from matchup data while the station average tuned WISP results are calculated from the full time series. 60

61 Figure 37: Comparison of CHL and TSM results as station 10 year mean values before tuning (red squares) and after tuning (blue diamonds). On the horizontal axis the MWTL measurements are projected and on the vertical axis the WISP algorithm results. For North Sea (Noordzee - NZ) stations the tuning brings the high CHL values a bit down and the TSM values down with a factor of almost 5. Eastern Scheldt (Oosterschelde OS) values for TSM and CHL are not affected that much by the tuning. The Wadden Sea (Waddenzee WZ) values show a different pattern. To achieve realistic (extremely high) TSM values by tuning, the CHL values are also affected and become overestimated by approximately 1/ Evaluating the performance of algorithms using RMSE as an indicator To evaluate the performance of the algorithms using the various masks was done based on a number of statistics (R, R2, slope, intercept, RMSE, MAPE). To illustrate the results we have selected RMSE as the one indicator for the goodness of fit of algorithm results to MWTL match-up data. 61

62 Figure 38: Comparison of the change in performance of algorithms (measured by RMSE) for the masks 0-5 for all data of all stations. The mask number is displayed on the horizontal axis, while the RMSE number is displayed on the vertical axis. 62

63 Figure 39: Comparison of the change in performance of algorithms (measured by RMSE) for the masks 0-5 for WZ data. The mask number is displayed on the horizontal axis, while the RMSE number is displayed on the vertical axis. 63

64 Figure 40: Comparison of the change in performance of algorithms (measured by RMSE in log-log space) for the masks 0-5 for WZ data. The mask number is displayed on the horizontal axis, while the RMSE number is displayed on the vertical axis. 64

65 Figure 41: Comparison of the change in performance of algorithms (measured by RMSE in log-log space) for the masks 0-5 for NZ data. The mask number is displayed on the horizontal axis, while the RMSE number is displayed on the vertical axis. Comparison of RMSE values for the various masks, for the three algorithms, in the various areas shows quite variable (Figure 38). For the all-stations-data WISP has the lowest RMSE and CC (depending on the mask) the higher values. For TSM this is the other way around. While the WISP results are tuned per region and have a good overall fit, the algorithm also produces quite some extremely high spikes that should be filtered out. For WZ data (Figure 39), C2R and WISP perform similar, while CC produces higher RMSE values. Also in the Wadden Zee case (Figure 39) the RMSE for TSM is lowest for CC results, which is somewhat surprising because CC data tend to underestimate TSM substantially. When looking at RMSE values from log-transformed data, the results are more in line with e.g. the visual interpretation of the scatterplots in log-space (Figure 40). In this case, for the very challenging Wadden Zee area, RMSE of WISP-CHL is highest while RMSE of WISP-TSM is lowest. Finally, RMSE values for the NZ area show lowest values for CHL and for TSM for the WISP algorithm (Figure 41). Final conclusion based on these statistics is very difficult for the following reasons: 1) The statistics are influenced by the noise in the FR data. It could very well be that the spatial structures of CHL blooms and TSM plumes show much greater spatial consistency than 65

66 suggested by the point data analysis. The presence of substantial noise is illustrated by the fact that most masks produce similar statistics. 2) The statistics were based on different number of observations between CC and WISP/C2R. 3) There can be a bias in the MWTL data (see discussion in section 4.3.1) which is unaccounted for. 4) The number of in-situ data per station is quite low and all the difficulties of comparing a water sample from a bottle at 1 m depth with a 300x300 m pixel observation at a time difference of maximally 2 hours apply. 5) A large number of stations is so close to the coast (at 2 km or less) that spectral distortion by the adjacency effect is likely. This will result in overestimation of TSM and errors in the calculation of CHL. It seems that the CC processing algorithm was trained including the adjacency effect (Doerffer, personal communication), so it is less sensible to this distortion. Probably this explains why the CC processor produces much better CHL results in near coastal stations. 4.6 Conclusion Validation based on station data for this area gives variable results. In areas with low dynamics (stations in the open North Sea) Chl and TSM retrieval works quite well. In highly dynamical areas and areas very close to the coast good results are more difficult to obtain. If one algorithm should be selected to process the whole area operationally, probably the CC algorithm is the best choice for CHL, although it has less valid points and some underestimation of the TSM values. For operational processing it is recommended to scale the CC_TSM with a function based on the regression analysis of CC_TSM all stations against TSM in situ: TSM= (CC_TSM-2.6)/ It is recommended to further study the regional tuning of the WISP algorithm using CC atmospherically corrected spectra as input. Based on the analysis of flags and masks it is difficult to come to a recommendation. For CC the risk of losing more valid points is substantial so, for operational processing CC with mask 0 is recommended. 66

67 5 Norway 5.1 Description of region The aquaculture sites in Norway are mostly located at the Norwegian west coast and in northern areas. The Norwegian coast is long and consists of different water types and areas. All these areas and water types are not yet fully validated concerning MERIS products. Most focus has been on area in southern Norway. Here different MERIS processor are tested by NIVA during the lifetime of MERIS (Sørensen et.al 2008, Sørensen et al. 2007) and the main findings are summarized here. Most of the tests are from open areas using the Ferrybox network (Figure 42) as well as traditional scientific validation cruises, and the area between Denmark and Norway has been investigated. Figure 42: The validation network in Norwegian and Danish water using Ferrybox (left) and traditional validation cruises during several campaigns in 2002 and 2003 (right). The Skagerrak differs from the rest of the North Sea by its wide range of depths, stratified water masses and mixture of three dominant water types: (1) Baltic Sea water of low salinity (typically 8) and a high content of yellow substance, (2) North Sea water of Atlantic origin with very high salinity (35 or higher) and a very low content of yellow substance, and (3) German Bight waters with high salinity (typically 31) and a high content of yellow substance. The influence of the German Bight is weaker in the Norwegian coastal waters, and therefore the third dominant water type is local river run-off, such as Glomma, and fjord waters. A large number of validation samples were collected by automated sensor and sampling systems onboard ships of opportunity (SOOP) operating between Norway and Denmark. Figure 43 illustrate a MERIS image overlaid with Ferrybox data in a transect between Oslo and Hirtshals. 67

68 Figure 43: MERIS image of Chl-a showing a spring bloom of algae in Skagerrak in February The transect from a Ferrybox system used for collecting validation data is also shown. The colour coding are the same for the image and the Ferrybox data The optical water types in the area In Sørensen et. al the optical properties of the water in the Skagerrak area (Figure 43) is described. In Table 12 the mean values of a y (442), a bp (442) and b p (442) from the surface and the half and full Secchi disk depths are presented together with the mean values for the corresponding spectral slopes in the range nm, collected from Sørensen et al. (2007). Results from Sørensen et al. (2007) were compared with Helgoland data, which was used in the MERIS standard products (2 nd processing) and the main results are summarized below. The mean spectral slope (a y ) of the yellow substance samples from Skagerrak was slightly lower than the slope in the reference model used for the standard products. The slope of bleached particles (a bp ) for the Skagerrak was in contrast slightly higher. The spectral slope or exponent of particle scattering (b p ) from Skagerrak was almost the same as for the reference model. The standard deviation for Skagerrak is almost twice as high as for the dataset used in the reference model, indicating a greater variation of particle properties and types. a y (442) (m -1 ) a bp (442) (m -1 ) b p (442) (m -1 ) Slope a y (nm -1 ) Slope a bp (nm -1 ) Slope b p (nm -1 ) Mean value Std. dev N Table 12: Optical properties of the Skagerrak water types (Sørensen et.al. 2007) 68

69 5.2 Overview of the processors Three neural network-based processors for optical Case 2 waters (FUB, C2R, and CoastColour cf Section 2.1) to retrieve bio-optical water quality indicators were compared to the standard MERIS algal_2 processor. All three NN processors are available as plugins in the Visat BEAM software developed by Brockmann Consult 5.3 Validation data All details about the data collected during the traditional cruises and the validation methods used in this area have been reported in Sørensen et al. (2007a), and they follow in general the MERISprotocols (Doerffer 2002). In the referred study only measurements of total suspended material (TSM) and chlorophyll-a (Chl-a) were used. The in vitro chlorophyll-a and TSM were determined with methods recommended in Doerffer (2002) and Tilstone et al. (2002). The methods are summarized in the AquaUSER deliverable D5.1. A Ferrybox system onboard ships of opportunity operating between Denmark and Norway collected water samples through an ISCO automatic water sampler from a fixed depth of 4-5 m. These water samples were pumped into a refrigerated system and filtered latest the next day for Chl-a (HPLC) and TSM. The Ferrybox system contains in addition a number of in-line sensors logging data with 1 minute interval from a continuous water flow. The system therefore delivers data with a spatial resolution of about 300 m depending on the ship s speed. Validation points close to the coast of Denmark (latitude<58.6 N) and Norway (latitude> 57.6 N) were removed. A reproduction of Figure 3 in Sørensen et al. (2008) is done with three different processors excluding the Level 2 Reduced Resolution (RR) from the 3 rd processing and including the CoastColour (v1.7) processer. The additional two is the FUB (v2.2) processor and the C2R (v1.6) processor. All three are described more detailed in section 2.1. All processors are implemented in BEAM which is used in the processing from L1 to L2 products, but the CC products where downloaded via the on-demand processing service. In addition the L1 product is pre-processed with the IdePix algorithm before running the FUB algorithm. Pixels with no flag raised for the l2w_flags.invalid and l2r_flags.l2r_invalid where included as match up observations and a limit of 2 hours between in situ and MERIS observations were set. 5.4 Results Validation of Chlorophyll-a The processing of the MERIS Case 2 data products for chlorophyll-a (Chl-a) used in the referred study is based on the optical quantities a pig (442) Doerffer (2002). NIVA data from Skagerrak was included in the second processing of the MERIS data and the new conversion factor for the 2 nd processing data was very close to the best fit for the Skagerrak (Sørensen et al., 2007a). For Chl-a a total of 73 matchup points were found using the Ferrybox collected data, and the results of Chl-a for the selected match-ups are shown in Figure 44. In the referred study, the in situ data explains 86 % (R 2 =0.86) of the variation and the RMS=1.17 mg/m 3, and the algal_2 product gives approximately 14 % higher values than the corresponding in situ values. This discrepancy is smaller than the variation and accuracy found in Chl-a HPLC methods which can be up to +/- 20% for trained validation teams. 69

70 Figure 44: The relationship between standard MERIS Case 2 chlorophyll-a (algal_2) and in situ chlorophyll-a (Chl-a_HPLC) from selected Ferrybox sampled data in the Skagerrak. The figure also shows the 1:1 line (green), the regression line (solid red), the 95% conf. int. for the regression (dotted blue) and 95% conf. int. for the prediction (dotted red.). Collected from Sørensen et al. (2008) Two other processors that show promising results are the FUB and Case2Regional processors. The latter of these is also trained on Skagerrak data. Results from these processors are shown in Sørensen et al. (2008) together with the standard IPF algal_2 product data from the SOOP transect on March On this date water samples were collected by the Ferrybox system. A reproduction of this figure is presented in Figure 45 with updated versions of the FUB and C2R processor together with CC processor. Figure 45: A reproduction of Figure 3 in Sørensen et al. (2008) presenting the relationship of MERIS FUB (v2.2) Chl-a product, MERIS Case2R (v1.6) Chl-a product, CC v1.7 Chl a product and Chl-a_HPLC from a transect in the Skagerrak between Denmark (57.5 N) and Norway (59.5N) on March

71 C2R data give clearly higher values, and CC a bit higher than the FUB data in the central Skagerrak. All the processors tends to give higher Chl-a values when one enters the Oslofjord area (Latitude >59.0 N), but FUB still follows the in situ data. In this area the influence from the land and the low saline and higher absorption Oslofjord water probably due to more CDOM Validation of total suspended material Earlier validation of from the Skagerrak area has shown good agreement with in situ data. The conversion factor determined for the area [Eq. 14] presented in Sørensen et al. (2007), TSM = 1.57 x b p(442) Eq. 14 is close to the relation that are used as the standard conversion factor for the Case2 TSM products as shown in Eq. 15, TSM = 1.73 x b p(442) Eq. 15 In the study of Sørensen et al. (2007) a total of 60 matchup observations were found for TSM and the results confirmed earlier findings, as seen in Fig.4 and in Eq. 16, TSM MERIS = 1.05 x TSM in-situ Eq. 16 The in situ data explains 71% of the variation (R 2 =0.71) in the MERIS data and the RMS=0.30 g/m 3. Figure 46: The relationship of MERIS Case 2 TSM product and in situ TSM from selected Ferrybox data in the Skagerrak. The figure shows the 1:1 line (green), the regression line (solid red), the 95% conf. int. for the regression (dotted blue) and 95% conf. int. for the prediction (dotted red.) TSM data converted from the sensor turbidity data (Eq. 17) were used in Sørensen et al. (2008) in the validation as single point measurement coincident with the HPLC match-ups locations (Figure 46) TSM = x Turbidity Eq. 17 In Sørensen et al. (2008) a study for TSM with older version of the FUB and C2R processors were tested together with the standard IPF TSM product on TSM data from the same Ferrybox transect as was used for Chl-a (Figure 45). In case 2 and coastal waters transect data are often better to use than 71

72 single point measurements due to frontal zones and sub pixel in-homogeneity. Figure 47 shows a very high TSM peak at N for the in-situ as well as for the three processors. There is a small shift between MERIS processed data and in-situ data in the peak location due to the time difference and the tidal variation. Interestingly, the TSM peak occurs at the same place as the Chl-a peak (Figure 45) for FUB. Figure 47: Transect of Ferrybox and MERIS TSM for the 3 different MERIS processors; standard IPF, FUB (v1.1) and Case2R (v1.4 beta). Salinity measured by the Ferrybox system is also shown. A similar study as in Figure 47 is presented in Figure 48 with L3 TSM products (mean from June to August 2011) from the three different processors with clear differences between the three. Data from the CC processor are lowest, while C2R data have highest values. They all seem to fail when getting closer to the coast, with CC failing already at greater distance than C2R and FUB. Preliminary correlative analyses, indicate that the C2R data give the closest fit to the in-situ data, but this must be verified with a larger dataset. In the same Figure 48, preliminary results from a comparison of the L3 TSM product from the CC processor with in-situ data from a station in the northwest Skagerrak on the Norwegian coast (Arendal 2) is presented. Data from the CC processor are clearly lower than the in-situ mean values from the station, but with much less variation in the dataset for some of the years. 72

73 Figure 48: Above a transect of MERIS L3 products for the three different MERIS processors; CC (v1.7), FUB (v2.2) and C2R (v16). Mean values of TSM averaged from June to August Below preliminary results from a comparison of MERIS L3 product from the CC processor and in situ data from Arendal 2 station (N , E ) situated on the northwest part of Skagerrak close to the Norwegian coast Validation of Chl-a from another large fjord on the West coast of Norway In Pedersen et al. (2012) MERIS data was compared with sensor data from the Ferrybox system in Trondheimsfjorden (Figure 49) in a surveillance monitoring program. The transect presented in Figure 50 represent case 2 waters and the MERIS Chl-a data presented is from the C2R processor (version unknown) together with chlorophyll a fluorescence data from the Ferrybox system. The Chla fluorescence data are a proxy for Chl-a, but with a calibration with in situ data give us a proxy close to the real values. Even though the C2R data is lower than sensor data for large areas, they are within the large variation of the sensor dataset (Figure 50). A comparison between 90 percentile of the insitu data, Ferrybox data and C2R from the referred study result in lowest values for C2R data, but with highest number of observations. 73

74 Figure 49: MERIS Case 2 image from the Trondheimsfjord with the Ferrybox transect shown Figure 50: MERIS data from a transect in the Trondheimfjord for one year compared with all Ferrybox Chlorophyll-a fluorescence data. 5.5 Conclusion The comparison study of the three different processors with in situ data for best results is far from finalized for Norwegian waters, especially for lower absorbing water at the west coast of Norway where the major part of the aquaculture industry are located. Previous studies, with older versions for some of the processors (C2R in particular) show that the MERIS standard Algal_2 product is overestimating the Chl-a concentration with approximately 14% while the TSM product has no significant difference from the in situ data. Still, this investigation confirms the overall findings in Sørensen et al. (2007a) that the MERIS Algal_2 and TSM products from the 2 nd processing are in reasonable good agreement with in-situ data in the open areas of Skagerrak. The deviation seen in the Chl-a is not large taking into account that a variation of 20% in the validation methods for Chl-a is often seen between validation laboratories (Sørensen et al. 2007b). 74

75 When comparing the three processors in particular, the overall impression from the preliminary investigations is best result from the FUB processor for the Chl-a parameter in Skagerrak and the C2R processor for the TSM parameter. The CC processor seems to fail for both parameters in this region. 75

76 6 Portugal 6.1 Description of region The coast at Sagres in SW of the Iberian Peninsula has a characteristic narrow continental shelf that extends rapidly to depths of over 1000 m (Figure 47). As the summer months are dominated by northerly winds, an offshore Ekman transport drives an upwelling of relatively cool, nutrient rich, subsurface waters along the west coast; after a prolonged period of northerly winds, upwelled waters will circulate around Cape Saint Vincent at the south-western tip of the Iberian Peninsula and flow eastwards along the southern shelf, including the Sagres area (Loureiro et al. 2005). The variations in the productivity of waters around Sagres due to changes in water masses provoke fluctuations in the bio-optical properties of these waters. The high primary productivity off Sagres has been the incentive for recent investment in bivalve offshore aquaculture in this coastal region (MADRP DGPA, 2007). Figure 51: Location of validation sites for MERIS in SW Iberian Peninsula. Station A is adjacent to the concession for offshore aquaculture belonging to Finisterra Lda. (from Cristina et al. 2015b) Sagres has been used as a validation site for the MERIS sensor on the ESA environmental satellite ENVISAT, between 2008 and There a number of advantages to this region as a validation site: many days in the year with clear skies; potential for validation activities all year round; a narrow continental shelf enabling access to open ocean conditions within 20 km from the shore (see Station C in Figure 47); minimal co-varying input from land and deep water adjacent to the coast, reducing the number of variables affecting adjacency contamination from the land (see Station A and B in Figure 47). In summary, it is possible to achieve a high frequency of measurements close in time between the in-situ sampling and the acquisition of the relevant satellite image. With the AQUA-USERS project in mind, it is important to highlight that Station A in Figure 47 is the location of the Finisterra L da concession for offshore aquaculture; this company is one of the Users in this project. The following contribution to this deliverable on regional optical algorithms is based on a comparison between in-situ and satellite (MERIS) data collected between (Icely et al. 76

77 2012). The data has been submitted to the ESA MERMAID data base with the number of match ups achieved at the different sampling sites and processing procedures shown in Table 13. Much of this work has been published in peer reviewed journals or is under submission to peer reviewed journals (Cristina et al. 2013, 2015, 2015b; Goela et al. 2013, 2015, 2015b). With regard to the quality of the data discussed, SGM has successfully participated in inter-calibration exercises for both the radiometric ( Zibordi et al. 2012) and HPLC measurements for pigments (Icely et al. 2012). N A B C Initial No VIC 90 (67%) 108 (82%) 115 (90%) VIC 103 (77%) 97 (74%) 115 (90%) ICOL 51 (38%) 108 (82%) 113 (88%) VIC+ ICOL 85 (63%) 113 (86%) 115 (90%) Table 13: Number of matchups (N) with potential for MERIS validation in Sagres (Initial). ). Number of matchups after processing procedures, without vicarious adjustment (NoVIC); with vicarious adjustment (VIC); with ICOL processing (ICOL); with vicarious adjustment and ICOL processing (VIC+ ICOL) at Station A (A), Station B (B), and Station C (C). (from Cristina et al. 2014) 6.2 Overview of processors MERIS standard processors Based Cristina et al Match-up data for in situ and MERIS water leaving reflectance (ρ w ) from the standard MERIS processor MEGS 8.1 has been compared to test the efficacy of different combinations in the processing chain for all the viewing and oceanic conditions at Sagres. These combinations include no vicarious adjustment (NoVIC) and vicarious adjustment (VIC); we use the term vicarious adjustment, rather than vicarious calibration, as Lerebourg et al. (2011) use VIC to adjust internally the Level 2 Ocean branch processing and not to modify the Level 1 top of atmosphere (TOA) radiometric calibration. The VIC procedure adopted for MERIS (Lerebourg et al. 2011) follows a methodology similar to that of Franz et al. (2007), using data from the South Pacific gyre and Southern Indian Ocean for calibration in the near infrared (NIR) and using data from MOBY, and additional clear water validation sites for the calibration at visible wavelengths. The adjustment is applied to TOA reflectances that are calibrated and corrected for glint. The bands 709 and 779 nm in the NIR are used for reference, whereas coefficients are used to derive the remaining NIR bands. The greatest differences between the reference and the derived bands occur at 865 and 885 nm. Lerebourg et al. (2011) ascribe these changes to possible residual stray light in the sensor; however, errors in glint correction may be an alternative explanation. The coefficients for the visible wavelengths are derived using the entire processing chain and tend to show a stronger effect in the blue wavelength. The net effect of these changes is to bring the radiometry closer to the 1:1 relationship for clear-water sites (MQWG 2012) Use of ICOL An additional combination to the MERIS processing chain is the Improved Contrast between Ocean and Land (ICOL) processor, which has been implemented to correct for adjacency effects. Since VIC is 77

78 also applied to the entire processing chain, and ICOL substantially changes the processing in nearshore waters, the effect of ICOL is tested in this study both without vicarious adjustment (ICOL) and with vicarious adjustment (VIC + ICOL) Overview of regional bio-optical algorithm for Portugal Based on Cristina et al. 2015b The standard MERIS data product Algal_1 (also referred to as API1 or MER API1 in the figures) is the total chlorophyll-a (TChla) determined through a polynomial regression based on multiple band ratios of MERIS normalized water-leaving reflectances (ρ w ) where TChla comprises the sum of monovinyl chlorophyll-a, divinyl chlorophyll-a, chlorophyllide a, phaeophytide-a and phaeophorbidea. The regional TChla product has been computed using inversion schemes based on the Multilayer- Perceptron (MLP) neural net (Kajiyama et al. 2013). Results are derived from both the in-situ (Rrs SITU ) and the MERIS bottom-of-atmosphere (Rrs MER ) remote sensing reflectance. Corresponding data products are henceforth denoted MLP(Rrs SITU ) and MLP(Rrs MER ), respectively. The applicability range of the regional algorithm was evaluated through a novelty detection scheme (Bishop 1994; D Alimonte et al. 2014). This approach has already been used in former investigations to filter MLP inputs (Bishop 1994, D Alimonte et al. 2003) by assessing how well the input is represented within the MLP training dataset (Bishop 1994). The MLP applicability range adopted here is based on a novelty index η computed from the Principal Component Analysis (D Alimonte et al. 2014) of logtransformed Rrs values. Key features of this novelty index are: 1) η is bounded between [0, ]; 2) the more the Rrs spectrum is similar to the in-situ measurements used for training the regional MLP, the lower is its novelty index η; 3) a Rrs spectrum is considered within the MLP applicability range when η is below a threshold; and, 4) independent analyses have shown that a threshold η=3 fits general application requirements. Different sets of center wavelengths (413, 443, 490, 510, 560 and 665 nm) were tested to derive the regional TChla concentrations from space-born Rrs. For the match-up analysis, the following quantities are compared with the in-situ TChla: 1) Algal_1; 2) MLP(R MER rs ); and 3) MLP(R SITU rs ). Data products were evaluated through the scattering and the bias as absolute (ε) and signed (δ) biased percent differences, respectively: N 1 y N i xi 1 yi xi 100%; 100% Eq. 18 N x N x i 1 i i 1 i where y i indicates one of the tested products, the x i is the in-situ reference, i is the sample index, and N is the number of samples The comparison of Algal_1 and the MLP(R rs MER ) products maps was performed in addition to the match-up analysis. These data products are evaluated through the absolute (ε * ) and signed (δ * ) unbiased percent differences: N N * 1 yi xi * 1 yi xi 200%; 200% Eq. 19 N y x N y x i 1 i i i 1 i i where x i and y i are the MLP(R rs MER ) and Algal_1 values, respectively, taking the mean of the two values as a reference. 78

79 6.2.4 Algorithms for the Sagres WISP-3 measurements The OC4Me (Eq 1) is the algorithm that has been used for Sagres validation of the MERIS sensor, but it is planned to try out the regional algorithm (Cristina et al. 2015b & Goela et al. 2015b) with the WISP-3 readings. With regard to TSM,the ratio R(0-,720)/R(0-,500) provides the best validation result for TSM in the WISP-3 data set. TSMdrived=2.641+(lnR(0,720)/(lnR(0,500)) Eq. 20 where R(0-) is the irradiance reflectance at 720 and 500nm. 6.3 Validation data In situ measurements related to MERIS Algal pigment product Radiometric parameters and concentrations of optically active water constituents were estimated between September 2008 and March The measurements were consistent with the MERIS validation protocols. The measurements were timed to coincide with the MERIS overpass within 30 minutes at Station A and within 1.5 hours at Station B and C. The distance from the coast of stations A, B, C were 2, 10 and 18 km, respectively (Figure 51). The clear skies and flexible access to boats at Sagres have enabled approximately 300 matches between MERIS products and in-situ data for the three stations (see Table 13). This corresponds to 26 sampling days during the period of sampling. A match-up is considered when: (i) in-situ measurements coincide with the MERIS overpass; (ii) there are clear sky conditions; (iii) there are good sea conditions; and (iv) the satellite data were filtered for contamination (non-flagged) pixels. Coincident with the radiometric measurements, water samples were taken at each station with a Niskin bottle at three depths, (0 m, ½ Secchi depth, and 1 Secchi depth). The in-situ parameters measured at these three stations were the total concentration of Chlorophyll a (TChla) and its degradation products, the total suspended matter (TSM), and the absorption of yellow substance (YS). This report will focus mainly on TChla which was determined by High Performance Liquid Chromatography (HPLC); the method and the analysis of this parameter is explained in more detail in Goela et al and Goela et al As part of an effort to improve the quality of the comparison between Algal_1 and in-situ TChla, all the in-situ data were optically weighted by using the protocol described in Smith et al. (2013) for the assessment of MERIS optical products in the shelf waters of the KwaZulu-Natal Bight, South Africa. At Sagres, the water samples were collected at three different depths and these measurements were used for the depth integration (Cristina et al. 2015) Statistical analysis for the match-up analysis MERIS RR satellite images were used for the time series between September 2008 and March For the match-up days, the satellite data used was from the MERIS FR satellite images. Parameters used to assess the results from the matchups and quantify the agreement between satellite Level 2 products (yi) and in-situ measurements (xi) include (i) the mean ratio (MR) in Eq 21, (ii) the absolute percentage difference (APD) in Eq. 22; (iii) the average of relative percentage difference (RPD) in Eq. (23); and (iv) the intercept, slope and the coefficient of determination (R 2 ). The match-up index is i and the number of matchups is N: 1 N y MR i N i 1 x i Eq

80 1 N y APD i x i 100 % N i 1 x i Eq N y RPD i x i 100 % N i 1 x i Eq In situ measurements related to WISP-3 estimate for TChla and TSM Thirty sampling campaigns were carried out weekly at the aquaculture site between the 14th March 2014 and 5th December Water samples were collected for TChla and TSM, at the same time as optical readings were taken using the WISP-3 hand-held instrument. 6.4 Results Comparison between standard MERIS products and in situ data From Cristina et al. 2014, 2015 The results presented here are essentially for water leaving reflectance(ρ w ) and/or remote sensing reflectances (Rrs) (note that Rrs can be approximated from ρ w by scaling by π; i.e., Rrs=ρ w /π), which are the basis for the MERIS Algal Pigment products. All the data, apart from that collected with the WISP-3 radiometer in 2014, were based on the validation work between 2008 and A comparison of the four combinations used on the matchup data is shown in Figure 52, where scatter plots relate MERIS ρ w on the y-axis to in situ ρ w on the x-axis for wavelengths 443, 490, and 560 nm; these are considered the most important bandwidths contributing to the MERIS API 1 algorithm. The uncertainty values for ρ w are represented as horizontal error bars in Figure 52. In general, at oceanic Station C, there are reasonable R 2 values of between 0.74 and 0.86 for the regression analyses of the NoVIC, VIC, ICOL, and VIC + ICOL combinations. However, at the inner Stations A and B there is considerable variation between the R 2 values; most of the low values occur at the coastal Station A for ICOL (R 2 from 0.33 to 0.61), but at Station B there is a marked improvement for ICOL (R 2 from 0.61 to 0.80). At Station A, the best values are for NoVIC (R 2 from 0.44 to 0.69) and VIC + ICOL (R 2 from 0.59 to 0.76), whereas at Station B, the best values are for NoVIC (R 2 from 0.66 to 0.82). 80

81 Figure 52: Station C. The 1:1 relationship is represented by the solid diagonal line, whilst the linear regressions are represented by the dashed lines. For each match-up point, the vertical bar indicates one standard deviation within 3 3 pixel box used for the match-up and the horizontal bar represents 8% uncertainty budget for the in situ VIC + ICOL(blue) (from Cristina et al. 2014) Figure 53 shows a comparison between MERIS Algal_1 and in-situ TChla, both without and with ICOL. The statistics from the three stations show that the MERIS API 1 without and with ICOL are overestimated relative to the in-situ TChla where the MR>1 and the RPD>0. At Station A, the MERIS API 1 with adjacency correction show the best agreement with in-situ data, increasing the R 2 and decreasing the uncertainties. However, the same does not occur at the other two stations, where there is a better agreement between data without the ICOL processor. In general, R 2 increases offshore and the uncertainties decrease, with the exception of Station B for the case of the MERIS API 1 data without ICOL that shows better results than Station C. At the study area, all the available MERIS Level2 satellite images between September 2008 and March 2012 have been extracted, where the images are free from cloud cover and have been 81

82 filtered for contamination. Figure 53 shows the variability of Algal_1 throughout this period at the three stations, with the in-situ data showing higher concentrations at Station A, declining at Station B and culminating with the lowest values at Station C. There are also seasonal differences with higher values occurring mainly between early spring until the end of the summer, and lower values occurring during the winter. Figure 53: Scatter plots of MERIS algal pigment index 1 (API 1) versus in situ optical weighted total concentration of chlorophyll a (Cf equivalent to TChla) at Station A, B and C.. 1:1 relationship is represented by solid diagonal line, whilst the linear regressions are represented by the dashed lines. The green dots represent the MERIS API 1 without ICOL processor and the blue dots represent the MERIS API 1 with the ICOL processor (from Cristina et al. 2015) Comparison between standard MERIS algal pigment index (Algal_1), equivalent product from a regional algorithm, and in-situ data From Cristina et al. 2005b Match-up results presented in Figure 54 and Table 14 indicate that the tested product with the best agreement with TChla REF is MLP(R SITU rs ). Corresponding statistical figures are: scattering ε=29%, bias δ=1%, coefficient of determination R 2 = The reported δ values for the three stations indicate an overestimate of Algal_1, MLP(R MER rs ) and MLP(R SITU rs ) with respect to TChla REF. Table 14 also shows the scattering and bias associated to each sampling station; the largest uncertainties correspond to Algal_1. The results from the standard and tested products are less accurate at Station A, and tend to improve in Station B and Station C. For the Algal_1 and the MLP(R MER rs ) products, this result suggests the presence of adjacency effects, and possibly limitations in the aerosol models near the coast. However, in the case of the MLP(R SITU rs ), which uses in-situ radiometric data as input, the degradation of the data product accuracy also indicates an increased complexity of seawater optical properties near to the coast. A matchup analysis (Figure 54) and a product maps comparison has been performed in this study to evaluate standard and the regional algorithms with respect to in-situ TChla measurements. The cross-checking of the statistical results of these two independent assessments provides additional insights. Namely, the matchup analysis indicates that the bias between Algal_1 and TChla REF is 63%, whereas the bias between MLP(R MER rs ) and TChla REF is 24%. That is in agreement with the independent product map comparison indicating a bias of 46% between Algal_1 and MLP(R MER rs ); i.e., close to the difference of the two former results. The convergence of these analysis supports the comparison of standard and regional products maps as a means to analyse space mission deliverables, especially when the number of match-up data is limited 82

83 Figure 54: Comparison between (a) Algal_1 with the TChlaREF as well as equivalent products computed with regional algorithms(b) MLP(RrsMER) and (c) MLP(RrsSITU). N ε(%) δ(%) R 2 A B C All A B C All A B C All A B C All Algal_1 vs TChla REF MLP(R MER rs ) vs TChla REF MLP(R SITU rs ) vs TChla REF Table 14: Comparison between standard (Algal_1), regional bio-optical algorithms MLP(RrsMER) and MLP (RrsSITU),and TChlaREF (from Cristina et at 2015b) The comparison between standard MERIS pigment index values and products derived with the regional MLP are presented in Figure 55. The MLP version adopted for this comparison also uses input R rs values at 490, 510 and 560 nm. Figure 55 (a) shows results for MLP(R rs MER ) whilst Figure 55 (b) shows the applicability range of MLP(R rs MER ) with R rs spectral values within the range shown a green colour and those outside the range are yellow and red. The area displaying an applicability range with a novelty index less than 3 extends over a significant portion of the MERIS image for the Atlantic off the Iberian Peninsula. As expected, zones out of the applicability range are mostly near to the coast or in clearer oceanic waters. Three regions of interest (ROI) are identified in Figure 55 (e) to test the performance of the Sagres region MLP in different areas along the coast of Portugal. These include the Figueira da Foz region influenced by the Mondego river plume (Northern ROI in red), the Lisbon region (Central ROI in green), and the Sagres region where field data for the development of the regional MLP have been collected. Algal_1 values are shown in Figure 55(c), whereas the difference between Algal_1 and MLP(R rs MER ) is in Figure 55 (d). Pixels where the relative difference between Algal_1 and MLP(R rs MER ) is less than -35% are coloured in pink, but where the relative difference between MERIS and regional products is between -35% and 35%, the colour is green but, finally where the MLP(R rs MER ) overestimation is more than 35%, the color is in yellow. Results derived from R rs spectra within the MLP(R rs MER ) applicability range are mostly within the (-35%, 35%) relative difference region cf. Figure 55(b) and Figure 55(d). The scatter plot in Figure 55(f) reports Algal_1 versus MLP(R rs MER ) data points by considering an ensemble of all ROI samples and using the same colour scheme adopted in Figure 55(e). Statistical figures indicates ε=47% and δ=46%, which indicate a systematic difference between data products. The coefficient of determination is R 2 =0.91. Statistical data confirm the tendency to overestimation in coastal regions. 83

84 Figure 55: Comparison between standard MERIS pigment index product and results obtained by applying the regional MLP for the Algarve region (from Cristina et al. 2015). Investigations have focused on the evaluation of the Algal_1 product. Data analyses in selected ROIs indicate this standard MERIS product tends to be higher than equivalent MLP estimates. This tendency to overestimation mostly features a systematic bias and a reduced data scattering. A possible explanation can be systematic uncertainty depending on the atmospheric correction process (Cristina et al. 2014). Another element to take into consideration can be an increased level of yellow substances (YS) and/or non-algal particles (NAP) in the study area with respect to Case 1 waters with the same TChla amount (Cui et al. 2014). Both YS and NAP tend in fact to reduce R rs values in the blue wavelengths. On the other hand, the NAP component induces a quite uniform increase of R rs in the visible domain. The composite effect of these factors can result in a higher spectral slope of R rs in the blue-green spectral interval, leading the algorithm for Algal_1 pigment index retrieval in Case-1 waters to overestimate TChla values. 84

85 6.4.3 Validation of other products Although most of the analysis on the Portuguese site has focused on the validation of radiometric and pigment data for the MERIS products Algal Pigment Indices 1 and 2, other in situ variables have been measured at the Sagres validation sites including: water transparency; the spectral diffuse attenuation coefficient, Kd(490); and the absorption coefficients at 443 nm for phytoplankton (aph), non-algal particles (a nap ), and yellow substance (ays). The statistics for these variables are summarised in Table 15. All three stations are characterized by low turbidity, with mean Secchi depths of between 11.0 and 16.5 m. The means of the Kd(490) range between 0.08 and 0.13 m 1, with minimum and maximum values of 0.03 and 0.25 m 1, and relate to the means for Algal_1 of between 0.08 and 2.27 mg m The studies comparing some of these in situ measurements with MERIS products as well the development of bio-optical regional algorithms for TSM, Kd, coefficients of absorption has only just started, although it is already evident that TSM for example is much more variable than the pigments. Summary of Inherent Optical Properties at Sagres Depth (m) Transparency (m -1 ) K d (490) (m -1 ) API1 (mg m -3 ) TSM (ug. - l) a ph (443) (m -1 ) a nap (443) (m -1 ) a ys (443) (m -1 ) Station A Station B Station C Average±SD 41.1 ± ± ± 40.5 Min. - Max Average±SD 11.0 ± ± ± 6.1 Min. - Max Average±SD 0.13 ± ± ± 0.04 Min. - Max Average±SD 0.91 ± ± ± 0.34 Min. - Max Average±SD 1.87 ± ± ± 0.63 Min. - Max Average±SD 0.06 ± ± ± 0.03 Min. - Max Total % (Average±SD) 48.4± ± ±21.5 Average±SD 0.01 ± ± ± 0.00 Min. - Max Total % (Average±SD) 8.8± ± ±7.4 Average±SD 0.05 ± ± ± 0.03 Min. - Max Total % (Average±SD) 42.8± ± ±22.4 Table 15: Summarises the values for in situ data from Sagres (from Cristina et al. 2014) There have been substantial studies using the in-situ measurements for coefficients of absorption in Sagres to assess whether there are differences in the bio-optical properties of Sagres waters under bloom and no-bloom conditions, and whether the in-situ data can be related to the MERIS products. In the study area, a ph changed markedly between bloom and no-bloom conditions, increasing by a factor of 2 to 3 in bloom conditions at both wavelengths maximums of 443 and 678 nm (Figure 56). Taking into account that the mean concentration in the bloom period is 2.8 mg l -1 and in the nobloom is 0.50 mg l -1, these in-situ results are in agreement with the models presented in Goela et al. (2013) for comparing TChla concentration with a ph (443) and a ph (678). These models predicted values of m -1 and m -1 for a ph (443) and a ph (678), respectively, at a concentration of 2.8 mg l -1 for TChla, and values of m -1 and m -1 for the same coefficients, at a concentration of 0.5 mg l - 1 for TChla. These values from the models are similar to the in-situ values from this study, with 85

86 averages of m -1 and m -1 for a ph (443) and a ph (678), respectively, at a concentration of 2.8 mg l -1 concentration for TChla, and values of m -1 and m -1 for the same coefficients, at a concentration of 0.5 mg l -1 for TChla. Besides these differences in the values for a ph, there are changes in the shapes of the coefficient spectra, with increasing blue:red ratios in no-bloom conditions (Figure 56). Figure 56: Spectra of phytoplankton coefficient of absorption under a) bloom and b) no-bloom at Sagres (from Goela et al. 2015) Preliminary analysis of WISP-3 products Correlations between the WISP-3 retrieved Chl-a versus the in situ data show reasonable correlations with relatively low RMSE errors (Figure 57). However TSM versus in situ was much more variable (Figure 58). 86

87 Figure 57: Scatter plot of the Chl-a derived from the WISP-3 spectra versus the in situ Chl-a. Figure 58: shows the scatter plot between TSM derived from the WISP-3 versus the in situ TSM. The R2 is 0.62 and the RMSE is 1.13 mg/l. This preliminary work with the WISP-3 has been submitted to the Proceedings for an ESA symposium (Fragoso et al. 2015), but will be reassessed with more results from WISP measurements during 2015 and in the future in Conclusion Standard processing Approximately 130 matchups between MERIS water-leaving reflectances (ρ w ) and in situ measurements have been obtained between 2008 and 2011 for each of three stations at Sagres (A, 87

88 at 2 km; B, at 10 km; and C, at 18 km). These numbers are reduced during the MERIS third reprocessing procedures dependent on the combinations of NoVIC, VIC, ICOL, and with ICOL + VIC: with approximately 20 60% for the inshore Station A; 20% for Station B; and 10%for the offshore Station C. The statistical comparison of the matchups between MERIS ρ w and the in situ ρ w shows a better coefficient of determination, and less uncertainty and bias, at the centre of the visible spectra ( nm) than at the extremes (412 and 665 nm). The oceanic Station C at Sagres is of particular interest because it has characteristics in common with both BOUSSOLE and AAOT validation sites. However, vicarious adjustment results in poorer statistics, with the regression slope being closer to unity at all wavelengths without vicarious adjustment. With the exception of the wavelengths 412 and 443 nm for R 2, the intercepts, MR, RPD, and APD are better without vicarious adjustment applied. The differences for MR and APD indicate that the vicarious adjustment results in a marginal improvement in these two bands, whereas the RPD indicates that the vicarious is an overadjustment. Overall, Station C, Sagres site, has achieved better matchup statistics for the MERIS sensor than BOUSSOLE. The statistics for both NoVIC and VIC are similar to those for AAOT. Differences can be attributed to the more turbid conditions at AAOT and low values for the red at Sagres. The ICOL processing shows mixed results with improvements to matchups occurring only for some campaigns. The uniformity between the bio-optical characteristics of the stations at Sagres indicate that validation data from Sagres would be particularly useful for improving the algorithms close to the shore and for the development of regional algorithms. Although the validation of TSM and Kd have not been discussed in this section, the Sagres in-situ data is now being compared to the appropriate MERIS products Development of bio-optical regional algorithms for Sagres The comparison between the standard and regional Algal Pigment Index 1 (Algal_1) estimates in the Atlantic off the southwestern Iberian Peninsula. Standard Algal_1 data are those delivered by the Medium Resolution Imaging Spectrometer (MERIS) orbiting sensor. Equivalent quantities are computed by applying a regional inversion scheme using as input both MERIS and in-situ Rrs. Reference data for the development of the regional algorithm and for the analysis of tested products include field measurements of total concentration of chlorophyll a (TChla) and coincident Rrs values collected at different distances from the coast. Validation results, based on matchup analysis, identifies a systematic overestimation of standard Algal_1 versus the reference TChla values. The additional comparison of product maps in selected regions of interest confirms this finding, and demonstrates the feasibility and relevance of using regional algorithms for investigating space- born products. Analogous applications are hence devised for the early-stage evaluation of the forthcoming SENTINEL-3/OLCI data products. A similar approach has been used by Goela et al. (2015b) for Algal Pigment Index 2 (Algal_2) and the Algarve team is now exploring how to use the regional algorithms for the WISP-3. 88

89 7 UK 7.1 Description of region The seas around Scotland are directly affected by oceanic circulation (Figure 59). The steep continental slope separates oceanic regions and the shelf sea. Most waters from the North Atlantic that enter the North Sea do so between Orkney and Shetland, around the north east of Shetland and through the deep Norwegian Trench. Tidal currents are predictable and stronger than the non-tidal currents in many areas. Tidal currents cause mixing in the water column and therefore often determine the location and extent to which the water column is stratified into different distinct layers. Although tidal currents are important for vertical mixing, they are not very important for the overall transport of water. The non-tidal circulation in the North Sea is predominantly anti-clockwise and circulation on the shelf west of Scotland, (the Scottish Coastal Current) is mainly northwards. However this circulation is strongly affected by winds and density-driven coastal currents and jets, which can lead to large changes in currents and even a reversal of this general pattern for short periods. Figure 59: Circulation map representing the general circulation pattern within the North Atlantic and North Sea areas. It should be noted that flow is not confined to these arrow tracks. Circulation of Atlantic water is shown by the white arrows with arrows with coastal circulation represented by the green arrows (Taken from Baxter et al. 2011). Star shows the position of the UK site for AQUAUSERS. The phytoplankton community in Scottish waters is typical of those found in northern latitudes. During spring, as the days lengthen and the waters begin to warm, there is a rapid burst of phytoplankton growth, dominated by small rapidly growing diatoms (spring diatom bloom). During the summer months the phytoplankton community is dominated by dinoflagellates. In some years, but not all, there is a second bloom of diatoms in the autumn. The diatom species dominating this autumn bloom are larger than those found in the spring. The water temperature on the west coast tends to be 1-2 C warmer than the east during the spring period. This may explain the difference in 89

90 the timing of the spring diatom bloom between the phytoplankton monitoring sites in these two regions. During the spring bloom the highest number of diatoms is observed in March at the monitoring site on the west coast whereas on the east coast the highest number of diatoms is observed in May (Figure 60). Figure 60: 2 Seasonality of diatoms on (A) west coast and (B) east coast. The green dots represent the monthly average of diatoms observed and the green triangles represent the overall monthly average since 2000 (Figure taken from Baxter et al. 2011) 7.2 Overview of processors The algorithms for water quality parameters estimation in the UK waters have been implemented in the SeaWiFS Data Analysis System (SeaDAS) software package. The SeaDAS version 6.4, developed by the National Aeronautics and Space Administration (NASA), provides a common framework for atmospheric correction of MERIS data, estimation of chlorophyll-a concentration, diffuse attenuation coefficient Kd(490) and Coloured Dissolved Organic Matter (CDOM). The SeaDAS processor implements the atmospheric correction scheme originally developed by Gordon and Wang (1994). The scheme uses two bands in the near infrared to evaluate the contribution of aerosols to observed reflectances. It can be applied to MODIS, MERIS and SeaWiFS sensors. The chlorophyll-a products were generated from MERIS data by applying the Ocean Color Chlorophyll (OC) 4v6 algorithm and IFREMER OC5 algorithm implemented in L2GEM module of SeaDAS package (Gohin et al. 2008). The OC5 algorithm was developed to correct the chlorophyll-a overestimation of OC3M algorithm in the Bay of Biscay and the English Channel coastal areas. The algorithms were specially tuned to account for the difference in spectral bands between MERIS and other sensors (NASA ). The K d (490) product was generated using the standard KD2 algorithm (Mueller 2000) that was also corrected for MERIS nominal centre wavelengths and implemented in in L2GEM module. CDOM was estimated using absorption due to CDOM and detritus at 443nm as a proxy. CDOM was generated by applying the Quasi-Analytical Algorithm (QAA) integrated into SeaDAS (Lee et al. 2006). For non-algal SPM estimation PML will be using IFREMER semi-analytical algorithm (Rivier et al. 2012) that was implemented as a separate processing module written in Python. SPM product was generated by applying this processing module to MERIS reflectance data. 90

91 7.3 Validation data For validation, in situ measurements of Chl-a, TSM, IOP or K d were not available for the Scottish coast within the duration of the project. Therefore, datasets available from the literature and other local data sources from the UK coast were used (i.e. from the Western Channel Observatory ( )). The most comprehensive dataset for validation and testing of algorithms in coastal waters to date is the one collated during the ESA project Coastcolour ( This dataset (Nechad et al. 2015) comprises three types of data: a) match-ups: where in situ water quality parameters (i.e. Chl-a, TSM, IOP and K d ) are available simultaneously with a cloud-free Medium Resolution Imaging Spectrometer (MERIS) product; b) in-situ reflectances: where an in situ waterleaving reflectance measurement (denoted by RLw which is derived from the remote-sensing reflectance, Rrs following RLw = π Rrs) is available simultaneously with an in situ WQ; c) simulated reflectances: where RLw have been simulated by Hydrolight for specified sets of IOPs and geometrical conditions. In situ data come from 17 areas around the world, with unbalanced temporal distribution. In total the following measurements were available, per parameter: 1153 for Chl-a (239 in the North Sea), 538 for SPM (212 in the North Sea), 610 for the absorption coefficient (a) (117 in the North Sea) and 330 for the backscattering coefficient (b b )(28 in the North Sea) and 167 for K d (6 in the North Sea). Of those data only 12 points had satellite matchups with Rrs, Chl-a and SPM simultaneously for the North Sea. This scarcity of matching in situ data lead to the evaluation of the algorithms by using simulated reflectances (dataset c above). The distribution of the simulated dataset (CCRRv1) is shown in Figure 61. A total of 5000 simulations were computed from the combination of Chl-a (CHL, in the figure), mineral particles (MP) and yellow substance (a g (443)). Figure 61: Simulated a) Mineral Particles (MP) and b) ag(443) versus the simulated Chl-a concentrations in CCRRv1 (Nechad et al. 2015). The colours indicate the ranges in the MP, ag(443) and Chl-a. The concentrations of MP, a g (443) and Chl-a were used with Hydrolight to compute hyperspectral RLw (2.5 nm spectral resolution) and then sub-sampled to the MERIS, MODIS and SeaWiFS bands as detailed in Nechad et al. (2015). 91

92 Figure 62 is shown as a comparison of CCRRv1 dataset with previous datasets (IOCCG) and in-situ data from CoastColour. The simulated data in CCRRv1 have a larger dispersion for the greater values of absorption is mainly due to the tolerance in the dataset of greater ranges of mineral particles and chlorophyll concentration, which in turn lead to extended ranges in backscattering. Figure 62: Comparison between simulated data: IOCCG dataset (IOCCG, 2006) and the CCRRv1 dataset (Nechad et al. 2015). a) variations of the remote sensing reflectance Rrs (440) with a(440), b) variations of RLw band ratio 410:440 with respect to RLw band ratio 490:555. The blue diamonds represent the NOMAD (Werdell et al. 2005) subset of in situ data extracted from the SeaBASS dataset and used in the algorithm testing in IOCCG (2006) Quantitative and qualitative assessment A suite of quantitative indicators (Nechad and Ruddick 2012) have been used to assess the performance of the algorithms used by PML, comparing the value introduced in Hydrolight (x input ) with the value retrieved by the algorithm (y alg ), over a N number of valid retrievals. The indicators of quality of the retrieval are: Root mean squared error on the log 10 converted data is n 2 i 1 ( yalg xinput ) RMSE Eq. 23 N Correlation coefficient on the log 10 converted data, r The regression lines Log10(y alg )=αlog10(x input )+β Eq. 24 The Mean Absolute Percentage Error (MAPE), for linear values is n 1 i 1 yalg xinput MAPE Eq.25 N x 7.4 Results input The report that evaluates the performance of the algorithms selected by PML is the Round Robin Coastcolour report (Nechad and Ruddick, v2.2, December 2012). The main results from this report are summarised here with respect to the algorithms used by PML in the North Sea area for the Scottish salmon producer and the Southern Portugal site. 92

93 Figure 63 summarises the results concerning the chlorophyll concentration, from using the OC5 algorithm. Figure 63: Chlorophyll comparison between input values to Hydrolight (i.e. reference) and retrieved by the OC5 algorithm (i.e. computed). Statistics are described in Section Figure 7.6 summarises the results concerning the inherent optical properties, including CDOM. a) b) 93