aquaculture user driven operational remote sensing ... · aquaculture user driven operational...

AQUAculture USEr driven operational

Remote Sensing information services

AQUA-USERS is funded under the European Community’s 7th Framework Program (Theme SPA.2013.1.1-06: Stimulating development of downstream services

and service evolution, Grant Agreement No 607325)

Deliverable 7.5

Future sensors and VA evolution report

PML, Deltares, GRAS, FFCUL, WI, SGM

2016-09

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D7.5:Future sensors and evolution of trends in value adding

27/10/2016

Task 7.5: Future sensors and evolution of trends in value adding

Deliverable 7.5: Future sensors and VA evolution report

Lead beneficiary PML (3)

Contributors Deltares (9), GRAS(6), FFCUL(4), WI(1)

Due date Month 35 (30 September 2016)

Actual submission date 27/10/2016

Dissemination level PU

Change record

Issue Date Change record Authors

0.1 01/07/2016 First draft PML

0.2 17/10/2016 Second draft PML et al.

1.0 26/10/2016 Final 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

Kurekin, A., Miller, P., Huber, S., Peters, S., Sa, C., and Eleveld, M. (2016) “Future sensors and VA evolution”, AQUA-USERS deliverable D7.5, EC FP7 grant agreement no: 607325, 46p.

© Copyright 2016, the member of the AQUA-USERS consortium. All rights reserved.

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Task objective (from DoW)

Evaluating the impact that future remote sensing sensors and systems might have on the retrieval and delivery of the information on key parameters to the Users

Analysis of main trends in value adding of remote sensing methods

Sustainability support of remote sensing services

Assess and recommend future improvements in resolving power and coverage of EO systems

Scope of this document

The scope of this document is the analysis of the current and expected evolution and future development of new EO sensors and in situ instruments that may affect the user-driven methods and services developed within the project. The document also includes a survey of related projects currently running in Europe and the main trends in value adding of remote sensing methods.

Abstract

With the introduction of new and more advanced EO sensors and in situ instruments it is important to support sustainability of methods and services that were developed within AQUA-USERS in terms of evolution and adaptation to new sensors.

The trends have been analysed in the development of EO sensors that may affect user-driven remote sensing methods and services. This includes an overview of the future and upcoming polar orbiting sensors (Sentinel-2, Sentinel-3, PACE) and geostationary sensors, such as GOCI, that offer a new way for data processing by exploitation of temporal coherency of natural processes.

The related projects currently running in Europe have been surveyed to identify trends in the downstream remote sensing information services driven by users. The trends in development of EO algorithms and sensors have been analysed. We have also looked into the evolution and future development of in situ measurement instruments and autonomous robotic sensors, such as gliders, that can automatically analyse water samples on-board.

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List of abbreviations

Abbreviation Description

AUV Autonomous Underwater Vehicles

BDRF Bidirectional Reflectance Function

CDAF Climate Data Assessment Framework

CDOM Coloured Dissolved Organic Matter

CMEMS Copernicus Marine Environment Monitoring Service

COMS Ocean, and Meteorological Satellite

CPC Cyano‐phycocyanin

CPE Cyano‐phycoerythrin

CTD Conductivity, Temperature and Depth

CV Coefficient of Variation

DOC Dissolved Organic Carbon

EO Earth Observation

FASTNEt Fluxes Across Sloping Topography of the North East Atlantic

FR Full Resolution

FRM Fiducial Reference Measurements

FWHM Full Width at Half Modulation

GDPS GOCI Data Processing System

GEO-CAPE Geostationary Coastal and Air Pollution Events

GES Good environmental status

GeOCAPI Geostationary Ocean Colour Advanced Permanent Imager

GHRSST Group for High Resolution Sea Surface Temperature

GOCI Geostationary Ocean Color Imager

GODAE The Global Ocean Data Assimilation Experiment

GOES Geostationary Operational Environmental Satellite

GPS Global Positioning System

HAB Harmful Algal Bloom

HABSOS Harmful Algal BloomS Observation System

KOSC Korean Ocean Satellite Centre

KIOST Korean Institute of Ocean Science & Technology

LDA Linear Discriminant Analysis

MASSMO Marine Autonomous Systems in Support of Marine Observations

MODIS Moderate Resolution Imaging Spectroradiometer

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MSFD Marine Strategy Framework Directive

MSG Meteosat Second Generation

MSI Multi-Spectral Instrument

MTG Meteosat Third Generation

NAP Non-Algal Particles

NIR Near Infrared

NN Neural Networks

NTU Nephelometric Turbidity Units

OCI Ocean Colour Instrument

OFS Operational Forecast System

OLCI Ocean and Land Colour Instrument

OLI Operational Land Imager

PACE Pre-Aerosols Clouds and ocean Ecosystems

RR Reduced Resolution

Rrs Remote Sensing Reflectance

SLSTR Sea and Land Surface Temperature Radiometer

SPM Suspended Particulate Matter

SSB Shelf-Sea Biogeochemistry

SST Sea Surface Temperature

SVM Support Vector Machine

SWIR Short Wave Infrared

TSM Total Suspended Matter

TSS Total Suspended Solids

USV Unmanned Surface Vehicles

UV Ultraviolet

List of related documents

Short Description Date

DOW Annex 1 – “Description of Work” 01/07/2013

D 3.1 Regional optical algorithms report 30/10/2015

D 6.3 NRT Case study 3 “daily management report”

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Table of contents

1 Introduction ............................................................................................................... 8

2 New and upcoming EO sensors ................................................................................... 9

2.1 Polar orbiting sensors ................................................................................................................... 9

2.1.1 ESA Sentinel-2 mission, Multi-Spectral Instrument (MSI) .................................................... 9

2.1.2 ESA Sentinel-3 mission, Ocean and Land Colour Instrument (OLCI) .................................. 10

2.1.3 ESA Sentinel-3 mission, Sea and Land Surface Temperature Radiometer (SLSTR) ............ 12

2.1.4 NASA Pre-Aerosols Clouds and ocean Ecosystems (PACE) mission ................................... 12

2.2 Geostationary sensors ................................................................................................................ 13

2.2.1 Meteo satellites ................................................................................................................. 14

2.2.2 Korean Geostationary Ocean Colour Imager (GOCI) ......................................................... 16

2.2.3 GEO-CAPE and the Geostationary Ocean Colour Advanced Permanent Imager (GeOCAPI) 18

2.2.4 The exploitation for indicators, HABS, and aquaculture natural processes ...................... 18

3 Evolution and future development of in situ instruments ......................................... 20

3.1 Introduction ................................................................................................................................ 20

3.2 Near surface remote sensing ...................................................................................................... 21

3.3 Inventory of current Radiometer systems for above water reflectance measurements. .......... 23

3.3.1 WISP-3 ............................................................................................................................... 23

3.3.2 Zeiss-based spectrometers: Trios, Satlantics, Dalec .......................................................... 23

3.4 Recent general developments in the field of radiometry: launch of the ESA FRM4SOC project 25

3.5 Some requirements for next generation field radiometer systems ........................................... 25

3.6 Current innovations in spectrometer design .............................................................................. 29

3.7 Marine autonomous vehicles ...................................................................................................... 31

3.7.1 Types of marine autonomous vehicles .............................................................................. 31

3.7.2 Capabilities of marine autonomous vehicles ..................................................................... 31

3.7.3 Example applications of marine autonomous vehicles ..................................................... 32

4 Development of downstream remote sensing services ............................................. 33

4.1 Currently running European projects ......................................................................................... 33

4.1.1 FP7 HIGHROC ..................................................................................................................... 33

4.1.2 H2020 Co-ReSyF and ESA C-TEP ........................................................................................ 34

4.1.3 H2020 TAPAS ..................................................................................................................... 34

4.2 Trends in value adding of remote sensing methods ................................................................... 34

4.2.1 Sea Surface Temperature .................................................................................................. 34

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4.2.2 HAB detection methods and services ................................................................................ 35

4.2.3 Aquaculture indicators ...................................................................................................... 39

5 Conclusions .............................................................................................................. 40

6 References ............................................................................................................... 41

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1 Introduction

Development of highly user-driven and sustainable services for the aquaculture industry is one of the primary objectives of the AQUA-USERS project. The AQUA-USERS application and services bring together satellite information and in situ observations of optical water quality and temperature, ecological parameters and relevant weather prediction data and met-ocean data to help end-users with making important decisions and improve management efficiency of aquaculture sites.

One of the strategic objectives formulated in the AQUA-USERS project is to “… contribute to sustainable development in a broad sense, especially on the sustainable development of service provision based on GMES satellite data …” (AQUA-USERS DOW). To provide sustainable support of remote sensing services after the lifetime of the project, it is important to assess future improvements of EO sensors and evaluate the impact these may have on the retrieval and delivery of the information on key parameters to the users.

This report is dedicated to a specific task of the assessment of future satellites and in situ monitoring system capabilities to ensure the long term continuity and improvement of the services. New and upcoming ocean colour sensors are analysed in Section 2, including sun-synchronous polar orbiting and geostationary sensors. Section 3 of the Report is dedicated to the analysis of evolution of in situ instruments (including field radiometer systems) and innovations in their design. Marine autonomous systems are presented in Section 3.7. The main trends in development of downstream remote sensing services and remote sensing methods are described in Section 4.

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2 New and upcoming EO sensors

2.1 Polar orbiting sensors

The majority of satellite Earth observation (EO) sensors, such as MODIS, VIIRS, Sentinel 2 and 3 are placed in sun-synchronous polar orbits. The orbit is slightly tilted towards north-west and does not go over the poles. Due to a combination of the orbital plane of the satellite and the rotation of the Earth the sensor can provide global coverage of the Earth covering both land and ocean. The altitude and inclination of the sun-synchronous orbit are selected in such a way that the sensor passes any point at the Earth surface at the same time. This provides nearly the same surface illumination angle and consistent measurements of the surface reflectance in visible and infrared wavelengths. The typical altitude of the sun-synchronous orbit is about 800 km with the orbital period of 100min that gives polar orbiting sensors the advantage in data resolution and coverage compared to geostationary sensors.

2.1.1 ESA Sentinel-2 mission, Multi-Spectral Instrument (MSI)

The ESA Sentinel-2 Earth observation mission is positioned by ESA as a follow on of the SPOT and Landsat missions. The program includes a constellation of two satellites with the first one (Sentinel-2A) currently in the operational phase and the second one (Sentinel-2B) being prepared for launch in April 2017. With a swath of about 290km the Sentinel-2A MSI sensor provides a global coverage of the Earth surface every 10 days. With two satellites on the orbit, the revisit time will be reduced to 5 days (Sentinel-2 on AWS, 2016).

In terms of radiometric resolution that characterises the ability of an instrument to distinguish differences in reflectance, Sentinel-2 MSI demonstrates similar performance to Landsat-8 OLI sensor. The radiometric resolution of the MSI sensor, expressed in bit number, is 12 bits (Sentinel-2 team, 2015).

The multi-spectral instrument on board of Sentinel-2 uses 13 spectral bands, including visible, near infrared and shortwave infrared. The MSI provides spatial resolution of up to 10 meters in the main visible and near-infrared bands, which makes it an efficient tool for monitoring water quality parameters in coastal areas. The spectral bands of Sentinel-2A MSI and Landsat-8 Operational Land Imager (OLI) sensors are compared in Figure 1. It follows from Figure 1 that spectral bands 2,3,4 and 8a of MSI are nearly identical to bands 2,3,4 and of OLI, but MSI is also equipped with additional bands 5 (705nm) and 6 (740nm) that extend its capabilities in estimation of water quality parameters. K. Toming et al. (2016) have demonstrated that Sentinel-2A MSI can be applied for estimation of water colour, Chl-a, coloured dissolved organic matter (CDOM) and dissolved organic carbon (DOC) water quality parameters in the lake waters.

The advantages of Sentinel-2A in detection of algal blooms have been demonstrated by ESA in August 2015 when cyanobacteria algal bloom was detected in central Baltic Sea using satellite image. The image in Figure 2 clearly shows streaks and filament features on the sea surface shaped by eddies and internal waves (Copernicus Sentinel-2, 2015). The 10 meter resolution of the MSI sensor makes the algal bloom features in Figure 2 easy to identify.

Overall, the comparison with the Landsat-8 OLI sensor demonstrates the importance of improving sensor spatial resolution, reducing revisit time and increasing the number of spectral bands. Despite the advanced spatial resolution, the limited number of visible spectral bands and relatively low radiometric accuracy of MSI (compared to MODIS, MERIS and Sentinel-3 Ocean and Land Colour Instrument (OLCI)) remains the main limiting factors in applying this sensor for water quality monitoring.

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Figure 1: Spectral bands of Sentinel-2A MSI sensor in comparison with Landsat 8 and 7. Source: (Landsat Science, 2016)

Figure 2: Sentinel-2A MSI image of cyanobacteria algal bloom in central Baltic Sea in August 2015. Source: (Copernicus Sentinel-2, 2015)

2.1.2 ESA Sentinel-3 mission, Ocean and Land Colour Instrument (OLCI)

The Sentinel-3 OLCI instrument is designed as a replacement of the ENVISAT MERIS sensor and inherited most if its features, including 15 spectral bands, which are nearly identical to ENVISAT MERIS bands, and 300 meters spatial resolution (Sentinel-3 team, 2013).

The Sentinel-3 OLCI not only inherits but also extends the capabilities of its predecessor (see Table 1). The number of sensor spectral bands has been increased from 15 (MERIS bands) to 22. The new

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Table 1 Characteristics of Sentinel-3 OLCI spectral bands. The equivalent MERIS spectral bands are shown in grey (Sentinel-3 team, 2013)

Band λ centre (nm)

Width (nm)

Function

Oa1 400 15 Aerosol correction, improved water constituent retrieval

Oa2 412.5 10 Yellow substance and detrital pigments (turbidity)

Oa3 442.5 10 Chl absorption max., biogeochemistry, vegetation

Oa4 490 10 High Chl, other pigments

Oa5 510 10 Chl, sediment, turbidity, red tide

Oa6 560 10 Chlorophyll reference (Chl minimum)

Oa7 620 10 Sediment loading

Oa8 665 10 Chl (2nd Chl abs. max.), sediment, yellow substance/vegetation

Oa9 673.75 7.5 For improved fluorescence retrieval and to better account for smile together with the bands 665 and 680 nm

Oa10 681.25 7.5 Chl fluorescence peak, red edge

Oa11 708.75 10 Chl fluorescence baseline, red edge transition

Oa12 753.75 7.5 O2 absorption/clouds, vegetation

Oa13 761.25 2.5 O2 absorption band/aerosol corr.

Oa14 764.375 3.75 Atmospheric correction

Oa15 767.5 2.5 O2A used for cloud top pressure, fluorescence over land

Oa16 778.75 15 Atmos. corr./aerosol corr.

Oa17 865 20 Atmos. corr./aerosol corr., clouds, pixel co-registration

Oa18 885 10 Water vapour absorption reference band. Common reference band with SLSTR instrument. Vegetation monitoring

Oa19 900 10 Water vapour absorption/vegetation monitoring (max. reflectance)

Oa20 940 20 Water vapour absorption, atmos./aerosol corr.

Oa21 1 020 40 Atmos./aerosol corr.

bands at 764.4 and 767.5nm were introduced to improve atmospheric and aerosol correction capabilities and band 673 nm - to perform chlorophyll fluorescence measurements.

The field of view of OLCI sensor is tilted away from the sun to minimise the impact of sun glint. This also helps to improve coverage of the global ocean by reducing the revisit time to less than 3.8 days at the equator and less than 2.8 days for latitudes more than 30 degrees for sun-glint free data (ignoring the effect of clouds). With the launch of Sentinel-3B scheduled for 2017 there will be two sensors on orbit, which will reduce the global coverage time to less than 1.9 days (Birruti, 2012). With the L2 products being delivered by ESA in less than 3 hours, Sentinel-3 OLCI can be recommended for future integration into the NRT water quality products and services developed in the AQUA-USERS project. The AQUA-USERS water quality products, originally developed and tested

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for MERIS sensor, will require minimal effort for extension to OLCI that inherited all MERIS spectral bands.

2.1.3 ESA Sentinel-3 mission, Sea and Land Surface Temperature Radiometer (SLSTR)

The SLSTR design incorporates the basic functionality of AATSR, with the addition of some new, more advanced, features. These include wider swath coverage which completely overlaps the OLCI swath, more spectral bands, and a spatial resolution of 0.5 km for visible and SWIR bands (ESA Sentinel Online, 2016).

SLSTR provides data continuity back to 1991 as its data permit the continuation of data-sets from previous instruments: ATSR on ERS-1, ATSR-2 on ERS-2 and AATSR on Envisat.

It is important to note that the SLSTR instruments returns SST measurements for the ocean 'skin' and that the temperature of the sea skin surface is typically a few tenths of a degree cooler than the temperature a few centimetres below. Due to the limited penetration of thermal infra-red radiation through the water column, the infra-red radiometric temperature is that of only the top few tens of micrometres, whereas the oceanographically understood SST is a measure of the temperature in the top 10 cm (ESA Sentinel Online, 2016).

Note also that one SLSTR Level-2 product provides SST following the Group for High Resolution Sea Surface Temperature (GHRSST) data processing specification. As a consequence, SST is given as a single field, derived from the best performing single-coefficient SST field in any given part of the swath (ESA Sentinel Online, 2016).

2.1.4 NASA Pre-Aerosols Clouds and ocean Ecosystems (PACE) mission

PACE is a new strategic climate continuity mission in support of NASA's Plan for a Climate-Centric

Architecture for Earth Observations and Applications from Space (NASA, 2010). Its main objectives are to monitor aerosol particles, clouds, and many factors related to the marine carbon cycle including the phytoplankton pigment chlorophyll. The NASA PACE mission is declared to be “the most comprehensive look at global ocean colour measurements in NASA's history” (PACE, 2016). The Ocean Colour Instrument (OCI) on-board of PACE will be collecting hyperspectral observations for broad spectral coverage from the ultraviolet (UV) to near infrared (NIR) and several bands in short wave infrared (SWIR). Figure 3 shows the spectral coverage of PACE OCI in comparison with the CZCS, SeaWiFS, MODIS and VIIRS sensors. It can be seen in Figure 3 that the main difference from other sensors is in the UV bands that will help to improve estimation of aerosols and discrimination of living and non-living components in the upper ocean. The continuous high resolution spectral measurements in visible wavelengths will assist in estimation of the composition of phytoplankton communities.

One of the threshold ocean science questions being addressed by PACE mission is: “what is the distribution of both harmful and beneficial algal blooms and how is their appearance and demise related to environmental forcing? How are these events changing?” (PACE Mission science definition team report). With continuous measurements in range from 350 to 890 nm with resolution 5 nm, the PACE mission will offers new opportunities for estimation of water absorption spectra, discrimination between different phytoplankton species, detection of harmful algal blooms and determination of their composition (http://phys.org/news/2016-07-nasa-pace-mission-uncover-health.html).

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Figure 3: Spectral bands of PACE sensor in comparison with its predecessors. Source (PACE, 2016)

The spatial resolution of PACE OCI will be kept within the standard for NASA sensors with values of 1x1 km at nadir. To reduce the effect of sun glint the sensor will be tilted by 20 degrees. Additional distinctive features of OCI are the monthly lunar calibration and a single detector rotating telescope scanner design similar to SeaWiFS (PACE, 2016).

Overall, PACE OCI is a significant step forward. By increasing the number of spectral measurements it will open new opportunities for the development of water quality and harmful algal bloom detection methods based on ocean colour. The PACE mission has been scheduled for launch in 2022.

2.2 Geostationary sensors

There is an urgent need for geostationary ocean colour sensors, because current polar orbiting observations are biased and observations of important coastal and ocean processes are now missed (e.g., Cole et al., 2012; Racault et al., 2014). The sun-synchronous acquisition biases the observations (Doxaran et al., 2009; Valente and Da Silva, 2009, Van der Wal et al., 2000), because the sun does not only act as a light source but also as a component in the Sun-Earth-Moon gravitational system which forces the tidal components (Eleveld et al., 2014). Sun-synchronous satellites always sample a certain coupling between tidal constituents. The implications for optical water quality parameters (such as Suspended Particulate Matter (SPM)) are location-specific (Eleveld et al., 2014). In addition, optical

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data are obviously impacted by clouds, and these datasets therefore have a sampling bias for fair weather. These biases – which are inherent to optical remotes sensing from platforms in a sun-synchronous, near-polar orbit (Figure 4) – concern observations from well-known ocean colour sensors such as CZCS, SeaWiFS, MODIS and MERIS and VIIRS. Finally, consecutive observations of one diel cycle – which is known to affect many physical and biogeochemical processes at the coast and in the ocean (e.g. O'Malley et al., 2014) – are inaccessible from these sun-synchronous sensors (IOCCG, 2012; Mouw et al., 2015). Consequently, geostationary ocean colour sensors have a huge potential for a better understanding of how the marine ecosystem works (Figure 5), which is ultimately also important for our work on ecosystem indicators, HABs and the information that we can supply to the aquaculture sector.

2.2.1 Meteo satellites

The meteorological community has been working with geostationary observations of rapidly changing processes for decades (Chesters, 1998) and it has been tempting to test their technology for ocean colour purposes. For her PhD, Neukermans (2012) has exploited SEVIRI (Spinning Enhanced Visible and InfraRed Imagers) with solar bands in the range of 635, 810, and 1640 nm, on Meteosat Second Generation (MSG).

SEVIRI has reduced spectral and spatial resolution and a degraded signal-to-noise ratio compared to standard ocean colour missions. SEVIRI’s bands in the red and near-infra-red (NIR) spectra only allow for retrieval of suspended sediments, turbidity and identification of extremely high plankton biomass. Only bright targets with remote sensing reflectance above 0.001 sr−1 in the red can be distinguished. SEVIRI’s spatial resolution of 3 km at nadir results in coarser spatial resolution over Europe: for example, 6 km resolution in the southern North Sea. Nonetheless, SEVIRI has been shown to provide frequent imaging of every 15 min, and thereby considerably improved temporal cover.

Neukerman et al. (2009) already demonstrated the benefits of a vastly improved sampling frequency, typically one image per hour or more, and hence the possibility to resolve new processes with tidal and diurnal variability. The probability of obtaining data during periods of scattered clouds is also greatly enhanced. However, the advantages go beyond simply obtaining more data. The exploitation of temporal coherency of natural processes over the timescales resolved by geostationary sensors may offer entirely new ways of processing data (Ruddick et al., 2012, 2014). Instead of pure pixel-by-pixel processing, information from adjacent pixels in time may allow better constraint of the ocean

Figure 4: Sun-synchronous polar orbiting platforms with optical ocean colour sensors produce observations which are biased due to tidal aliasing and weather

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Figure 5: Time-space scales diagram illustrating physical and biological processes overlain with sampling domains of various platforms: mooring and tripods (blue), typical low-earth orbit (LEO, orange-brown area) and from a geostationary (GEO, green area) ocean-colour satellites. The red dashed form indicates merged LEO and GEO data over decades. (IOCCG, 2012; Dicky et al., 2006)

colour inversion problem or provide new opportunities for data quality control via temporal outlier detection of retrieved marine or atmospheric parameters. Multiple geostationary sensors at different longitudes give extra information on the bidirectional reflectance of the ocean-atmosphere system. A preliminary example of subjective identification of temporal spikes in early morning/late afternoon GOCI imagery, presumably associated with high air mass atmospheric correction difficulties, can be seen in Figure 6. In this case, the multi-temporal coefficient of variation over the day shows objectively the areas where erroneous data can be found in a single image. Such temporal spikes could be easily identified in automated post processing of ocean colour data (Ruddick et al., 2014).

In addition, Van Hellemont et al. (2014) have been exploring the combination of geostationary and polar orbiting LEO data. To exploit the advantages of both sensors, the spatial resolution of MODIS and the temporal resolution of SEVIRI, a daily function, F(t), representing temporal variability, is first derived from the noise- filtered SEVIRI reflectance. Each SEVIRI observation at a given time (t) is divided by the observation at the time of MODIS overpass (t0):

(1)

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Figure 6: GOCI-derived Total Suspended Solids (TSS) for the Bohai Sea (left) at 01:16 UTC, 12.6.2011, and (right) coefficient of variation (CV) over all cloud-free images of the same day. The areas of high CV over the day are caused mainly by apparently erroneous data in one or two images. In the right hand image pixels with zero/one data value for the day are given in white/ grey respectively (Ruddick et al., 2012).

This function describes the relative changes of each SEVIRI image to the one at t0 and is then used to modulate the MODIS data at t0 to derive the synergy marine reflectance on the high resolution grid:

(2)

Kwiatkowska et al. (2016) anticipate further products and services from EUMETSAT's FCI instruments on Meteosat Third Generation satellites (MTG), including potential chlorophyll-a products. Recently, Peschoud et al. (2016) presented statistical methods for fusion of OLCI (sun-synchronous Sentinel-3) and FCI (geostationary MTG).

2.2.2 Korean Geostationary Ocean Colour Imager (GOCI)

The Geostationary Ocean Color Imager (GOCI) is the world's first ocean colour observation satellite placed in a geostationary orbit. the Korean Communication, Ocean, and Meteorological Satellite (COMS) with GOCI on board was launched in June 2010 for near real-time monitoring of marine environments in northeast Asia with a 500 m spatial resolution. GOCI observations have been available since 2011 and GOCI covers the 2,500 × 2,500 km square around Korean peninsula centred at 36°N and 130°E and is comprised of sixteen (4 × 4) slot images. GOCI has six visible bands with band centres at 412, 443, 490, 555, 660 and 680 nm, and two near-infrared bands with band centres at 745 and 865 nm (Choi et al., 2012; Ryu et al., 2012a and 2012b). It is operated by the Korean Ocean Satellite Centre (KOSC), at the Korean Institute of Ocean Science & Technology (KIOST). GOCI captures the images of the ocean and its colour around the Korean Peninsula 8 times a day and it has a lifetime span of 7.7 years. KIOST has developed a system for the processing of the GOCI data called GOCI Data Processing System (GDPS, Ryu et al., 2012b) with a local atmospheric correction algorithm and a L3 processor (for building the daily composites).

GOCI has greatly contributed to the current knowledge. There are multiple publications on the instrument and its radiometry and vicarious calibration, bidirectional reflectance function (BDRF) and its impact on vegetation indices, stray light and atmospheric correction, clouds and sea fog, and aerosols and optical depth. The main focus has been on TSM and sediment transport, turbidity and Kd, currents and fronts, though there are also a few influential publications on the biological

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Table 2: MSG SEVIRI solar-reflective spectral bands (Kwiatkowska et al., 2016)

Table 3: MTG FCI solar-reflective spectral bands (Kwiatkowska et al., 2016)

parameters, dispersion of patches of massive floating green algae (Son et al., 2015 and 2016), and Geostationary satellite observations of dynamic phytoplankton photophysiology using the 680 nm fluorescence band (O’Malley et al., 2014).

The next generation of the GOCI sensor, GOCI-II, will be launched with the Geo-Kompsat-2B in 2018. GOCI-II will have a better spatial resolution (300m x 300 m) and 13 spectral bands (Table 4). It will also have global coverage (Pacific/Asia/Australian part of the globe) with one daily global observation, plus the regular 8 times/day observation over the Korean Sea.

Table 4: GOCI-I and GOCI-II technical requirements (IOCCG, 2012)

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2.2.3 GEO-CAPE and the Geostationary Ocean Colour Advanced Permanent Imager (GeOCAPI)

IOCCG (2012) made an inventory of Ocean-Colour Observations from a Geostationary Orbit. Most of these dedicated geostationary ocean colour missions are many years away from reality or are even quite uncertain. More details of these missions can be found in IOCCG (2012).

The Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission will join the global constellation of geostationary atmospheric chemistry and coastal ocean colour sensors planned to be in orbit in the 2020 time frame. Multiple observations per day are required to explore the physical, chemical, and dynamical processes that determine tropospheric composition and air quality over spatial scales ranging from urban to continental, and over temporal scales ranging from diurnal to seasonal. Likewise, high frequency satellite observations are critical to studying and quantifying biological, chemical, and physical processes within the coastal ocean and beyond. These observations are to be achieved from a vantage point near 95°-100°W, to potentially view North and South America as well as the adjacent oceans. The Science Working Groups have endorsed the concept of phased implementation using commercial satellites to reduce mission risk and cost. Multiple instruments are being considered.

The “Geostationary Ocean Colour Advanced Permanent Imager” (GeOCAPI) mission aims at observing ocean colour hourly in coastal zones and the open ocean from a geostationary orbit over the entire Earth’s oceanic and coastal areas as seen from a GEO position over Europe, and at a nadir resolution of 250m. This includes virtually the whole Atlantic Ocean, the Mediterranean Sea and European Nordic seas. Beyond the science that GeOCAPI will foster, data necessary to further develop operational monitoring of coastal areas will also be delivered. Such monitoring will be significantly enhanced when high-frequency observations are available (Antoine and the GeOCAPI Science team, 2016).

2.2.4 The exploitation for indicators, HABS, and aquaculture natural processes

IOCCG Report 8 (2009), entitled Remote Sensing in Fisheries and Aquaculture, shows how ocean colour can be used to support a number of important research and applied/operational efforts. In support of these efforts, ocean-colour observations from a geostationary platform will provide significantly improved temporal coverage of near shore coastal, adjacent offshore and inland waters, and the higher frequency observations from a geostationary platform will help mitigate the effects of cloud cover, as well as better resolve the dynamic, episodic, and/or ephemeral processes, phenomena and conditions commonly observed in coastal regions. This will result in a denser and more comprehensive ocean-colour data set, leading to further development, use and operational implementation of more timely and accurate products, e.g., harmful algal bloom forecasts, which in turn will provide better information to users in support of their management and decision-making needs (IOCCG, 2012).

It’s also important to note that tremendous strides have been made in processing images over the past few decades. The first global ocean-colour image took many months of processing to achieve. Now these are produced routinely, with little delay. The tools to produce images, and composite images, on the same scales are becoming available. E.g., in Copernicus Marine Environment Monitoring Service (CMEMS, 2016a), the standardisation of file formats, enables integration with other remotely-sensed observations (e.g. wind vectors and altimetry), and it is becoming relatively easy to incorporate these into models and predict the movement of oceanic features such as fronts, eddies or upwelling that can be used for fish harvesting and management purposes. Remotely-sensed data provide information to calculate objective ecosystem indicators that can be applied in operational mode as an aid to rational management (IOCCG, 2012).

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Several examples of the monitoring of HAB development by geostationary sensors have been reported. For example, GOCI has contributed to the unravelling the physical processes leading to the development of an anomalously large Cochlodinium polykrikoides bloom in the East Sea/Japan Sea, published by Kim et al. (2016). The presence of Cochlodinium polykrikoides (C polykrikoides) was detected by the Geostationary Ocean Color Imager (GOCI) and validated by in situ observations. GOCI observations have been available since 2011, and combination with other multi-satellite and buoy measurements obtained between 2011 and 2013 allowed examining various stages in the physical condition of the developing C polykrikoides bloom. The results indicate that this HAB is related to four processes: the transport of C polykrikoides from the south coast of Korea to the HAB area; a relatively high insolation; continuous coastal upwelling; and a favourable Sea Surface Temperature (SST) for C.polykrikoide growth.

Using data collected by the Geostationary Operational Environmental Satellite (GOES) Imager, Hu and Feng (2014) documented diurnal changes of a Trichodesmium erythraeum bloom first identified by the Moderate Resolution Imaging Spectroradiometer (MODIS) on the West Florida Shelf. Despite the low-signal-to-noise ratio, the 550-750-nm band revealed clear patterns of Trichodesmium mats floating on the ocean surface and their temporal changes between 14:15 and 22:30 GMT on May 22, 2004. Normalization of the delineated bloom against the ocean background provided an effective atmospheric correction that enabled quantification of the changes in bloom size (i.e. area) and bloom intensity over the course of a day. The area coverage increased by about eightfold from midmorning (14:15 GMT) to reach its maximum around 18:30 GMT, whereas the mean intensity of the bloom area increased by about 22% from midmorning to 17:30 GMT. In the afternoon, while the bloom area remained relatively stable on the water surface, bloom intensity sharply decreased. These temporal patterns may be caused by physical aggregation and/or vertical migration of the Trichodesmium cells.

Finally, in Section 4 an example is given of the monitoring of suspended particulate matter for turbid oyster farming ecosystems (Gernez et al., 2014), which is also a use case for the HIGHROC project (HIGHROC, 2015). The use of geostationary observations for characterisation of suspended suspended particulate matter transport in tidal environments was already mentioned for GOCE.

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3 Evolution and future development of in situ instruments

3.1 Introduction

Discrete In situ sampling and, increasingly high frequency automated in situ sampling (either at fixed positions or from moving platforms such as Ferry boats and e.g. drones) are carried out by inland- and coastal water management authorities operationally in selected waters. Earth observation-based assessments of water quality are quasi operational as demonstrated for instance in Deliverables 3.1 and 6.3 of the AQUA-USERS project, and are seen as a valuable additional source of management-relevant spatial and temporal water quality information. Each of these three methods has specific strengths and weaknesses. This section discusses the potential for merging and adapting in situ sampling schemes by water quality management authorities to also start providing parameterisation and validation data for Earth observation implementation.

For further operationalisation of Earth observation for inland and coastal water quality parameters four types of in situ and laboratory water quality measurements may be of use or are required:

1. Routine in situ measurements by water management agencies or for instance aquaculture operators possibly augmented by some low-cost additions relevant to assessing validity of Earth observation derived water quality assessments (Table 5). These routine measurements are useful for parameterizing (semi)empirical methods as well as for validating all type of Earth observation of water quality algorithms.

2. In situ high frequency automated sampling above and/or below the water surface that enable frequent match-ups with satellite data as well as provide diurnal variability assessments of the water studied.

3. Expert bio-optical measurements for forward and inverse physics-based retrieval model parameterisation and validation (Table 5).

4. Low cost low quality high volume “citizen observatory ” type of instruments

Routine discrete in situ measurements of direct relevance for Earth observation of water quality are CHL concentration, TSM concentration (preferably as seston dry weight and then split into organic and inorganic fractions), Kd and Secchi disk transparency. Algal cell identification and counts are of use, but need translation into optical observable variables such as CHL light absorption or Cyano ‐phycocyanin (CPC) or Cyano‐phycoerythrin (CPE) absorption. Nephelometric Turbidity Units (NTU) measurements are also of use but again require translation into backscattering of all particulates (including algae) in the water or to Kd.

Increasingly, water management authorities, and to some extend aquaculture companies, are experimenting with, or deploying operationally, in situ autonomous high frequency sensors such as algal pigment and CDOM fluorimeters (see e.g. Groetsch, Simis, Eleveld, & Peters, 2014). Table 5 provides an overview of in situ measurements done routinely by water management agencies. A relative new measurement technique for water management is to measure some optical water quality parameters by measuring the colour of the water (as Remote sensing reflectance) and to derive the parameters using optical model inversion schemes (algorithms).

In situ high frequency automated sensors are becoming accessible and affordable. These automated sensors often measure variables such as fluorescence by algal pigments or CDOM or in the case of turbidity sensors perpendicular light scattering at one wavelength. In situ automated sensors will play an increasing role in validating Earth observation derived water quality parameters; as well as assessing natural short term (every few seconds to every few hours) variability; they are more cost-effective than discrete in situ sampling. As Earth observation images typically provide a spatially

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Table 5: Routine in situ measurements and recommended autonomous in situ sensors for deployment by water management authorities and aquaculture operators

MEASUREMENT METHOD

SPECTRO

CHL

SPECTRO

CDOM

GRAVI-

METRIC

FLUOROME

TERS

CELL ID &

COUNT Kd/ PAR

SINGLE λ

LASER NTU

MULTI-λ

LASER NTU

SPECTRO-

RADIO

LAB-BASED SAMPLE ANALYSIS

RECOMMENDED AUTONOMOUS IN SITU ABOVE/IN

WATER

SUBMERGED SUPERVISED

CHL

CPC

CPE

Algal cell counts

CDOM

NAP

TSM

Particle size

distribution

Algal cells

only

Kd

Turbidity (NTU)

Secchi Disk

Transparency

Highly Suited Suitable Potential Not Suitable Variable has a partial effect but cannot be used directly

SPECTRO=spectrophotometric; CHL=chlorophyll; CPC=cyano-phycocyanin; CPE=cyano-phycoerythrin; CDOM =coloured dissolved organic matter; TSM=total suspended matter; NAP = non-algal particulates; Kd= vertical attenuation of light coefficient; HPLC=high performance liquid chromatography; SD=Secchi disk transparency; NTU=nephelometric turbidity units.

explicit snapshot at one time only, in situ automated sensors can provide knowledge on short term spatial and temporal variability.

3.2 Near surface remote sensing

A recent development is the use of near surface optical observations using above water spectro-radiometers (see Section 3.3 for an overview). The purpose of such sensors is to provide a means for quick and accurate measurement of the optical water quality parameters, to provide a measurement of reflectance itself and to provide a means to calibrate and validate reflectance measurements from flying platforms such as satellites, aircrafts and drones. Since the near surface measurements are hardly affected by attenuation in the atmosphere they form an ideal starting point for algorithm development. An important branch of algorithms are semi-analytical approaches where a bio-optical simulation model is used to simulate an observed spectrum. Those models need information about the optical properties of water and its suspended constituents (algae, suspended matter, etc.). Table 6 lists a selection of the most important measurements of optical properties to drive optical models and algorithm development.

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Table 6: Most important optical properties to drive optical models and algorithm development and suitable measurement methods

WATER QUALITY VARIABLES

MEASUREMENT METHOD

HPLC SPECTRO-

PHOTO CELL ID & COUNT

LISST-100 BB9

AC-9 AC-S SD

SPECTRO-RADIO

LAB-BASED SAMPLE ANALYSIS IN SITU SUBMERGED

SUPERVISED ABOVE/IN

WATER

Algal pigment related measurements

CHL N/A

CPC N/A

CPE N/A

Cell counts N/A N/A

Dissolved organic matter related measurements

CDOM N/A

N/A N/A N/A

Particulate matter related measurements

Particle size distribution

Algal cells only

NAP N/A N/A

TSM N/A

Light related measurements Kd N/A Calc Calc

Turbidity N/A Calc Calc

SD Calc Calc

Highly Suited Suitable Potential Not Suitable Variable has a partial effect but cannot be used directly

CHL=chlorophyll; CPC=cyano-phycocyanin; CPE=cyano-phycoerythrin; CDOM =coloured dissolved organic matter; TSM=total suspended matter; NAP = Non-algal particulates; Kd= vertical attenuation of light coefficient; HPLC=high performance liquid chromatography; SD=Secchi disk transparency, Calc=calculated.

Other instruments are available from other manufacturers. LISST-100 is a submersible laser scattering instrument that measures concentration and particle size spectra, pressure and temperature. BB9 is a backscatterometer at 9 wavelengths. AC-9 and AC-S are a hyperspectral light absorption and beam attenuation meters. See Error! Reference source not found. for Earth observation processing context.

Many optical model parameters behave more or less as continuous functions over the relevant spectral domain. NAP and CDOM absorption behave as exponential declining functions, as does water scattering. A deviation to this rule is formed by the phytoplankton absorption that can behave quite diverse over the relevant optical domain. Where traditional algorithm approaches to e.g. CHL (Gons, 1999) and CPC (Simis, Peters, & Gons, 2005) assume that the pigment absorption can be characterised by its main absorption peak, recent literature (e.g. Simis & Kauko, 2012) show that pigment absorption functions (in this case for Chl-a, CPC and CPE) show significant absorption features over large parts of the visible domain. To better resolve and model algal species/groups using spectroscopy it will be worthwhile to consider measuring hyperspectral phytoplankton absorption functions more regularly.

By implementing above-water in situ spectroradiometers, especially as continuously measuring instruments, several benefits may arise:

Provide validation or input to atmospheric correction of satellite images.

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Allow to apply the same algorithms as used for Earth observation imagery in combination with in situ sampling programs.

Adapt and develop locally tuned algorithms

Continuous measurements (under daylight conditions) of a water body

Can be mounted on any type of superstructure (bridge, tower etc).

Do not suffer from biofouling (although they do need cleaning from dust and protection from insects, e.g. spider webs).

3.3 Inventory of current Radiometer systems for above water reflectance measurements.

Measuring the light that leaves the water surface is quite complicated because a spectrometer does not only see this component but also any light that is reflected by the water surface into the sensor (sun and sky glint). The theory of the measurement is treated in Mobley et al. (2015). A discussion on potential error sources can be found e.g. in Brando et al. (2016). There are several systems that allow to measure remote sensing reflectance either as hand-held (single shot) instrument or as fixed-position instrument that measures at regular intervals.

3.3.1 WISP-3

The WISP-3 (Hommersom et al., 2012) is a handheld system based on the Ocean Optics Jaz spectrometer series. The spectrometers are of the CCD type. The optical range of the WISP-3 is ∼380 to 800 nm, with a band width (full width half max) of ∼3.9 - 4.9 nm at a pixel step of 0.3 nm. There are three separate channels to measure Lu, Lsky and Ed. Radiance is measured using a Gershun tube, irradiance is measured with an Ocean Optics CC3 cosine collector. The two Gershun tubes point at angles of 42 deg relative to the zenith and the nadir. The spectrometers are fitted with optical fibres (diameter 400 μm) connecting to Ocean Optics Gershun tubes with 3 deg FOV apertures (the tubes can be adapted to alternative FOV’s), respectively the CC3. The fibres are fixed in order to prevent moving. Under standard settings, the WISP-3 takes five measurements for each radiometer in a total of 30 to 90 s (depending on the light intensity). It calculates the average Lsky, Lu and Ed and derives the average reflectance from these. It automatically corrects for non-linarites and dynamic dark readings, which are measured on a number of separate pixels that are not irradiated by external light during the measurements.

3.3.2 Zeiss-based spectrometers: Trios, Satlantics, Dalec

As far as retrievable, all Zeiss based spectrometers use the Zeiss MMS-1 (developed in 1993) with the following general characteristics:

Maximal spectral range 310-1100 nm

Spectral resolution around 10 nm (256 pixels)

Pixel dispersion approx. 3.3

Spectral accuracy 0.3 nm

Sensor is CMOS

Since all the components are glued together in a semi-monolithic design, spectral recalibration should not be necessary at all.

3.3.2.1 TRIOS

The RAMSES hyperspectral radiometers, constructed by TriOS GmbH (Germany), can be used for in situ reflectance acquisition. Two types of RAMSES sensors are available: RAMSES ARC VIS and

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RAMSES ACC-2 VIS, measuring hyperspectral radiance and irradiance, respectively. Both, ARC VIS and ACC-2 VIS cover a wavelength range from 320 nm to 950 nm with one sample every 3.3 nm and a spectral accuracy of 0.3 nm. The radiance measuring ARC VIS sensor has a field of view of 7°. A cosine collector can be fixed in front of the irradiance sensor ACC-2 VIS to collect the light. The TRIOS system is mostly used as fixed position instrument or on moving platforms such as Ferry boats (Simis & Olsson, 2013).

3.3.2.2 Satlantics

Satlantic Hyperspectral HOCR radiometers use a Zeiss spectrograph optimally configured and characterized to measure light between 350 and 800 nm (approximately 136 individual channels). With the HOCR series, a variable integration time is used for all channels in the array and upper and lower thresholds are set so that no channel saturates within that array. Thermal dark current changes that occur within the spectrograph are corrected across the full spectrum with the use of a mechanical dark shutter that closes periodically in the radiometer. A separate frame of data is generated for this dark reading. Frame rates are dependent on the integration time of the device so are considered variable. When light levels are high, the integration time is low and frame rate is high, so that you are collecting many frames per second. As the light level decreases, the integration time must increase and therefore the frame rate becomes longer. Integration times range from 4 ms to 2 seconds.

3.3.2.3 Dalec

The DALEC sensor head contains three compact hyperspectral spectroradiometers (Carl Zeiss Monolithic Miniature Spectrometers), as well as a Global Positioning System (GPS) and pitch and roll sensors, and is designed to be mounted on a boom positioned over the water, typically off the ship’s bow (Brando et al., 2016). A deck unit contains a data logger, batteries, and a charging circuitry. Each spectroradiometer records 200 channels (400–1050 nm) with spectral resolution of 10 nm, spaced at ~3.3 nm intervals. The Zeiss spectroradiometers in the DALEC measure in the 305–1140 nm range, but the system includes a UV filter with the dual purpose of guarding against second order effects in the diffraction grating and providing a wavelength range for measurement of the spectrometer dark current during data collection. Following Mobley's (1999) recommendations, radiance channel viewing angles are fixed to 40° off nadir (Lu) and zenith (Lsky) when the sensor is held level. The Lu and Lsky sensors have a 5° field of view and the Ed pλq sensor has a cosine-like response. A passive gimbal mount with adjustable damping stabilizes the instrument while the ship is in motion to ensure consistent measurement geometry. An embedded compass, GPS and motor control adjust φ during data collection. To avoid viewing the ship, the DALEC automatically seeks the “ideal” measurement geometry within user-defined boom-relative limits (i.e. φ 135° , or at least φ 90° ). Pitch and roll sensors record data for quality control purposes.

3.3.2.4 ASD Fieldspec

The FieldSpec-4 spectroradiometer has full-range detection capacity (350 nm – 2500 nm). The resolution is about 10 nm in the visible domain. Since the instrument is single channel, the measurement of remote sensing reflectance (Rrs) takes 3 steps: the measurement of Lsky; the measurement of Lu; and the measurement of Ed by pointing the sensor to a reflectance panel of known (calibrated) reflectance. This enhances the temporal separation between the measurements. The advantage of the single channel/single spectrometer design is that all measurements are taken with the same instrument with the same calibration (deviations), making the measurement insensitive to small calibration differences between sensors in a 3 sensor design (WISP-3, TRIOS, Satlantic, Dalec). The instrument does not record viewing angle. The opening angle is determined by the properties of the fibre tip, which provides a rather large opening angle of 20°.

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3.4 Recent general developments in the field of radiometry: launch of the ESA FRM4SOC project

There is quite some literature about spectro-radiometer intercomparisons (e.g. Hommersom et al., 2012; Zibordi et al., 2012). The reason why intercomparisons remain necessary is probably that instruments with very different technical specifications and various forms of calibrations are on the market. Especially in the more challenging highly absorbing waters, measurement uncertainty becomes an issue. The objective of the Fiducial Reference Measurements for Satellite Ocean Colour (FRM4SOC) project (FRM4SOC, 2016) is to establish and maintain SI traceability of Fiducial Reference Measurements for satellite ocean colour. It implements some of the CEOS OC-VC INSITU-OCR White paper recommendations. Proposed activities by the FRM4SOC project:

Develop and implement an instrument laboratory and field inter-comparison experiment for FRM radiometers (round robin) with mandatory participation of National Meteorology Institution(s)

Foster and enhance international Ocean Colour validation activities.

Study what is required in terms of infrastructure for vicarious calibration and validation for Europe for the next 20 years. Leading to firm recommendations on the way forward for the next generation of European Ocean Colour vicarious calibration/verification infrastructure.

The output will be written up in the form of an IOGGC Monograph and, subject to IOCCG agreement, could form an Official monograph.

3.5 Some requirements for next generation field radiometer systems

Depending on the mode of application and the specific purpose, researchers and users should be able to choose between fixed-position stations, hand-held radiometers and radiometers on floating and flying platforms (buoys and airplanes/drones). Important factors are:

Instrument stability, sensitivity to environmental factors, calibration drift should be reduced by

smart instrument design

The prescribed viewing geometry should be strictly observed and known

The calibration of the instrument is valid and performed properly, preferably with some form of

certification

For high quality (FRM) satellite validation measurements, the SI traceability and the instrument

characterisation becomes more and more important (sensitivity, stray light, thermal drift, etc.).

For validation and under-flight studies the spectrometer should have equal or a significantly

higher spectral resolution than the satellite/aircraft/drone.

For application in challenging environments (high sediment concentration, studies of bottom

vegetation etc.) a combination of high sensitivity and extended spectral range is required (e.g.

observation capability at NIR with relative high sensitivity)

In order to maximise the chance of match-ups more attention should be given to fixed position

instruments.

Flagging of sub-optimal measurements and correction of unwanted artefacts by sun glint/sky

glint should be investigated and implemented. Such methods should lead to preferred or even

standardised approaches.

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To prevent data loss, connection to the internet and automated upload of the measurements is

recommended. For satellite cal/val it would be beneficial if databases would provide an

interface to specific cal/val databases.

Instrument handling (especially for hand-held systems) should be easy enough to allow

deployment by non-specialists.

Although cheap/low quality citizen observations are seen as a means to increase the number

and spatial spread of observations, the current status of such instruments seems too low.

Instrument stability, sensitivity to environmental factors and calibration drift should be improved by smart instrument design:

The idea that one instrument is able to perform a three channel measurement (without changing the instrument orientation, as implemented in the WISP-3 and more expensive systems such as Dalec and Satlantics) is very beneficial since the complete measurement cycle can be very short and automated by a central control unit. The WISP-3 system features three different radiometers, one for each channel. So do all multichannel Zeiss based spectrometers (Dalec, Satlantics, TRIOS). Theoretically this should not be a problem if all spectrometers are accurately calibrated (spectral and radiometric calibration) and do not drift. In practice such a set up involves the regular calibration of all three channels, which is more costly. Replacing the three internal spectrometers with only one (that is fed the input from the 3 channels by means of some optical switch) has many advantages. The system becomes cheaper in purchase and maintenance costs. Also there is no effect of calibration differences per channel. Thirdly, if there is a small drift in the radiometric calibration, it would not affect the Rrs calculation since it will affect all channels equally. In terms of characterising the instrument for FRM applications, there is only one set of characteristics (sensitivity, stray light, etc.) for the instrument. With 3 different integrated spectrometers with 3 different sets of characteristics, the calculation of the influence of the separate parameters on the Rrs is more difficult. So it is interesting to consider building future spectrometer systems that observe multiple channels using only one internal spectrometer.

The viewing geometry should be strictly observed and known. The relative angles of all channels can be fixed so there is no uncertainty about the relative viewing directions. Of course the handling/positioning of the instrument in the end determines if viewing guidelines were followed strictly. If not, the measurement quickly becomes worthless. The WISP-3 system is quite heavy and somewhat difficult to keep in the required position for the duration of a measurement. It does not contain a built-in GPS and tilt sensor or clinometer. Future inclusion of automated tilt- and position registration will allow to select only those measurements that follow protocol. It would also allow to guide the user to always take a measurement in exactly the right positioning. Registration of position and time will help to calculate some boundary conditions for a good water measurement (e.g. solar angle above 30 degrees) directly. Such improvements will ensure that correct spectral measurements can be done also by non-trained personnel.

The calibration of the instrument is valid and performed properly, preferably with some form of certification.

Currently the situation is that radiometers are calibrated by the manufacturers, which is not necessarily a bad thing, but it has been noted that several instruments of the same manufacturer/calibration show differences if the calibration is performed in free space. It is also costly because the instruments have to be send to the manufacturer regularly. For future evolution of calibration accuracy we should consider vicarious calibrations to a reference instrument. For practical purposes one could consider using a portable lamp that can be attached to the fore-optics of radiometers for vicarious calibration, having the advantage that

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sending around such a lamp is cheaper and less risky than sending around instruments. The FRM4SOC might proceed in setting up some form of certification which would help for quality control and standardization of calibrations.

For high quality (FRM) satellite validation measurements, the SI traceability and the instrument characterisation become more and more important (sensitivity, stray-light, thermal drift, etc.).

Although the manufacturer of the spectrometer provides some data on spectrometer properties, such properties are mostly unknown by the community that uses them for EO validation. Therefore it is a good idea to start characterising the most frequently used devices to come to an understanding of what the limitations are in the measurement. This would also provide a framework for future instrument improvement and development.

For validation and under-flight studies the spectrometer should have equal or a significantly higher spectral resolution than the satellite/aircraft/drone.

For many future studies it will be important to be able to reconstruct the satellite spectrum accurately based on in situ spectral observations. In our case hyperspectral design is a compromise between the spectral resolution of the phenomena we want to observe and the spectral resolution of satellite systems that need to be calibrated/validated. E.g. MERIS spectral band passes are mostly 10 nm wide and have a block shape. This is the result of the internal design where 1 nm wide spectral bands are combined into the broader bands of interest. Ideally, to validate MERIS observations one would need a similarly designed field spectrometer. Most satellite systems use a more or less Gaussian bandpass filters. FWHM values can be between 10 and 50+ nm. The EnMAP sensor will feature a spectral sampling distance of 6.5 nm (420-1000 nm) and 10 nm (900-2450 nm) and is more or less comparable to the overall spectral characteristics of the ASD fieldspec.

In terms of observable parameters, most colouring agents in the water have relatively continuous inherent optical properties. The exception is phytoplankton absorption that is in many cases a composite of several contributing pigment absorptions (see Figure 7 and Figure 8 (Simis & Kauko, 2012)). It is clear that the interesting information, separating one species of algae from another is hidden in small parts of the spectrum where the differences are also not very big. Major peaks and valleys have a spectral width of 20 – 50 nm but small scale differences are at a resolution around or below 20 nm.

In addition to information on the in-water colouring agents, a measured spectrum (either by satellite or above water radiometer) will in most cases contain some form of contamination with sky and sun glint. A solar spectrum at the bottom of the atmosphere contains sub-nm variability. Separating sun glint and water leaving radiance can make use of the differences in spectral local variability (Simis & Olsson, 2013) and will be more effective if the spectral resolution of the sensor is closer to the variability in the solar spectrum. So it seems clear that spectral resolution should become better than 10 nm and probably better than 2 nm in future to compensate for the glint effects.

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Figure 7: (A) Mass-specific in vivo absorption of the phycocyanin and allophycocyanin pigment fraction and (B) Chl-a specific in vivo absorption of other pigments, from PC-rich cyanobacteria and the rhodophyte Porphyridium aerugineum (Simis & Kauko, 2012)

Figure 8: Mass-specific in vivo absorption of phycoerythrin of PE-rich cyanobacteria and the rhodophyte

Porphyridium ruentum, obtained by fitting the in vivo absorption spectra against the average aChl() and aPC() spectra given in Figure 7

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Figure 9: Relative intensity of natural sunlight

For application in challenging environments (high sediment concentration, studies of bottom vegetation etc.) a combination of high sensitivity and extended spectral range is required (e.g. observation capability at NIR with relative high sensitivity). Dogliotti et al. (2015) propose the use of an 859 nm band to measure sediment concentrations up to 1000mg/m3 in very turbid estuaries. For CDOM measurements sometimes observations below 400 nm are proposed.

Although cheap/low quality citizen observations is seen as a means to increase the number and spatial spread of observations, the current status of such instruments seems too low.

3.6 Current innovations in spectrometer design

Two novel prototypes of spectrometer systems for water quality measurements are currently under development at Water Insight BV and BlueLeg Monitor BV in the Netherlands.

The first instrument is the EcoSpot, a handheld spectrometer built around the concept of “only one spectrometer in the instrument”, with a spectrometer with adaptable properties (different grating, different spectral resolution). The instrument is designed for easy handling (also by non-experts), rapid processing in the field (internal computer) and display of the major results on screen (mobile phone built in).

The other instrument is the EcoWatch, which is a fixed position instrument, featuring even a broader range of spectrometer options and currently two viewing directions separated by 45 degrees, so that the instrument has acceptable azimuth angles for most of the day. In order to increase the broad usability of the dataset, the instrument can be combined with a small meteorological measurement unit and a downward looking video/photo-camera to observe the presence of floating layers, etc.

Both instruments automatically store the observations in a web database. A web application for viewing and analysing the data will be developed. Both systems can in future be delivered with a calibration lamp to perform vicarious calibrations in the field.

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Figure 10: The EcoSpot handheld spectrometer.

Figure 11: The EcoWatch fixed position instrument.

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3.7 Marine autonomous vehicles

Marine autonomous vehicles are a rapidly maturing technology and are now routinely deployed both in support of research and as a component of an ocean observing systems. The past decade has seen acceleration in the successful use of autonomous marine observing systems, particularly in shelf sea environments, to address fundamental science challenges such as ocean shelf exchange (Johnston et al, 2013), oxygen depletion, internal wave driven mixing and biogeochemical variability. Mobile platforms, such as gliders, provide much-needed flexibility in ocean observations as they allow for the movement of sensors through the water in three dimensions. It is clear that these technologies offer the potential for cost-effective observations of the marine environment over large areas at much higher frequencies, finer spatial resolution and for more extended periods than fixed point and ship-based approaches, providing complementary data to these and to satellite Earth observations.

3.7.1 Types of marine autonomous vehicles

Autonomous vehicles can be divided into two categories: autonomous underwater vehicles (AUVs) and unmanned surface vehicles (USVs):

Buoyancy-driven gliders – these USVs travel slowly up and down through the water column, exploiting the prevailing currents to minimise power usage. They are usually equipped with sensors measuring conductivity, temperature and depth (CTD), and optionally optical sensors measuring chlorophyll-a and CDOM fluorescence and optical backscatter, and oxygen concentration. These gliders provide vertical profiles of these parameters. Further sensors available include mixing rates (e.g. Ocean Microstructure Glider); and nutrients using micro-fluidic sensors.

Wavegliders – these wave-propelled surface vehicles provide a platform for meteorological sensors and echosounders, e.g. for acoustic monitoring of zooplankton and small pelagic fish.

Autosub - a fully manoeuvrable AUV (designed by the UK National Oceanographic Centre) that follows a programmed mission and automatically avoids obstacles. Different versions can survey the deep ocean and travel for 6,000 km over 6 months.

Figure 12. Examples of marine autonomous vehicles (left to right): a Slocum glider; a C-Enduro waveglider; and Autosub.

3.7.2 Capabilities of marine autonomous vehicles

Traditional in situ sampling is limited in its ability to provide the necessary vertically, temporally or horizontally resolved data sets to improve our understanding, simulation and prediction of key events and phenomena (e.g. spring and autumn blooms, subsurface chlorophyll maximum). Autonomous marine platforms, however (notably gliders), have the potential to drive a step change in the data coverage in shelf sea environments, giving a three-dimensional coverage at high spatial and temporal resolutions, much as ARGO has done for the open-ocean.

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The current generation of ocean gliders carry a range of sensors to measure physical and biogeochemical properties including temperature, salinity, chlorophyll fluorescence, optical backscatter, CDOM fluorescence, oxygen concentration, nitrate, photosynthetically active radiation, turbulence dissipation and velocity fields. They have a proven ability to maintain year round monitoring in busy and highly dynamic shelf seas. Vehicles usually transmit their data in real time via satellite communication while they are at the surface.

Autonomous marine platforms are highly versatile. Gliders for example may be deployed in a virtual mooring mode, providing high resolution (< 1 m) profiles of GES indicators, 3-4 times per hour in a shelf environment. Alternatively, they may be piloted to survey wider areas (10s-100s km), capturing larger scale spatial gradients and features that evolve over monthly to seasonal time scales. Real-time communication provides the opportunity to modify mission objectives after deployment enabling the sampling strategy to be adapted in response to episodic events (e.g. harmful algal blooms (HABs)) or to track slowly evolving phenomenon (e.g. fronts). Autonomous vehicles can be launched from land, small ribs or large ocean going vessels allowing them to be either placed immediately within an area of interest or piloted towards more remote locations (e.g. the shelf edge). Once launched, autonomous platforms continue operating in all weather conditions.

3.7.3 Example applications of marine autonomous vehicles

To take examples from UK science programmes, the FASTNEt (Fluxes Across Sloping Topography of the North East Atlantic) and SSB (Shelf-Sea Biogeochemistry) programmes completed 44 glider missions which totalled 2,126 days in the water. The successful use of gliders closer to land, in frontal regions and regions of fresh water influence has been demonstrated during the MASSMO (Marine Autonomous Systems in Support of Marine Observations) project.

In each of these projects, satellite Earth observation data (ocean colour, temperature, thermal fronts and altimetry) have added value by providing near-real time mission guidance and spatial context to the autonomous observations.

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4 Development of downstream remote sensing services

4.1 Currently running European projects

4.1.1 FP7 HIGHROC

The HIGHROC project (HIGHROC, 2015) aims to exploit HIGH spatial and temporal Resolution for the development of next generation coastal Ocean Colour products and services. One of the interested use cases mentioned on their website is to support oyster-farming, in close collaboration with the GIGASSAT-project (Gigassat, 2016) (project funded by the French Research National Agency (ANR-12-AGRO-0001, Principal Investigator: Fabrice Pernet, IFREMER)).

Figure 13: During high tide on 13 December 2005, SPOT-HRV map of suspended particulate matter concentration (SPM) showing Bourgneuf Bay (a), and a smaller region of interest near the oyster-farming parks of La Coupelasse (b). The location of oyster-farming sites is indicated by black polygons. One oyster park, the basic unit for oyster rearing, corresponds to a single polygon. The grey area shows the emerged part of the intertidal zone. c) and d): same as in b), but the resolution has been degraded to 300 m and 1200 m to respectively simulate MERIS SPM data at full resolution (FR) and reduced resolution (RR). Source: Gernez et al. (2014).

In this project, particular attention is given to SPM variations, because of their influence on the physiological responses of suspension-feeders such as oysters, mussels and other filter-feeders. High

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concentrations of suspended particulate matter negatively affect the ability of oysters to filter seawater, select and ingest particles. Satellite-derived SPM maps could be used in aquaculture management and spatial planning to identify farming sites with the most favourable SPM conditions for oyster growth. The HIGHROC project will contribute to the development of ocean colour remote sensing algorithms to map SPM and phytoplankton concentrations in turbid coastal waters characterized by large intertidal zones. The routine application of these algorithms to high spatial (30 m) and high temporal (15 min) satellite observations will allow improving the management of oyster- farming ecosystems.

4.1.2 H2020 Co-ReSyF and ESA C-TEP

The H2020 Co-ReSyF project coordinated by DEIMOS Engenharia aims to facilitate access to Earth observation data and various processing tools by the coastal and oceanic research community. Co-ReSyF will create a cloud platform, which simplifies integration of EO data use into multi-disciplinary research activities. This platform aims to be user friendly and accessible to inexperienced scientists as well as EO and coastal experts (Co-ReSyF, 2016).

The ESA-funded Coastal Exploitation Platform (C-TEP) is coordinated ACRI-ST. It is a data access service dedicated to improving the efficiently of data-intensive research into dynamic coastal areas. The C-TEP platform aims to provide advanced tools and data management resources to easily process and manage large volumes of coastal data and make it easily accessible to users (C-TEP, 2015).

Both projects focus on using cloud services for making large amounts of EO data easily accessible to (research) communities outside the remote sensing field. Co-ReSyF is primarily a resource for research and education, whereas C-TEP aims at operational services.

4.1.3 H2020 TAPAS

TAPAS (Tools for Assessment and Planning of Aquaculture Sustainability) is a H2020 project coordinated by the University of Stirling. It started in March 2016 and aims to consolidate the environmental sustainability of European aquaculture. This happens by developing tools, approaches and frameworks to support EU Member States in establishing a coherent and efficient regulatory framework, implementing the Strategic Guidelines for the sustainable development of European aquaculture and delivering a technology and decision framework for sustainable growth (TAPAS, 2016).

TAPAS is not a downstream project, not being funded under the H2020 Space Programme, but under the H2020 Innovative, sustainable and Inclusive Bioeconomy Programme. Still Earth observation, and to some extend optical in situ measurements, play a large role in the project, being integral part of two work packages with three partners organisations contributing. This development shows that these technologies are considered as mature and operational enough to provide valuable information for the assessment and planning of aquaculture sustainability.

4.2 Trends in value adding of remote sensing methods

4.2.1 Sea Surface Temperature

The retrieval of sea surface temperature from satellite measurements is operational and well-established and in general, the quality and accuracies of the products are high. SST time series

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extend already back to 1981 and combined efforts towards new integrated high-resolution1 SST products and services have been initiated.

The Global Ocean Data Assimilation Experiment (GODAE) is an international collaboration for ocean forecasting activities which, in 2002, initiated a GODAE High Resolution SST Pilot Project GHRSST-PP to address an emerging need for accurate high resolution SST products (Donlon et al., 2007). In 2009, the Pilot Project was replaced by the Group for High Resolution Sea Surface Temperature (GHRSST) that aims to provide the best quality global high-resolution SST products, in near-real-time, on a daily basis, to support operational forecast systems and the broader scientific community in the most cost effective and efficient manner through international collaboration and scientific innovation (Donlon et al., 2009). The provision of SST products for operational oceanography by the GHRSST has grown to a mature sustainable service. Since 2009 the key developments of GHRSST in support of SST data is a common format with uncertainty estimates and auxiliary data, new, world leading efforts at creating long-term Climate Data Records to support reanalysis and hind-casts, a new SST Climate Data Assessment Framework (CDAF), managing loss of key data streams and seamless integration of new satellite sensors (Le Traon et al., 2015).

MyOcean was another initiative fostering the development of innovative SST products with funding from the European Commission within the Copernicus Program. The MyOcean demonstration phase (2009-2015) enabled to build solid and reliable prototype operations and to develop the necessary management and coordination environment as well as the interfaces. Consequently, after MyOcean the Copernicus Marine environment monitoring service (CMEMS, 2016a) entered its operational phase in 2015. The products delivered by the CMEMS are provided free of charge and open access, including data from the Sentinel satellites, and cover the global ocean and marine ecosystems and the European regional seas. These products encompass a description of the current situation (analysis), the variability at different spatial and temporal scales, the prediction of the situation a few days ahead (Forecast), and the provision of consistent retrospective data records for recent years (re-analysis) (CMEMS, 2016b).

Most of the state-of-the art SST products are so called “blended” products. SST data from many different satellites are blended in order to make use of the best characteristics of each sensor data, for instance resolution or coverage, and finding an optimal and objective way to fill the data-voids under the clouds and near the coasts. As for other geophysical variables, specific services are developed for decision support tools and community portals to meet specific needs of stakeholders, scientists and user communities. Often data are provided free of charge and only when incorporated into dedicated services users are ready to pay for the service, which provides an added value. This is also in line with the vision of the EU Copernicus programme: Public data has significant potential for re-use in new products and services and more data openly available will contribute to discover new and innovative solutions addressing societal challenges (CMEMS Catalogue, 2015).

Long-term availability and continuity of observations are of paramount importance for an operational data service and efforts to improve blended products and to integrate new sensors into existing SST time series are therefore much needed.

4.2.2 HAB detection methods and services

Earth observation data have proven to be very useful in mapping the spatial extent and evolution of algal blooms (Miller et al., 2006; Stumpf et al., 2009; Pettersson and Pozdnyakov, 2013). However, there are number of critical limitations, including: absence of data on cloudy days, detection only of near-surface accumulations of phytoplankton and absence of information on species composition (Ruddick et al., 2008).

1 High-resolution in SST terms is <10km pixel resolution.

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The harmful algal bloom operational forecast system (HAB-OFS), developed by NOAA, approached this problem by combining different sources of information, including ocean colour images from satellites, wind and surface current direction and speed data from coastal and offshore buoys, field observations of bloom location and concentration provided by state agencies. This information was supplied by the NOAA Harmful Algal BloomS Observation System (HABSOS) that provides information about the collection of cell count observations of the algal species Karenia brevis for the Gulf of Mexico. The NOAA HABSOS project focuses on investigating the correlation between bloom observations and environmental data including satellite remote sensing data (Griffith, 2007).

The NOAA HAB-OFS provides operational forecasts on the potential development and associated impact of Karenia brevis harmful algal blooms. The HAB-OSF service issues the report forecasts of the potential levels of respiratory irritations associated with the Karenia brevis blooms for the next 3 to 4 days. Additional bloom analysis is included in the HAB bulletin shown in Figure 14. The bulletin includes daily ocean colour images from MODIS and chlorophyll concentration maps. The risk of Karenia brevis bloom is estimated through joint analysis of chlorophyll concentration maps, ocean colour satellite imagery, field observations, models, public health reports and buoy data (HAB-OFS, 2013).

The Scottish Karenia Watch EO HAB monitoring service developed at PML for the salmon farm companies, is in operation since 2013 (Shutler et al., 2015). The service is built around the analysis of ocean colour data and provides early warning of Karenia mikomotoi HAB events for Scottish finfish industry by issuing weekly bulletin with a report, satellite true colour images, chlorophyll estimation maps and their traffic light version, HAB risk maps. The traffic light maps indicate concentration of chlorophyll above several predefined thresholds. The Karenia HAB risk map indicates the similarity in ocean colour between current data and a set of confirmed HAB events. The methodology for HAB detection has been developed by Kurekin et al. (2014) and employs a fully automatic data-driven approach to identify HAB classification characteristics of water leaving radiances and derived quantities, and classify them into “harmful”, “non-harmful” and “no bloom” categories using Linear Discriminant Analysis (LDA). High efficiency of the method is achieved by using additional discrimination characteristics from the spectral ratios of water leaving radiances, absorption and backscattering. To reduce false alarm rate the data that cannot be reliably classified are automatically labelled as “unknown“.

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Figure 14: NOAA harmful algal bloom bulletin. Source: HAB-OFS, (2013)

Figure 15: The web interface of NOAA HAB observing system. Source: HABSOS, (2016)

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An approach for detection of Karenia mikimotoi HAB in satellite ocean colour data using a more advanced nonlinear classification method than LDA has been proposed in Kurekin et al. (2011). The LDA classifier is based on the assumption that the distribution of features is Gaussian with the same covariance matrix for all classes. When these requirements for the ocean colour data are not met, the results of HAB classification become less accurate than for the nonlinear classification technique that assumes a non-Gaussian feature distribution. A nonlinear classifier based on Support Vector Machine (SVM) (Vapnick, 1998) for automatic detection of Karenia mikimotoi HAB in the UK coastal waters and Phaeocystis globosa blooms in the Dutch coastal waters using MODIS and MERIS satellite images has been proposed in Kurekin et al. (2011). The HAB detection techniques based on SVM classifier demonstrated similar performance to LDA classifier but was more computationally demanding.

The methodology for detection of Karenia brevis harmful algal blooms in the West Florida shelf using

Neural Networks (NN) has been developed by El-habashi et al. (2016). Application of the NN

techniques in El-habashi et al. (2016) has been motivated by the lack of 678 nm fluorescence channel in VIIRS sensor that has been used as a replacement of MODIS-A. Instead, a NN approach has been applied to estimate phytoplankton absorption at 443 nm using Rrs measurements at 486, 551 and 671 nm. The estimated aph443 values were then correlated with chlorophyll-a concentrations and Karenia brevis cell counts in situ measurements. The additional constraints of low backscatter at

Rrs551 nm and a minimum aph443 have been applied in El-habashi et al. (2016) to detect Karenia brevis blooms.

The limited number of spectral band in the existing EO sensors still remains one of the main limitations for the development of new HAB detection methods using ocean colour data. The absorption spectra of photosynthetic algae are connected with the spectra of individual pigments that form the algal cells and the retrieval of entire spectra is important for the detection of these pigments and HAB classification based on pigment composition. The importance of using hyperspectral signatures for HAB discrimination has been demonstrated in Staehr et al. (2003), where the forth-derivative absorption spectra have been applied to discriminate Karenia mikimotoi and Prorocentrum minimum HAB species based on different pigment composition. Two approaches have been developed in Staehr et al. (2003) using the fourth-derivative spectra and a multivariate regression technique. Both of them were able to differentiate between the two HAB species grown under different light and nutrient supply conditions.

Astoreca et al. (2009) applied the second derivative analysis of total absorption and water-leaving reflectance for the discrimination of Phaeocystis globosa and diatom blooms in the Southern North Sea coastal waters. To find the discrimination features the water absorption and reflectance spectra have been measured for the phytoplankton species grown in laboratory conditions. The second derivatives of the absorption spectra were calculated to estimate the wavelengths of local absorption maxima and to reveal the differences between spectral shapes for different phytoplankton groups. It has been shown that the absorption spectra are not strongly affected by phytoplankton growth conditions and that the spectra can be averaged without loss of information. The main differences in spectral shape for Phaeocystis globosa and diatom species have been revealed from spectral analysis and applied for the development of Phaeocystis detection algorithm based on a baseline approach (Astoreca et al., 2009).

It has been demonstrated by Aguirre-Gomez et al. (2001) and Astoreca et al. (2009) that hyperspectral measurements of water reflectance can provide advantages in detection of HAB from space through the estimation of absorption maxima and linking this data to pigment composition. However, it has been found in Astoreca et al. (2009) that the differences in absorption spectra are very small. This imposes requirements on the accuracy of spectral measurements that can be challenging for the existing and future EO sensors as the performance of satellite algorithms can be affected by atmospheric correction errors, calibration errors and sensor internal noise.

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4.2.3 Aquaculture indicators

The variety of satellite products available through the space agencies and services like the Copernicus Marine Environment Monitoring Service is high and freely obtainable. However, information is scattered, from multiple sources, and not easily accessible for the regular aquaculture manager. There is a real need for the aquaculture industry to have access to near-real time data to support for their daily management decisions and for monitoring environmental parameters that may impact/or be affected by its activities. By addressing aquaculture requirements and presenting the satellite data in a manner that is useful and understandable to the aquaculture manager, projects such as the AQUA-USERS are adding value to the standard products available.

Aquaculture management actions (e.g. lowering cages, harvest, feed) change according to type of aquaculture (e.g. mussel, fish, seaweed), but, when considering monitoring purposes, there is a set of parameters that is common to the needs of the majority of users (D4.3-Table II). Most users are interested in having information on weather and sea state conditions, as well as water temperature and chlorophyll (Chl-a) concentration data. By compiling and integrating the convenient satellite, model and in situ data for a specific site, AQUA-USERS provides relevant and easily accessible information that can support aquaculture activities in that region.

For weather and sea state conditions, instant information may be sufficient for real time management, however, temperature and Chl-a data can also indicate gradients and fluctuations in the environmental conditions that are important for effective evaluation and decision making. In these cases, it is crucial to evaluate the natural variability of the parameters, in order to establish threshold that can be used as indicators. Based on satellite data archives, long-term statistics can be calculated to establish natural variability and threshold values that can provide alert conditions to site managers. This information on marine environmental parameters like Chl-a is essential to monitor water quality, not only for aquaculture managing, but also to comply with environmental directives and to evaluate aquaculture impact on the surrounding media. The implementation of European Directives such as the Marine Strategy Framework Directive (MSFD) emphasize this need and the Chl-a 90 percentile (P90) has been recommended as an indicator of eutrophication in many European coastal waters. Different studies have highlighted the effectiveness of using ocean-colour remote sensing data for that purpose (Gohin et al. 2008, Novoa et al., 2012).

A main challenge of using satellite data to develop indicators and monitor aquaculture sites is the location of those sites. Aquacultures are usually close to shore or within enclosed fjords or bays presenting difficulties in accurate data retrieval. The improvement of spectral, spatial and temporal resolution by future sensors, such as the Sentinel-2 and 3 data, may allow developments in this area. Lack of in situ data at aquaculture sites required for satellite data validation and indicator development is also a limitation. Efforts should be done to raise awareness of the need for in situ data collection at aquaculture sites.

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5 Conclusions

Trends have been analysed in the development of sensors and instruments that may affect methods and services developed within AQUA-USERS and allow them to be further improved. Two main groups of EO sensors have been covered: polar orbiting sensors with sun-synchronous orbit and geostationary sensors. The importance of the new Copernicus missions Sentinel-2 and Sentinel-3 as well as a new NASA mission PACE has been reviewed from the service development point of view.

Main geostationary sensors, such as Korean Geostationary Ocean Colour Imager (GOCI), GEO-CAPE, GeOCAPI and meteorological satellites have been considered and the exploitation of geostationary measurements for monitoring water quality, harmful algal blooms (HABS) and aquaculture natural processes has been evaluated. Their main advantage is the high temporal resolution that can help to fill gaps and overcome biases that exist in observations from polar orbiting systems.

Recent developments in the field of radiometer systems for in situ above water reflectance measurements have been analysed and general requirements for next generation field radiometer systems have been emphasized. The innovations in spectrometer design, recently made to meet these requirements, were considered.

The review of marine autonomous vehicles available for the water quality measurements has demonstrated that this is a rapidly growing technology. It is capable of addressing fundamental science challenges by offering flexibility in ocean observations and facilitating the movement of sensors in water in three dimensions. Marine autonomous sensors provide the capability of vertical, temporal and horizontal measurements of water quality.

The currently running European projects pay particular attention to enhancing the spatial and temporal resolution of water quality products and improving the accuracy of measurements. This aims can be achieved through the development of more advanced sensors and by increasing the number of sensors that provide less accurate measurements. The upcoming and new EO sensors not only form the basis for sustainable support of AQUA-USERS services but also opens new ways for further improvement of service quality.

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