*Project Report* **Fiducial Reference Measurements for Satellite Ocean Colour (FRM4SOC)**

**Andrew Clive Banks 1,\*, Riho Vendt 2, Krista Alikas 2, Agnieszka Bialek 3, Joel Kuusk 2, Christophe Lerebourg 4, Kevin Ruddick 5, Gavin Tilstone 6, Viktor Vabson 2, Craig Donlon 7 and Tania Casal 7**

Received: 29 February 2020; Accepted: 16 April 2020; Published: 22 April 2020

**Abstract:** Earth observation data can help us understand and address some of the grand challenges and threats facing us today as a species and as a planet, for example climate change and its impacts and sustainable use of the Earth's resources. However, in order to have confidence in earth observation data, measurements made at the surface of the Earth, with the intention of providing verification or validation of satellite-mounted sensor measurements, should be trustworthy and at least of the same high quality as those taken with the satellite sensors themselves. Metrology tells us that in order to be trustworthy, measurements should include an unbroken chain of SI-traceable calibrations and comparisons and full uncertainty budgets for each of the in situ sensors. Until now, this has not been the case for most satellite validation measurements. Therefore, within this context, the European Space Agency (ESA) funded a series of Fiducial Reference Measurements (FRM) projects targeting the validation of satellite data products of the atmosphere, land, and ocean, and setting the framework, standards, and protocols for future satellite validation efforts. The FRM4SOC project was structured to provide this support for evaluating and improving the state of the art in ocean colour radiometry (OCR) and satellite ocean colour validation through a series of comparisons under the auspices of the Committee on Earth Observation Satellites (CEOS). This followed the recommendations from the International Ocean Colour Coordinating Group's white paper and supports the CEOS ocean colour virtual constellation. The main objective was to establish and maintain SI traceable ground-based FRM for satellite ocean colour and thus make a fundamental contribution to the European system for monitoring the Earth (Copernicus). This paper outlines the FRM4SOC project structure, objectives and methodology and highlights the main results and achievements of the project: (1) An international SI-traceable comparison of irradiance and radiance sources used for OCR calibration that set measurement, calibration and uncertainty estimation protocols and indicated good agreement between the participating calibration laboratories from around the world; (2) An international SI-traceable laboratory and outdoor comparison of radiometers used for satellite ocean colour validation that set OCR calibration and comparison protocols; (3) A major review and update to the protocols for taking irradiance and radiance field measurements for satellite ocean colour validation, with particular focus on aspects of data acquisition and processing that must be considered in the estimation of measurement uncertainty and guidelines for good practice; (4) A technical

comparison of the main radiometers used globally for satellite ocean colour validation bringing radiometer manufacturers together around the same table for the first time to discuss instrument characterisation and its documentation, as needed for measurement uncertainty estimation; (5) Two major international side-by-side field intercomparisons of multiple ocean colour radiometers, one on the Atlantic Meridional Transect (AMT) oceanographic cruise, and the other on the Acqua Alta oceanographic tower in the Gulf of Venice; (6) Impact and promotion of FRM within the ocean colour community, including a scientific road map for the FRM-based future of satellite ocean colour validation and vicarious calibration (based on the findings of the FRM4SOC project, the consensus from two major international FRM4SOC workshops and previous literature, including the IOCCG white paper on in situ ocean colour radiometry).

**Keywords:** satellite ocean colour; fiducial reference measurements (FRM); calibration and validation; SI traceability and uncertainty; Copernicus; European Space Agency (ESA); Committee for Earth Observation Satellites (CEOS)

#### **1. Introduction**

Copernicus [1] is the European system for monitoring the Earth. It includes earth observation satellites, notably the Sentinel series developed by ESA [2], ground-based measurements and data processing to provide users with reliable and up-to-date information delivered through a set of Copernicus Services related to environmental and security issues. The Copernicus Marine Environmental Monitoring Service (CMEMS, [3]) provides critical marine information in near-real time to the various levels of the user community. Copernicus satellite missions are designed to serve CMEMS by providing systematic measurements of the Earth's oceans to monitor and understand large-scale global dynamics as well as providing data for coastal and inland water applications including eutrophication monitoring, sediment transport and environmental impact assessments [4].

The Committee for Earth Observation Satellites (CEOS) defines calibration as "the process of quantitatively defining a system's responses to known, controlled signal inputs". Validation, on the other hand, is "the process of assessing, by independent means, the quality [uncertainty] of the data products derived from those system outputs" [5,6]. Validation is a core component of a satellite mission (and should be planned for accordingly), starting at the moment satellite instrument data begin to flow until the end of the mission. Without adequate validation, the geophysical retrieval methods, algorithms, and geophysical parameters derived from satellite measurements cannot be used with confidence because meaningful uncertainty estimates cannot be provided to users.

The societal benefits of Ocean Colour Radiometry (OCR) are well-articulated [7–10] and include management of the marine ecosystem and the role of the ocean ecosystem in climate change, aquaculture, fisheries, coastal zone water quality, and the mapping and monitoring of harmful algal blooms. Consequently, Copernicus has developed two relevant satellite families (Sentinel-2 and Sentinel-3) that carry two complementary payload instruments that can measure ocean colour to support the CMEMS service. These are the Multi Spectral Instrument or MSI [11]; and the Ocean and Land Colour Instrument or OLCI [12]. Once in orbit, the uncertainty characteristics of (a) the satellite instruments established during pre-launch laboratory calibration and characterisation activities and (b) the end-to-end geophysical measurement retrieval process, can only be assessed via independent calibration and validation activities. Ground reference measurements are therefore essential to the Sentinel-2 MSI and Sentinel-3 OLCI OCR but were not adequately covered in the operational Copernicus system plan.

Within this context, the European Space Agency (ESA) has funded a series of Fiducial Reference Measurements (FRM) projects [13] targeting the validation of satellite data products of the atmosphere, land, and ocean, and setting the framework, standards, and protocols for future satellite validation

efforts. Fiducial reference measurements, as originally defined by [14,15], are a suite of independent ground measurements that provide the maximum return on investment for a satellite mission by delivering to users the required confidence in data products. This is in the form of independent validation results and satellite measurement uncertainty estimation, over the entire end-to-end duration of a satellite mission. The FRM must: have documented traceability to SI units (via an unbroken chain of calibrations and comparisons); be independent from the satellite retrieval process; be accompanied by a complete estimate of uncertainty, including contributions from all FRM instruments and all data acquisition and processing steps; follow well-defined protocols/community-wide management practices and; be openly available for independent scrutiny.

Following the recommendations from the International Ocean Colour Coordinating Group's white paper on in situ ocean colour radiometry [16], and in support of the CEOS ocean colour virtual constellation [9], the main aim of FRM4SOC [17] was therefore to establish and maintain SI traceable ground-based fiducial reference measurements for ocean colour with the relevant protocols and uncertainty budgets for an ongoing international reference measurement system supporting the validation of satellite ocean colour. This paper details how the FRM4SOC project achieved this and showcases the most important results, including the community consensus-driven scientific road map for the future of satellite ocean colour validation based on fiducial reference measurements.

The paper is structured according to the sections listed below, which follow the project's organisation. SI-traceability and uncertainty budgets are essential for FRM and a focus on these was maintained throughout (see Figure 1). Section 1 is this introduction section, with the following sections being numbered in order starting with Section 2:

**Figure 1.** FRM4SOC project overview: a focus on SI traceability and the addition of uncertainty at each step of the FRM process.

#### **2. FRM and the Future of System Vicarious Calibration of Satellite OCR**

Post launch system vicarious calibration (SVC) using highly precise and accurate ground radiometric measurements is an essential step in the process of achieving sufficient satellite ocean colour product quality to meet the needs of Copernicus [1] and the Global Climate Observing System (GCOS, [18]). At present there is only one fully operational dedicated ocean colour SVC facility run by NASA and NOAA off the coast of Hawaii, USA (MOBY, [19,20]); and only one other site in the world (BOUSSOLE, [21]), which, although it has reached the requirements and high standard of data quality expected for SVC purposes, is at pre-operational status due to a lack of long-term investment.

From an operational perspective, it is crucial that SVC is implemented as early as possible in an ocean colour satellite mission's lifetime as it is the key to public product release (ideally SVC infrastructure should be operational before launch to ensure continuity of long-term data records in a multi-mission perspective). Past experience has demonstrated that approximately two high quality matchups per month are produced by a permanent mooring for the purpose of SVC [22]. At this rate, several years can pass before consolidated vicarious gains can be derived from a single infrastructure. In an operational context, it is, therefore, crucial to increase the number of operational SVC systems to reduce this delay. Furthermore, the EC, ESA and EUMETSAT have put a significant amount of investment into the Sentinel series of satellites and the OLCI and MSI sensors to provide ocean colour products. Value for money from this investment, in terms of good quality ocean colour data and products, is potentially at serious risk if the European SVC infrastructure is not upgraded and supported in the long term.

With the above in mind, between the 21st and 23rd of February 2017, the FRM4SOC project organised an international workshop at ESA entitled "Options for future European satellite OCR vicarious adjustment infrastructure for the Sentinel-3 OLCI and Sentinel-2 MSI series" [22]. The primary objective of this workshop was to evaluate the options and approaches for the long-term vicarious calibration of the Sentinel-3 OLCI and Sentinel-2 MSI series of satellite sensors. This evaluation was performed with the support and active participation of the world's experts in ocean colour SVC and ocean colour radiometry fields. Presentations were given covering all major aspects of ocean colour SVC globally; and open debates were held to discuss lessons learned, to analyse strengths and weaknesses of the different approaches, and to review the cost and requirements to implement, operate, and maintain SVC infrastructure, in order to clearly establish Copernicus' needs in the short and long term. Drawing from the current status of ocean colour SVC the workshop concluded with a consensus for the development of Copernicus' capacity. The key recommendations of this consensus can be summarised as follows:

of a second new European infrastructure in a suitable location to ensure the required Copernicus operational system for SVC including operational redundancy.

#### **3. Measurement Requirements and Protocols When Operating FRM OCR for Satellite Validation**

One of the key achievements of the FRM4SOC project has been to review the state of the art of protocols for the measurement of downwelling irradiance [27] and water-leaving radiance [28]. This builds on heritage from the NASA Ocean Optics protocols series [29], recently updated in [30], but: (a) broadens the scope from oceanic waters [31] to all waters where satellite data products are used, including coastal and inland waters [32]; (b) takes account of the many protocol refinements since 2004, including input from the MERIS optical measurement protocols [33]; (c) focuses particularly on the estimation of uncertainties from the data acquisition and processing steps, as required in the FRM context.

#### *3.1. Measurement Requirements*

As regards the measurement requirements for satellite OCR validation in the FRM context, it is necessary to:

27000 and 0508), and photographs of water state (showing water colour, waves and any floating material), sky conditions (full sky, using fish-eye lens) and the radiometers themselves (showing any fouling or possible obstructions).

Many of these requirements are described in the CEOS/IOCCG White Paper [16], to which the reader is referred for more detail. A detailed description of auxiliary optical and biogeochemical parameters can be found in [29], and further considerations on relevant metadata can be found in [34].

#### *3.2. FRM4SOC Review of Data Acquisition and Processing Protocols*

There have been several major developments over the period 2004-2017 since the last revision of the NASA Ocean Optics protocols [29] that helped shape the FRM4SOC protocols, including:

The essential methods described in the FRM4SOC protocols for measuring downwelling irradiance (three generic methods—see Figure 2) and water-leaving radiance (four generic methods—see Figure 3) can be considered to have reached a reasonable degree of maturity in that they have existed for at least 10–15 years in some form. However, it is clear that there are many incremental improvements still occurring and still possible because of improved understanding/modelling of optical processes and new instruments and measuring platforms.

**Figure 2.** Summary of sources of uncertainty for the three generic families of method for measurement of downwelling irradiance. Reproduced with permission from [27].

**Figure 3.** Summary of sources of uncertainty for the four generic families of method for measurement of water-leaving radiance. Reproduced with permission from [28].

The FRM4SOC protocols review papers for downwelling irradiance [27] and for water-leaving radiance [28] discuss in detail the different measurement approaches and the sources of uncertainty that need to be considered and provide guidelines on best practice for making these measurements.

#### *3.3. Recommendations from FRM4SOC Protocols Review*

In addition to the guidelines provided by the protocols themselves, there are some key recommendations from them for teams participating in satellite ocean colour validation activities that need to be considered when attempting to achieve FRM status for their measurements:

Furthermore, it is recommended to ESA and other space agencies to:

Finally, it is recommended to the IOCCG:

• To adopt a terminology that reflects the generic nature of aquatic optical processes: "air-water interface" instead of "sea surface", "water colour/reflectance" instead of "ocean colour", "aquatic/water optics protocols" instead of "ocean optics protocols", etc.

#### **4. Review of the Most Common FRM OC Radiometers Used for Satellite OCR Validation**

As mentioned in the previous section on protocols, the type of instrument used and its calibration are also major components of a validation measurement uncertainty budget. Therefore, the FRM4SOC project has undertaken a review of the most common ocean colour radiometers used for the purpose of taking validation measurements. The main objectives in carrying out this review were to:

The review therefore focused on the radiometers used for in situ measurement and, in particular, on establishing traceable documentation on their characterisation, including factors such as immersion factor, cosine response, linearity, stray light/out of band response, spectral response, temperature sensitivity, dark currents, radiometric noise and polarisation sensitivity. It also contains some information on radiometric calibration and wavelength calibration of the instruments, although calibration aspects were dealt with in more detail in other parts of the FRM4SOC project and this paper (see Sections 5 and 6).

The list of the radiometers reviewed can be seen in Table 1. The full report, which gives further details of the characteristics of each of the instruments listed, is publicly available from the project website [36].

**Table 1.** Summary of key characteristics of the instruments and systems described in the review (*L* represents radiance, and *E*d represents downwelling irradiance).

To our knowledge, this report is the first attempt that has been made to compile information on all commonly used OCR to the level of detail that is required to construct a full uncertainty budget for instrument-specific aspects. This level of detail far surpasses the information that is generally made publicly available, e.g., on manufacturer websites, and should in any case be available for individual instrument units and not just for an instrument family. In many cases, sufficient information is just not available. In some cases, radiometer manufacturers have performed characterisation tests, but the information is not publicly available and/or is considered confidential, which is contrary to FRM requirements. It is not the intention, and in fact would be neither feasible nor ethically acceptable, to recommend a "best" OCR nor, a fortiori, a "best value for money" OCR. It is for the OCR users, as customers, to make such decisions. However, it is hoped that the FRM4SOC survey and report will help understand what information is or is not currently available for preparation of an FRM uncertainty budget, so that these users will be able to make informed purchase decisions and request the relevant information on radiometer characterisation from their suppliers. Similarly, this process should reward the efforts of the most conscientious instrument manufacturers, who perform careful characterisation tests and provide this information to their customers and to the scientific public and space agencies that use data from these instruments for satellite validation purposes.

To ensure the reliability of measurement results, i.e., traceability to the units of SI with the associated uncertainty evaluation, the review recommended to instrument manufacturers:

The review recommended to instrument users:

• To order regularly the radiometric calibration of instruments in well-equipped calibration labs, collect and carefully analyse the results.

The review recommended to ESA and other space agencies or entities, including Copernicus Services, requiring Fiducial Reference Measurements for satellite validation, to fund and encourage:

#### **5. Comparisons of Irradiance and Radiance Reference Sources Used in the Calibration of Ocean Colour Radiometry**

From the International Vocabulary of Metrology (VIM, [37]), metrological traceability is the property of a measurement result whereby the result can be related to a reference through a documented unbroken chain of calibrations, each contributing to the measurement uncertainty. For FRM4SOC optical radiometry this traceability is to SI where the primary standard/reference is provided by the NPL cryogenic radiometer [38]. The traceability chain for OCR for satellite validation can be seen in Figure 4.

What is not evident from this diagram is that the calibrated irradiance and radiance sources are usually the first part of the chain that is distributed outside an NMI such as NPL. Therefore, these sources provide the foundation of testing the performance of any international network of calibration laboratories and satellite validation.

The main objectives of this initial phase of FRM4SOC were therefore to design and document protocols and procedures and implement a laboratory-based (round-robin) comparison experiment to verify the performance of reference irradiance and radiance sources (i.e., lamps, plaques, etc.) used to maintain the calibration of FRM OCR radiometers traceable to SI. The protocols used to implement both the irradiance and radiance source comparisons are publicly available from the project website [39,40].

**Figure 4.** Simplified SI traceability chain at NPL for satellite ocean colour validation.

#### *5.1. Irradiance Reference Source Comparisons*

These international comparisons took place at the National Physical Laboratory (NPL) of the UK between the 3rd and 7th of April 2017. The main aim was to verify the performance of reference irradiance sources that are used in the calibration of ocean colour radiometers. Participants were from the following organisations and countries: NPL, UK (pilot); Tartu Observatory, Estonia; Laboratoire d'Océanographie de Villefranche-sur-Mer (LOV), France; Satlantic, Canada; Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia; Natural Environment Research Council (NERC) Field Spectroscopy Facility, UK; and the National Oceanic and Atmospheric Administration (NOAA), USA. All participants were required to bring or send three (minimum of two) FEL lamps that are used as reference irradiance sources in their calibration laboratories. It was mandatory that each of the participant's lamps had an SI traceable certificate from its last calibration and information about burn time since that calibration (less than 50 h).

At NPL the Spectral Radiance and Irradiance Primary Scales (SRIPS, [39,41,42]) facility is used to transfer the scale from the NPL primary reference standard for spectral emission, a high-temperature blackbody, to lamp and integrating sphere sources. These sources are then used as secondary spectral radiance and irradiance standards further down the chain. For the FRM4SOC irradiance comparison each participant lamp was measured against such an NPL secondary standard lamp obtaining irradiance values under the carefully controlled conditions of the SRIPS and Reference Spectroradiometer System (RefSpec) facilities at NPL.

The results of these SI-traceable comparisons, as represented in Figure 5, show a comparison of irradiance sources used for OCR calibration. To achieve these results a somewhat more complex analysis than a simple difference to the NPL scale was required. The results of the comparison were expressed in terms of the difference between the spectral irradiance values measured by each participant and the mean spectral irradiance values measured by all participants. Since the participants all measured different lamps (i.e., their own lamps), the required differences between them were determined via measurements at NPL of all lamps. The mean ratio between the participants' measurements and those made at NPL was calculated and results for each lamp were then expressed relative to this mean ratio, so showing the degree to which the individual measurements agree with one another. This was necessary for a couple of important reasons: (1) the participants had various different SI-traceability routes for their lamps, i.e., several different NMIs providing their calibration; and (2) a few of the lamps were recently calibrated at NPL, which with a simple difference to the NPL scale would have shown them performing almost perfectly and thus giving a misleading and biased comparison.

**Figure 5.** Comparison between irradiance sources from several OCR calibration laboratories, including the NPL spectral irradiance scale as a reference. Reproduced with permission from [43].

All participants' lamps are traceable to SI, and so the results show how the lamps compare with this realisation of the SI irradiance scale, i.e., the mean of all. The comparison shows that they agree among each other to within ±1 to 1.5%. Also included is the difference between the mean of all and the NPL spectral irradiance scale, which shows an agreement for the entire set of lamps to within 1% across all wavelengths. Uncertainties were calculated for each lamp's ratio (not shown) and these generally ranged from 1.6%–1.7% in the UV wavelengths down to 0.9% in the NIR.

These comparisons should not be misinterpreted to mean that there is anything wrong with any of the lamps at particular wavelength ranges. The trend of any single lamp will be due to a combination of factors that may include, for example, the trends of all the other lamps and whether any of them are suffering from the effects of ageing since their last calibration. The likely reason for lamps following the same trend is probably related to whether calibration for more than one has been transferred from the same primary lamp. Again, there is nothing wrong with doing this and this does not indicate anything wrong with the calibration reference lamp. These results are in fact comparable to differences seen between different NMI realisations of the irradiance scale [41,42].

#### *5.2. International Transfer Radiometer Round Robin for Radiance Reference Source Comparison*

The radiance round robin comparison took place between June 2017 and May 2018. Its main aim was to verify the performance of radiance sources used to calibrate ocean colour radiometers. The comparison was conducted by NPL as pilot through the round-robin circulation of two ocean colour transfer radiometers. The transfer radiometers used were 7-band multispectral Satlantic ocean colour radiometers (OCR-200) on loan to the pilot from the Joint Research Centre (JRC) of the European Commission. Satlantic had customised these two particular instruments for JRC in terms of their angular characteristics to provide a narrower (~3◦) field of view than standard. Initial characterisation measurements to confirm this FOV were carried out by NPL in air, and found to be 2.5◦ ± 0.3◦ at FWHM, with a close to Gaussian profile.

The most commonly used radiance source for ocean colour radiometer calibration was used for these comparisons, i.e., an FEL lamp and reflectance panel combination. The FEL lamps were by design the same ones included in the irradiance comparison (see previous section).

The two ocean colour transfer radiometers were sent to each participant according to the schedule shown in Figure 6 in order for them to take at least two sets of radiance measurements of their in-house radiance source (lamp-panel combination) according to NPL protocols that accompanied the transfer radiometers [40].

**Figure 6.** Schedule of the FRM4SOC international radiance round robin.

The transfer radiometers were checked by NPL before and after each round of measurements by the participants. The round robin measurements were directly traceable to the NPL primary reference standards, using well-characterised facilities, and were supported by full uncertainty budgets. This direct link to SI not only provided a stringent test of the reliability of the various traceability routes used by the participants, but also allowed the uncertainties associated with the comparison to be evaluated. As in the irradiance comparison, use of the calibration certificates of each participant's lamp and panel was also essential because they are a critical part of the SI traceability and uncertainty evaluation of each participant's radiance measurements.

Each participant was requested to evaluate uncertainties associated with their radiance source operating in their own laboratory for these measurements. This included all the additional uncertainty components related to the alignment of the lamp, panel and radiometer, distance measurements, and

other relevant laboratory specific factors such as power supply stability and accuracy. The pilot had discussed all these aspects with participants and trained them in order to facilitate the correct compiling and reporting back of this uncertainty budget evaluation using pre-agreed templates.

The comparison measurand was the calibration factor determined for the transfer radiometer using each participant's own spectral radiance reference (i.e., a lamp-panel combination in a 0◦: 45◦ arrangement with the lamp set at a known distance from the panel). For each participant a separate calibration factor was determined for each of the seven specified wavebands of the transfer radiometers and each waveband was treated independently for the purposes of this analysis. It was recognised that participants may be using different types of reflectance panels to the 46 cm (18 inch) Spectralon panel used by NPL. Thus, it was essential that participants supplied the pilot with the technical details and history of the artefacts along with SI-traceable calibration certificates, the uncertainty evaluation according to the pre-defined and agreed format, see [40], and as much additional information on their laboratory conditions as possible, in order to aid the pilot in carrying out this comparison.

An example of the results from one of the two transfer radiometers of the comparison are presented in Figure 7 in terms of differences between each participant's measurements and the mean value of all of them. The other radiometer showed similar results and in general, they all agree within ±4%, a slightly higher difference than expected. The majority of the results forms a group located at around the 0% line and below on the y-axis values. A second group of 4 entries is located at the level of +3% difference from the mean comparison value. The majority of participants exhibited a range of differences across the channels of within 1% to 2.5%, with the notable exception of one of the participants blue (412 nm) channel measurements.

Additional investigation showed that the reason for these differences and groupings may be caused by a combination of the size of source effect and instrument effective FOV that affected the results of the smaller group. If these effects could be corrected for, or the measurements repeated with different settings, an agreement within 2.5% might be expected. Furthermore, each participant's uncertainty budget for the radiance measurements gave values of between 1.8% to 2.0% for low uncertainty participants, 2.1% to 2.4% for medium uncertainty participants and 2.5% to 3.1% for high uncertainty participants. Full details can be found in [43].

**Figure 7.** Comparison between radiance sources of participant OCR calibration laboratories (A–M) showing mean coefficient percentage difference across all distances. Reproduced with permission from [43].

#### **6. Comparisons to Verify the Performance of Ocean Colour Radiometers Used for Satellite Validation**

The main aim of these comparisons was to link the ocean colour field measurements to the radiometers' SI-traceable calibrations and verify whether different instruments measuring the same light source in the lab, or the same patch of water or sky outdoors, will provide consistent results within the expected uncertainty limits. As an outcome, methodologies used by participants for the measurements and data handling were also critically reviewed.

The laboratory and outdoor comparisons took place at Tartu Observatory (TO) in Estonia and at a lake nearby (Lake Kääriku) between the 8th and 13th of May 2017 with the calibration of all participants' radiometers taking place just prior to this between the 2nd and 7th of May 2017. This was an international event with participants and their radiometers taking part from several different organisations and countries: TO (Tõravere, Estonia) as pilot; Alfred-Wegener-Institut (AWI, Bremerhaven, Germany); Royal Belgian Institute of Natural Sciences (RBINS, Brussels, Belgium); National Research Council of Italy (CNR, Rome, Italy); University of Algarve (CIMA, Faro, Portugal); University of Victoria (UVIC, Victoria, BC, Canada); Sea Bird Scientific (Halifax, NS, Canada); Plymouth Marine Laboratory (PML, Plymouth, UK); Helmholtz-Zentrum Geesthacht (HZG, Geesthacht, Germany); University of Tartu (UT, Tartu, Estonia); Cimel Electronique S.A.S. (Paris, France).

The comparison exercise therefore consisted of three sub-tasks: an SI-traceable radiometric calibration of participating radiometers just before the intercomparison; a laboratory intercomparison of the measurement of stable lamp sources in a controlled environment; and an outdoor intercomparison of the measurement of natural radiation sources at a lake. Altogether, 44 radiometric sensors from 11 institutions were involved: 16 TriOS RAMSES, 2 Satlantic OCR-3000, 4 Satlantic HyperOCR, 4 WaterInsight WISP-3, 1 Cimel SeaPRISM and 1 Spectral Evolution SR-3500 radiance sensors, and 10 TriOS RAMSES, 1 Satlantic OCR-3000, 2 Satlantic HyperOCR, 2 WaterInsight WISP-3, and 1 Spectral Evolution SR-3500 irradiance sensors.

#### *6.1. Laboratory SI-Traceable Radiometric Calibrations*

Before the comparisons could take place the first task was the SI-traceable absolute radiometric calibration of the 44 participating radiometers. The calibrations were performed in the optical radiometry laboratory of Tartu Observatory (TO), Estonia. Calibration measurements were performed at the room temperature of 21.5 ◦C ± 1.5 ◦C in an EN ISO 14644 Class 8 equivalent cleanroom environment.

NPL provided two Gigahertz-Optik BN9101-2 FEL-type irradiance calibration standard lamps for the calibrations and comparison exercise. The lamps were calibrated by NPL and had not been used since the last calibration. Differences in responsivity, in the range of 340 nm to 980 nm, were less than ±0.5%. The drift of the irradiance values (at 500 nm) measured during the calibration campaign was ~0.1%, which is close to the detection limit of the filter radiometer. In certificates issued for the radiometers from these calibrations, the arithmetic mean of the responsivity measured by the two lamps was used. Radiance calibration was performed using the same lamps and a Sphere Optics calibrated white reflectance panel. Normal incidence for the illumination and 45◦ from normal for viewing were used. The panel had been previously calibrated in the same illumination and viewing conditions by NPL.

Additionally, a large number of the sensors involved in the comparisons were recalibrated at TO a year later for the FRM4SOC field intercomparison on the Acqua Alta Oceanographic Tower (AAOT) in the Gulf of Venice (see below) allowing the evaluation of the stability of the sensors. Most of these sensors (>80%) changed less than ±1% during this year.

#### *6.2. Laboratory Intercomparison of Measurements*

The main set of laboratory comparisons took the form of carefully controlled measurements of irradiance and radiance using stable lamp sources. These were a seasoned but uncalibrated FEL lamp for irradiance and a Bentham ULS-300 integrating sphere with internal illumination as a stable radiance source. Minimum sets of 30 measurements were taken by each radiometer with overall results seen in the graphs below. Consensus values were calculated as the median [37] of all presented comparison values. Reference values were applicable only for the indoor irradiance measurements, when the measurand used for this exercise was, during the comparison, also measured with a precision filter radiometer serving as a reference.

Despite different sensor types, as the radiation sources used for indoor comparison were spectrally very similar to calibration sources, agreement between sensors was reasonably close for radiance and for irradiance sensors (see Figures 8 and 9). No outliers were present, after correction of data by participants and unified data handling (especially harmonisation of spectral interpolation) by the pilot.

Relative uncertainty budget tables for irradiance and radiance, based on the spread of individual sensors measuring the same source during the indoor comparisons were also produced. Effects of different characteristics of the radiometers, such as temperature dependency, stray light, non-linearity, cosine responsivity and field of view, on the calibration and measurement uncertainty are discussed in detail in [44,45]. In summary though, from the indoor experiment, when conditions were similar to calibration conditions, a high effectiveness of the SI-traceable radiometric calibration has been demonstrated, and a large group of different types of radiometers operated by different scientists achieved a reasonably close consistency giving low standard deviations between radiance (27 in total) or irradiance (15 in total) results (*s* < 1%). This was, however, only achieved after some unification of measurement and data processing, e.g., alignment of sensors, structuring of collected data, and application of unified wavelength bands, a spectral interpolation method and non-linearity corrections. Nevertheless, variability between sensors may be insufficient for complete quantification of uncertainties in the measurements. For example, standard deviation of nonlinearity estimates versus the mean effect demonstrated that differences are not able to reveal the full size of systematic errors common for all the instruments. Therefore, all radiometers should be individually tested for all significant systematic effects that may affect the results, as this is the only way to get a full estimate of the effects degrading traceability to the SI scale.

**Figure 8.** Comparison of low intensity (**left**) and high intensity radiance (**right**) sources as measured by each participating instrument compared to the median over all instruments; after reviewing data by pilot, corrections submitted by participants and/or unified data handling by pilot. Blue dotted lines—expanded uncertainty of the median consensus value. Reproduced with permission from [45].

**Figure 9.** (**left**) Comparison of each participating irradiance sensor measuring an irradiance source compared to the median over all instruments; after reviewing data by pilot, corrections submitted by participants and/or unified data handling by pilot with blue dotted lines the expanded uncertainty of the median consensus value; (**right**) compared to the reference values of the filter radiometer with blue dashed lines the expanded uncertainty covering 95% of all data points and uncertainty of radiometric calibration included. Reproduced with permission from [45].

#### *6.3. Outdoor Intercomparison of Measurements*

The outdoor comparisons were conducted as a direct intercomparison of the downwelling irradiance *E*d, the downwelling sky radiance *L*d, and the total upwelling water radiance *L*u from a diving platform on the end of the 50 m pier at the southern shore of Lake Kääriku in Estonia, as shown in Figure 10. The physical and optical characteristics of the part of the lake measured were characteristically eutrophic, well known to the pilot, and are detailed in [44,46].

The outdoor measurements were performed in 5-min casts. Between the pilot announced beginning and end times of casts, all participants recorded the radiance and irradiance data at their usual fieldwork data acquisition rate. Thirty casts were recorded in total, but only seven of them were included in the intercomparisons. The selection of casts was based on the time series of the 550 nm spectral band. The pilot received the 550 nm time series data for 16 radiance and 10 irradiance sensors. Only the casts with the most stable signal and least missing data were selected for further analysis. All the selected casts were measured on May 12 2017—the second day of the outdoor experiment—due to adverse environmental conditions on the first programmed day, forcing the comparisons to be limited to *E*d, *L*d, and *L*u, rather than also including the remote sensing reflectance *R*rs and the water-leaving radiance *L*w derived from simultaneously measured *E*d, *L*d, and *L*u. Consensus values of irradiance and radiance were assigned as the median of the valid casts (C) for each of the conditions measured (Figure 11).

The measurement results for the field casts are presented in Figures 12 and 13 as the deviation from the above consensus values as this was considered the most appropriate way to show differences between the radiometers.

Relative uncertainty budget tables for the downwelling irradiance and water leaving radiance were also produced, based on the spread of individual sensors measuring the same target during the outdoor comparison [44,46]. Investigations to try to explain the marked differences between radiometers and types of radiometer shown in Figures 12 and 13 and these uncertainty tables were also undertaken within the framework of these comparisons and the FRM4SOC project.

**Figure 10.** Pier and diving platform at the southern coast of Lake Kääriku with all the radiance (**right**) and irradiance radiometers (**left**) mounted in common frames for the outdoor experiment.

**Figure 11.** Irradiance and radiance consensus values in the outdoor experiment. C8, C10, C12, C13, C14—blue sky (radiance) or direct sunshine (irradiance); C17—water in cloud shadow; C23—sunlit water. Reproduced with permission from [46].

For irradiance, the difference in cosine response was the main source of differences between different sensor groups revealed during the field experiment. Variability between irradiance sensors was about five times larger than that observed during the indoor laboratory exercise. This large variability between sensors during the outdoor exercise cannot be explained simply by the poor stability of sensors, as a stability check in lab conditions a year later has shown smaller changes than during the outdoor measurements some days after calibration. Variability cannot be fully explained by factors such as temperature, nonlinearity, and stray light either, as one could expect a smaller difference between radiance and irradiance sensors in this case. Most likely, the different behaviours of RAMSES and HyperOCR sensors are largely due to a different construction of input optics of these sensors and imperfect cosine response [47]. This hypothesis was supported by the angular response characterisation of 5 RAMSES irradiance sensors and comparing the integral cosine error values to the deviations from the consensus value in the outdoor experiment [46].

For radiance, the angular response (different fields of view) and spatial non-uniformity of the targets provides the main difference between different sensor groups. In the case of a spatially heterogeneous target (sky with scattered clouds, water at an oblique viewing angle) the large differences of FOV of different sensors will likely cause significant discrepancies between sensors. The variability between radiance sensors was about two times larger than during the indoor exercise. This can be partly explained by the larger effects of factors like temperature, stray light and nonlinearity that were not corrected for during the field experiment. For example, dependence of the calibration

coefficients on temperature can cause significant deviation from the SI-traceable result. For a maximum temperature difference of about 20 ◦C between calibration and later measurements (typically between 0 ◦C and 40 ◦C) a responsivity change of more than 10% may be possible [48,49]. This feeds back to the calibration procedure, which may be improved if its conditions are designed to cover situations possible during the use of an instrument in the field. For example, if it is known that the radiometer has a linear response with temperature [49], the responsivity of the radiometer can be evaluated when calibration is performed at three different temperatures covering the possible range of temperature variations during its later use in the field.

**Figure 12.** Irradiance sensors compared to the consensus value. Solid lines—RAMSES sensors; dashed lines—HyperOCR sensors; double line—SR-3500. Reproduced with permission from [46].

The different behaviours of RAMSES and HyperOCR sensor groups were also clearly revealed during the comparisons. For the RAMSES group, the variability of radiance sensors during indoor and outdoor exercises was very similar and the HyperOCR and WISP-3 sensors mainly caused the larger variability for the outdoor measurements. For irradiance measurements, the deviation of HyperOCR sensors from the consensus value of the group was very small, and the group of RAMSES sensors was the main cause of an increase in variability.

The spread of irradiance and radiance results from the comparison, with differences between the sensors due to their calibration state before the experiment, is summarised in Figure 14. All standard deviations of laboratory measurements were smaller than 1%. Standard deviations of the field results are substantially higher (1%–5%), but still much smaller than the variability due to the calibration state of the sensors before the experiment (5%–10%), i.e., the calibration that each participant would have used if the radiometers were not freshly calibrated just before the start of the intercomparison exercise. It must be noted, however, that some instruments had not been used for fieldwork in recent years and their calibration coefficients were several years old.

**Figure 13.** Radiance sensors compared to the consensus value in the outdoor experiment. C8, C12, C13—blue sky; C17—water in cloud shadow at 139◦ VZA; C23—sunlit water at 130◦ VZA. Solid lines—RAMSES sensors; dashed lines—HyperOCR sensors; double lines—SeaPRISM (SP) and SR-3500; dotted lines—WISP-3. Reproduced with permission from [46].

In the frame of the outdoor experiment when conditions for calibration and in the field are very different from each other, the variability between freshly calibrated individual sensors did increase substantially. This demonstrated a limitation of typical OC field measurements, even for sensors having recent SI-traceable radiometric calibration. Including laboratory intercomparison in the comparison of OCR sensors has clearly shown that a further reduction of the uncertainty of radiometric calibration of sensors will not improve the agreement between field results significantly. More relevant for achieving better SI-traceability and lower uncertainties in field measurements are improved specifications of radiometers, additional characterisation of individual sensors accounting for specific field conditions, and unified data handling.

**Figure 14.** Variability between irradiance and radiance sensors. *E*\_cal and *L*\_cal—due to calibration state; *E*(Lab), *L*(Low) and *L*(High)—variability in laboratory intercomparison; *E*(Sun), *L*(BlueSky) and *L*(Water) variability in the field. Reproduced with permission from [46].

The indoor experiment demonstrated the effectiveness of performing the radiometric calibration at the same laboratory just before intercomparison measurements [45,46] in obtaining consistent results. However, besides regular calibration, a sufficient individual characterisation of radiometers by testing them for all significant systematic effects is suggested from these comparisons as the best way to enable reduction of biases in outdoor intercomparisons. This should lead to a smaller variability between measurements from different instruments in the field, and a more realistic and complete quantification of uncertainties in measurement. To help in the interpretation of the results and in future outdoor intercomparison campaigns, the following further suggestions were proposed:

#### **7. Field Intercomparison Experiments to Verify the Performance of Ocean Colour Radiometers Used for Satellite Validation**

The overall objective of these field intercomparison experiments was to design and document protocols and procedures and implement field comparisons of FRM OCR radiometers, as well as build a database of OCR field radiometer performance knowledge over several years.

#### *7.1. The Atlantic Meridional Transect (AMT) Cruise Field Intercomparison Experiment*

Plymouth Marine Laboratory (PML), in collaboration with the National Oceanography Centre (NOC) Southampton, has operated the AMT since 1995 [50]. The cruise is conducted between the UK and the sparsely sampled South Atlantic during the annual passage from October to November of a NERC ship (RRS James Clark Ross, RRS James Cook or RRS Discovery). The transect covers several ocean provinces where key physical and biogeochemical variables such as chlorophyll, primary production, nutrients, temperature, salinity and oxygen are measured. The stations sampled are principally in the North and South Atlantic Gyres, but also the productive waters of the Celtic Sea, Patagonian Shelf and Equatorial upwelling zone are visited, which therefore offered a wide range of variability in which to conduct field intercomparisons for FRM4SOC.

The results from the AMT cruises have enabled the intercomparison of simultaneous measurements of water leaving radiance and reflectance. The differences observed between these measurements form a key component of estimating errors and uncertainties resulting from environmental variability, as well as instrument deployment methodology, instrument specifications and calibration.

The main AMT comparison for FRM4SOC was conducted from 23rd September to 4th November 2017 from Southampton, UK to South Georgia and the Falkland Islands on AMT-27, to compare along track measurements of *L*w and *R*rs(λ) between PML and Tartu Observatory (TO) radiometers. Measurements were carried out in various solar zenith angle, water and weather conditions. The ambient temperature varied from 1 ◦C to 28 ◦C. Altogether, data was collected from ~30 stations.

The AMT-27 cruise data consists of synchronised measurements of water leaving reflectance with two sets of hyperspectral radiometers both consisting of three radiometers in order to measure the upwelling radiance *L*u(λ), downwelling radiance from the sky *L*d(λ), and downwelling solar irradiance *E*d(λ). The PML set consisted of three Satlantic HyperSAS sensors and the TO set of three TriOS RAMSES sensors. All radiance and irradiance sensors were SI-traceably calibrated at the Tartu Observatory before and after the campaign. All of these sensors were involved a year before in the laboratory calibration intercomparison campaign (Section 6.1) and demonstrated differences less than ±1% both for radiance and irradiance results during indoor measurements (Section 6.2). However, during the outdoor exercise, the PML irradiance sensors showed up to 6% higher values in the blue part of the spectrum, and the PML radiance sensors showed up to approximately 10% higher values in the red and IR parts of the spectrum when compared to the respective TO sensors.

The radiance sensors *L*d(λ) and *L*u(λ) were mounted side by side on a common steel frame positioned at the front of the ship using 40◦ zenith and nadir viewing angles, respectively. The downwelling irradiance sensors were mounted on another steel frame positioned on the mast at the front of the ship, to avoid any ship shadows. Positioning of sensors ensured nearly identical measurement conditions for both 3-sensor radiometric systems (see Figure 15).

**Figure 15.** The route of AMT-27 through the Atlantic and the position of the FRM4SOC radiometers on RRS Discovery in operation during AMT-27.

The intercomparison allowed the analysis of the variability of responsivity between different types of freshly calibrated sensors with respect to the environmental and illumination conditions. As an example, the difference in the results of downwelling irradiance between PML and TO, as a function of ambient temperature and solar zenith angle, are shown in Figure 16.

**Figure 16.** Difference in downwelling irradiance between PML and TO sensors as a function of ambient temperature (**left**) and solar zenith angle (SZA, **right**).

With regard to ambient temperature, radiometric calibration of the sensors was performed in lab conditions at 21 ◦C and no temperature correction factors were applied for the field results. Responsivity change for both sensors was larger (and unknown) compared to the change of the signal ratio shown. The differences varied from approximately –5 to +5% in the temperature range of 1 to 30 ◦C. However, the sensors recorded similar irradiance values around 21 ◦C which corresponds to the calibration temperature. This result clearly shows the need for characterisation of field radiometers for thermal effects.

For solar zenith angle, the variation is in agreement with known or expected errors of the cosine collectors of compared sensors, evaluated to be within ±2% [45,46]. The stray light correction effect is negligible and shown in Figure 16 for reference only.

The comparison of HyperSAS and RAMSES measured water-leaving reflectance after applying stray light correction showed a very high agreement over all wavelengths. The systematic biases were negligible (see Figure 17).

**Figure 17.** Correlation between HyperSAS and RAMSES measured water-leaving reflectance after stray light correction on selected wavelengths. Colour is wind speed (m s−1) during the measurement.

The comparison between the OLCI-derived and in situ water-leaving reflectance, either by RAMSES (Figure 18A) or HYPERSAS (Figure 18B), showed a very good correlation in the blue to green wavelengths. For these wavelengths, the correlation with OLCI-derived water-leaving reflectance was even better after applying the NIR similarity correction [51,52] (Figure 19).

**Figure 18.** Correlation between OLCI-to-RAMSES (**A**) and OLCI-to-HyperSAS (**B**) water-leaving reflectances at selected wavelengths.

The above summary analysis shows that by comparing results to ancillary instrument data during the cruise (with regards to environmental conditions), the sources of any differences can begin to be established. From these results, recommendations can be made to adjust processing methodology (e.g., applying appropriate filtering thresholds), future instrument deployment methodology, and calibration processes. Furthermore, these comparisons contribute to the Type B estimates in an uncertainty budget [53]. A complete comparison analysis, including uncertainties, is being published using data collected during AMT-27 but nevertheless these initial results are promising, especially given the large differences in environmental conditions experienced during the AMT cruise.

**Figure 19.** Comparison of RAMSES (**A**) and HyperSAS (**B**) radiometer-derived water-leaving reflectance after stray light correction (blue) and after stray light+NIR similarity correction (brown) compared to OLCI's derived water-leaving reflectance at two stations.

#### *7.2. The Acqua Alta Oceanographic Tower (AAOT) Field Intercomparison Experiment*

The main aim of the AAOT intercomparison was to assess differences in radiometric quantities determined using a range of above-water and in-water radiometric systems (including both different instruments and processing protocols). Specifically, we evaluated the differences among:

The field intercomparison was conducted at the Acqua Alta Oceanographic Tower (AAOT) which is located in the Gulf of Venice, Italy, in the northern Adriatic Sea at 45.31◦N, 12.50◦E during July 2018. The AAOT is a purpose-built steel tower with a platform containing an instrument house to facilitate the measurement of ocean properties under exceptionably stable conditions (Figure 20). In total nine institutes participated in the international intercomparison: University of Algarve (UAlg, Faro, Portugal); Tartu Observatory, University of Tartu (UTar, Tartu, Estonia); Helmholtz-Zentrum Geesthacht (HZG, Geesthacht, Germany); Alfred Wegener Institute (AWI, Bremerhaven, Germany); Royal Belgian Institute of Natural Sciences (RBINS, Brussels, Belgium); Plymouth Marine Laboratory

(PML, Plymouth, United Kingdom); University of Victoria (UVic, Victoria, BC, Canada); Flanders Marine Institute (VLIZ, Ostend, Belgium); Laboratoire d'Oceanographique de Villefranche-sur-Mer (LOV, Villefranche-sur-Mer, France). This enabled the comparison of ten measurement systems comprising 29 radiometers.

**Figure 20.** Layout of the Acqua Alta Oceanographic Tower (AAOT). Reproduced with permission from [54].

To rule out any differences arising from absolute radiometric calibration, all of the sensors used during the campaign were calibrated at the University of Tartu (UT), under the same conditions, within ~1 month of the campaign. Measurements were then performed at the AAOT under near ideal conditions, on the same deployment platform and frame, under clear sky conditions, relatively low sun zenith angles and moderately low sea state.

All above-water radiometers except the PANTHYR system were located on the same purpose-built frames. The radiance sensors were located on the deployment platform on level 3 ona6m pole that situated them above the solar panels on level 4 (Figure 20). The frame was fabricated from aluminium to position the sensors side by side at 12.3 m from the sea surface (Figure 21A). All *L*sky and *L*t sensors were installed on this frame with identical viewing zenith angles and the deployment frame was adjusted for each measurement sequence to reduce sun glint. The radiance mast was positioned at the same level as the SeaPRISM AERONET-OC system (Figure 21B,C). For irradiance measurements, a telescopic (Fireco) mast was used on level 4 to minimise interference from the tower super-structure and other overhead equipment (Figure 21E,F). The mast and sensors were installed in the eastern corner of the platform at a height of 18.9 m above the sea surface (Figure 21E). The in-water deployment of a TRIOS profiler was carried out using an extendable boom from level 4 of the tower, whereas the C-OPS in water system was deployed from the CNR Research Vessel *Litus*.

**Figure 21.** Configuration of sensors on the AAOT platform showing (**A**) the mounting for *L*sky and *L*t radiometers, (**B**) location of radiance sensors next to the AERONET-OC SeaPRISM, (**C**) location of the radiance sensors on level 3 of the AAOT, (**D**) location of the irradiance sensors on the mounting block, (**E**) telescopic mast with irradiance sensors at the eastern corner of the AAOT, (**F**) proximity of the telescopic mast with irradiance sensors and the PANTHYR system just above the railings below. Reproduced with permission from [54].

Measurements were made from the 13th to 17th July 2018. All above water measurements were conducted every 20 min from 08:00 to 13:00 GMT over a discrete measurement period of 5 min (known as casts). In water C-OPS were also coordinated to these times and in water TRIOS measurements were made directly after the above water casts. Only casts with wind speeds < 5ms−1 and clear skies (no cloud) were accepted. Using these criteria, 35 casts were valid from the campaign. Each institute used their standard processing to compute downwelling irradiance (*E*d), sky radiance (*L*sky), radiance from the water surface (*L*t) and remote sensing reflectance (*R*rs). Mean, median and standard deviation values of these parameters over each 5-min cast were submitted. These were compared to the weighted mean of above-water systems that were submitted by the 'blind' submission date, and subsequently used as a reference.

For downwelling irradiance (*E*d), there was generally good agreement between sensors with differences of <6% for most of the sensors over the spectral range 400 nm–665 nm. One sensor exhibited a systematic bias, of up to 11%, due to poor cosine response. For *L*sky, the spectrally averaged difference between optical systems was <2.5% and for *L*t the difference was <3.5%. For *R*rs, the differences between above-water TriOS RAMSES were <3.5% and <2.5% at 443 and 560 nm, respectively, and were <7.5% for some systems at 665 nm. Seabird HyperSAS sensors were on average within 3.5% at 443 nm, 1% at 560 nm, and 3% at 665 nm. The differences between the weighted mean of the above-water and in-water systems was <16.5% across visible bands (Figure 22).

**Figure 22.** Scatter plots of *R*rs from the different above- and in-water systems versus weighted mean *R*rs from above-water systems (RAMSES-A, -B, -C, HyperSAS-A, -B). Reproduced with permission from [54].

These results give an indication of the importance and need for similar regular comparisons in the future highlighting errors in or differences between sensor systems and methods and helping characterise possible uncertainties. A more detailed analysis can be found in [54].

#### *7.3. The FRM4SOC Field Intercomparison Database of OCR*

During the course of the project PML designed and built a database for FRM4SOC. Essentially this is a PostgreSQL database with a GIS web portal interface. It provides a web interface to remotely sensed, modelled and in situ data. Its functionality includes the ability to carry out simple analysis and plotting, as well as at all stages of analysis the ability to download data for local processing if preferred. Figure 23 shows the overall design.

The portal uses the Open Geospatial Consortium (OGC) Web Map Service for displaying imagery data and the OGC Web Feature Service (WFS) and Sensor Observation Service (SOS) interface standards for interacting with in situ data. The analysis and plotting capabilities include: time series; latitude or longitude Hovmöller plots; scatter/regression; compositing; animations; and match-ups from CSV files. Data from the AMT cruises and the AAOT experiment have been included along with the calibration and traceability information for the OCR radiometers that were used throughout the FRM4SOC intercomparisons.

**Figure 23.** The architecture and functionality of the FRM4SOC field intercomparison database. Graphs and plots are included only as examples of the functionality of the visualisation tool.

#### **8. End-to-End Uncertainty**

Having an uncertainty estimate for a measurement is crucial for objectively and numerically gauging how much trust we can place in that measurement. Furthermore, an uncertainty estimate or budget for a field OCR measurement should be constructed and calculated from uncertainty estimates from an unbroken chain of calibrations back to a primary reference standard (preferably SI), in order for this measurement to be considered as an FRM. This concept of end-to-end uncertainty for FRM4SOC meant using NMI agreed protocols to conduct a derivation and specification of uncertainty budgets for FRM OCR field measurements used for satellite OCR validation that had been collected as part of FRM4SOC.

NPL therefore developed a methodology that was based on the guide to the expression of uncertainty in measurement (GUM) [53]. This was based on the Monte Carlo method of uncertainty evaluation GUM supplement [55] and calculated this uncertainty budget for three TriOS RAMSES instruments, one ACC-VIS measuring irradiance and two ARC-VIS measuring radiance, supplied by the University of Tartu [56].

These radiometers were used throughout FRM4SOC, i.e., they were calibrated, characterised and used as part of the laboratory intercomparison measurements, the controlled outdoor intercomparison measurements and the FRM4SOC field intercomparison experiment at the Acqua Alta Oceanographic Tower (AAOT) in the Gulf of Venice (see previous sections). It is these AAOT measurements that were used as the example where uncertainty is propagated from the preceding FRM4SOC calibrations and characterisations. Two sets of observations of irradiance and radiance were used from the AAOT, one from 13th July 2018 between 11:00 and 11:04 ('cast 1') and another from 14th July 2018 between 11:40 and 11:44 local time ('cast 2'). At these times, downwelling irradiance, downwelling radiance and upwelling radiance were all measured simultaneously. Measurements were performed at the AAOT under near ideal conditions, on the same deployment platform and frame (see previous section), under clear sky conditions, sun zenith angles of approximately 24◦ and moderately low sea state with wind speed of 3.1 m s−1 and 0.5 m s−1 for each cast, respectively. The average chlorophyll content was Chl = 0.77 mg m−3 and absorption of the coloured dissolved organic matter was CDOM (442 nm) = 0.12 m−1.

A Monte Carlo approach was chosen for this uncertainty propagation because the analytical method can become difficult to apply on complex functions with many correlated input parameters where the calculation of sensitivity coefficients is not straightforward. Monte Carlo Methods (MCM) for uncertainty estimation are recognised, accepted and summarised in the GUM supplement [55]. MCM is a numerical method that requires a distinct probability distribution function (PDF) for each of the input components; if input components are correlated then the joint PDF and the measurement equation are required. The MCM will then run a large number of numerical calculations of the measurement equation and with each iteration will use a random choice of each of the inputs from the available range defined by the relevant PDF. The large number of output values calculated using different input values at each iteration, provides the uncertainty of the output value with its PDF.

The true value of a measurement can never be exactly known; only an estimate can be made which is as good as the instrument and method used. Therefore, an error (bias) will always exist between the measured and best estimate value. Figures 24 and 25 illustrate the error (bias) contributions for the measurement equations for downwelling irradiance and water-leaving radiance respectively. These diagrams were first designed in the Horizon 2020 FIDUCEO project [57] to show the sources of uncertainty from their origin through to the measurement equation. The outer labels describe the effects that cause the corresponding uncertainty.

**Figure 24.** Uncertainty tree diagram for downwelling irradiance (*E*d). Reproduced with permission from [56].

To propagate uncertainty for the measurands of interest for FRM4SOC (*E*d and *L*w) the following Monte Carlo approach was applied:

**Figure 25.** Uncertainty tree diagram for water leaving radiance (*L*w). Reproduced with permission from [56].

Specifically, two scenarios were investigated in which the known biases are corrected (the ideal case), and the known biases are not corrected but treated as an uncertainty contributor (the non-ideal case). In addition, we present how the non-ideal case shows an under-estimation of measurement uncertainty because the biases are not corrected and the errors, due to a lack of that correction, are not accounted for. The required data for this activity included downwelling irradiance, downwelling radiance and upwelling radiance as well as all correction factors, the Fresnel reflectance of the water surface, and the fraction of diffuse to direct radiation at the time of measurement.

The resultant outputs of the uncertainty analysis are therefore for the ideal and non-ideal cases, as well as a corrected case where an extra correction is applied to show the true resultant uncertainty when not corrected. The MCM for downwelling irradiance and water-leaving radiance was run over two casts and results in Tables 2 and 3 are presented for the seven OLCI bands of interest (400, 442.5, 490, 560, 665, 778.8, 865 nm). It should be noted that environmental uncertainty is not included, and this may be the major limiting factor since it is likely to be larger than the absolute calibration uncertainty. An evaluation of how to correctly estimate environmental uncertainty for a range of conditions is yet to be completed.

**Table 2.** The mean and standard uncertainty as a percentage of the mean of the downwelling irradiance, *E*d [mWm−2 nm−1], presented for the ideal and non-ideal cases. Reproduced with permission from [56].

**Table 3.** The mean and standard uncertainty as a percentage of the mean of water-leaving radiance *L*w [mWm−2 nm−1 sr−1] presented for the ideal and non-ideal cases. Reproduced with permission from [56].

This part of FRM4SOC therefore demonstrated how to conduct an end-to-end uncertainty analysis for in situ radiometers of ocean colour measurements. The results of the three scenarios (ideal, non-ideal and corrected non-ideal) in Tables 2 and 3 highlight the importance and benefits of carrying out instrument characterisations before campaigns and performing instrument corrections in addition to absolute radiometric calibration. It is recommended that the sources of uncertainty that are likely to dominate over the absolute calibration uncertainty (or other more dominant uncertainty contributors which cannot be corrected for) should be characterised before campaigns so that these can be corrected for. This will produce results with reduced uncertainties as demonstrated in the ideal scenario (Tables 2 and 3). The most likely parameters that will need prior characterisations are stray light, cosine response, temperature and non-linearity corrections. Full details can be found in [56] and following these guidelines will support compliance with the FRM requirements of in situ ocean colour measurements for use in satellite product validation.

#### **9. Conclusions and the Road Map for the FRM-Based Future of Satellite Ocean Colour Validation and Vicarious Calibration**

The work and results of FRM4SOC highlighted in this paper is already having a significant impact on the earth observation and ocean colour community. In particular, FRM4SOC played a prominent role in the two previous Sentinel-3 validation team meetings at EUMETSAT [58,59], and the FRM4SOC international workshop report [22] on ocean colour system vicarious calibration (OC-SVC) is being used as one of the main requirements reference documents for the future of Copernicus OC-SVC infrastructure. The project has also inspired a sibling in the form of the amt4sentinelfrm project run by the Plymouth Marine Laboratory of the UK specifically for following FRM principles in the measurements taken on the yearly Atlantic Meridional Transect cruises [50,60].

Even though the FRM4SOC developed measurement protocols and uncertainty budgets have been thoroughly tested in several laboratory and in-field comparison exercises, and the space agencies are beginning to demand FRM for satellite product validation, there remains considerable effort required before FRM in ocean colour has gained widespread adoption within the ocean colour validation community. Considering that this continued effort is in support of ensuring high quality and accuracy Copernicus satellite mission data, in particular Sentinel-2 MSI and Sentinel-3 OLCI ocean colour products, and contributes directly to the work of ESA and EUMETSAT to ensure that these instruments are validated in orbit, FRM4SOC produced a scientific road map for the FRM-based future of satellite ocean colour validation and vicarious calibration [61]. Therefore, along with the main project conclusions in the form of recommendations, this paper concludes with the main associated FRM4SOC scientific road map recommended actions (Figure 26).

#### **IMPLEMENTING FRM**

#### **METHODS, PROTOCOLS AND PROCEDURES AND UNCERTAINTY BUDGETS**

	- a. harmonise measurement protocols;
	- b. agree and establish principles of good practice in performing measurements, particularly to estimate and document measurement uncertainties;
	- c. identify, harmonise and establish requirements for measurement and correction of gains and assess their uncertainty levels;
	- d. provide consolidated examples on compiling uncertainty budgets
	- e. provide training on good practice and building uncertainty budgets.

#### **PROPERTIES OF OCR**

**Figure 26.** *Cont.*

encouraged and funded by ESA and other space agencies or entities.

#### **COMPARISON EXPERIMENTS AND DATABASE OF OCR FIELD RADIOMETER PERFORMANCE**

	- a. reference standards (NMI and OCR calibration laboratory level);
	- b. calibration and characterisation methods of OCR (calibration laboratory level);
	- c. in situ field measurements:
		- understanding, interpretation, and following established protocols;
		- competence and experience of personnel (all levels).

**A11** Ensure appropriate funding to organise comparison experiments for validation of established methods and uncertainty budgets on all levels of the traceability chain.

#### **OPTIONS FOR LONG-TERM FUTURE EUROPEAN SATELLITE OCR VICARIOUS ADJUSTMENT**

**Figure 26.** *Cont.*

**Figure 26.** Conclusions (**C**) from the FRM4SOC project and their recommended actions (**A**).

**Author Contributions:** Conceptualisation, A.C.B, R.V., C.D., C.L., K.R., G.T., T.C.; methodology, A.C.B., R.V., C.D., K.A., A.B., J.K., C.L., K.R., G.T., V.V.; writing—original draft preparation, ACB; writing—review and editing, A.C.B., R.V., C.D., K.A., A.B., J.K., K.R., G.T., V.V.; visualisation, A.C.B., R.V., K.A., A.B., K.R., G.T., J.K., V.V.; project administration, R.V., C.D., T.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the European Space Agency project Fiducial Reference Measurements for Satellite Ocean Colour (FRM4SOC), contract No.<PHONE_NUMBER>/16/I-Sbo."

**Acknowledgments:** Input and comments from numerous scientists and experts are gratefully acknowledged. The FRM4SOC team, G. Zibordi, D. Antoine, all participants of the project events, and manufacturers of the ocean colour radiometers are kindly thanked for the valuable support and contribution to the project outcomes and this paper.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


*1.3. Problems with AD of Bio-Waste*

Due to a variable of bio-waste composition, conducting the AD process entails certain difficulties. To maintain biogas production at a stable level, many monitoring parameters need to be taken into count (feedstock size, total solids, volatile solids, pH value, ammonium nitrogen, volatile fatty acids (VFA), redox potential, alkalinity ratio, biogas composition (CH4, CO2, H2 and H2S), temperature, trace elements concentration, organic loading rate (OLR), and hydraulic retention time (HRT)). As a result, trained workers with laboratory equipment are needed [11]. Lack of concise process control and optimization of biowaste composition lead to harmful intermediate compounds' production and process instability. It is due to organic waste nature. Most FW has acidic pH which consumes digested feedstock alkalinity and is quickly decomposed during the hydrolysis phase. Quick decomposition with a combination of high protein and lipids content leads to rapid generation and accumulation of ammonia (NH3), and VFAs over inhibitory levels [12]. Though high VFA concentration does not have to inhibit the process since VFAs are essential nutrients for bacteria growth, pH value needs to be kept at an optimal level to balance the inhibitory effects of VFAs and NH3 [13]. As a result of difficulties, AD of bio-waste (especially FW) is often performed at a low OLR of 2–3 gCOD × (L × d)−1 [12]. For that reason, different substances improving process stability and performance are added [13]. One such substance getting attention recently is biochar.

Biochar is considered as the material improving the methane fermentation process [14]. Biochar can absorb compounds such as H2S and CO2, and it also has the potential to mitigate the inhibition of ammonia and acids. It also creates an optimal environment for the growth of microorganisms, which results in faster colony development and higher biogas yield. The effect of biochar addition (positive or negative) depends on the specific situation like reactor type (batch, continuous) substrate type, type of fermentation, type of the biochar, and others [14].

The biochar is produced from organic materials during thermal processing at temperatures above 300 ◦C in a free oxygen atmosphere. Depending on conditions, the process is called torrefaction (200–320 ◦C), pyrolysis (>300 ◦C) [15], or hydrothermal carbonization (180–320 ◦C) [16]. Besides temperature, other parameters specify these processes, inter alia residence time, pressure, and initial moisture. Torrefaction and pyrolysis are performed at atmospheric pressure for pre-dried materials, while hydrothermal carbonization is performed at overpressure for wet materials. Each process has pros and cons and is used for different materials and purposes. The amount and quality (desired properties) of carbonaceous material obtained from thermal processing depends on feedstock type and process conditions. In general, the higher the process temperature, the more energy-consuming the thermal processing, and the lower amount of biochar is produced in favor of the yield of other products (liquid and gases) [15–17]. Therefore, low-temperature biochars produced with lower energy demand than under high-temperature pyrolysis may be considered as a sustainable source of structural additive for FW AD. The scientific question on its influence on AD performance may be derived.

#### *1.4. Study Aim*

All the advantages of the AD process improvement by biochar addition have not been fully explored because biochar can be produced from various substrates, under different conditions, and various substrates can be processed by AD. Additionally, the application of biochar produced from the same materials as being processed under AD has been rarely studied [18]. In this work, five low-temperature biochars that potentially could be made using residual heat from biogas combined heat and power units (300–400 ◦C) were produced and used to enhance the AD of FW. Moreover, biochars were produced from the substrate (here food waste) under torrefaction, low-temperature pyrolysis, and hydrothermal carbonization conditions.

#### **2. Materials and Methods**

#### *2.1. Materials*

#### 2.1.1. Inoculum Preparation

As inoculum for biochemical methane potential tests, digestate from the 1 MWel commercial agricultural biogas plant (Bio-Wat Sp. z o.o., Swidnica, Poland) was used. The ´ biogas plant is operating on wet (dry mas < 10%) and mesophilic conditions (37 ◦C). The digestate was collected to plastic canisters and was taken to the laboratory where it was stored at room temperature for ~24 h. The next day, the digestate was filtered through gauze to separate liquid from solid particles: unprocessed substrate, plastics, etc. Then, the liquid digestate was stored in the climate chamber (Pollab, model 140/40, Wilkowice, Poland) at 4 ◦C before the biochemical methane potential test.

#### 2.1.2. Food Waste Preparation

The food waste mixture for biochemical methane potential tests was prepared from food purchased in the grocery store. The mixture consists of 3.67% of orange, 8.67% of banana, 7.33% of apple, 1.33% of lemon, 24.33% of potatoes, 4.67% of onion, 3.33% of salad, 3.33% of cabbage, 2.33% of tomatoes, 6% of rice, 6% of pasta, 3% of bread, 3% of meat, 12% of fish meat, and 11% of cheese by fresh mass. The fresh food waste mixture had 64.2% of moisture content (MC), while volatile solids (VS) constituted 95.8% of dry mass. The ash content (AC) of the mixture was 4.2%. The FW composition was based on the work of Valta et al. [19]. The properties of moisture content, total solids (TS), volatile solids (organic matter content), and ash content, of used food materials, and mixture composition per fresh, dry, and volatile solids percentage share bases are presented in Table 1.

**Table 1.** Food waste properties and its share in food waste mixtures.

\* as received base. \*\* as dry base.

FW components were dried in the laboratory dryer (WAMED, model KBC-65W, Warsaw, Poland) at 105 ◦C and shredded. Drying time differed depending on the food type. Then, dry food was ground through a 1 mm screen using a laboratory knife mill (Testchem, model LMN-100, Pszów, Poland). Ground FW samples were stored in plastic string bags, at room temperature. The mixture for AD was prepared from ground dry food materials according to data presented in Table 1. To ensure mixture homogeneity, one portion of 1 kg was prepared before the biochemical methane potential test. In addition, all tests were done using this mixture.