The Ocean Colour Climate Change Initiative: III. A round-robin comparison on in-water bio-optical algorithms

Satellite-derived remote-sensing reflectance (Rrs) can be used for mapping biogeochemically relevant variables, such as the chlorophyll concentration and the Inherent Optical Properties (IOPs) of the water, at global scale for use in climate-change studies. Prior to generating such products, suitable algorithms have to be selected that are appropriate for the purpose. Algorithm selection needs to account for both qualitative and quantitative requirements. In this paper we develop an objective methodology designed to rank the quantitative performance of a suite of bio-optical models. The objective classification is applied using the NASA bio-Optical Marine Algorithm Dataset (NOMAD). Using in situRrs as input to the models, the performance of eleven semi-analytical models, as well as five empirical chlorophyll algorithms and an empirical diffuse attenuation coefficient algorithm, is ranked for spectrally-resolved IOPs, chlorophyll concentration and the diffuse attenuation coefficient at 489 nm. The sensitivity of the objective classification and the uncertainty in the ranking are tested using a Monte-Carlo approach (bootstrapping). Results indicate that the performance of the semi-analytical models varies depending on the product and wavelength of interest. For chlorophyll retrieval, empirical algorithms perform better than semi-analytical models, in general. The performance of these empirical models reflects either their immunity to scale errors or instrument noise in Rrs data, or simply that the data used for model parameterisation were not independent of NOMAD. Nonetheless, uncertainty in the classification suggests that the performance of some semi-analytical algorithms at retrieving chlorophyll is comparable with the empirical algorithms. For phytoplankton absorption at 443 nm, some semi-analytical models also perform with similar accuracy to an empirical model. We discuss the potential biases, limitations and uncertainty in the approach, as well as additional qualitative considerations for algorithm selection for climate-change studies. Our classification has the potential to be routinely implemented, such that the performance of emerging algorithms can be compared with existing algorithms as they become available. In the long-term, such an approach will further aid algorithm development for ocean-colour studies.

Reply to Haddock, S. H. D. Reconsidering evidence for potential climate-related increases in jellyfish

Following the publication of our paper (Attrill et al. 2007), we became quickly aware of a couple of errors. We have subsequently been collaborating with Dr. Chris Lynam (Lynam et al. 2004, 2005) to bring together our two datasets, explore the common patterns within our data, and attempt to provide a consensus on how climate is affecting gelatinous plankton in the North Sea. During this reanalysis, two errors within the data were discovered, one involving a transcription error of a column of residuals during de-trended analysis, the other a major data entry error deep in the Continuous Plankton Recorder (CPR) database for sector B2. Here we present a revised version of table 1 from Attrill et al. (2007) to incorporate corrections to these transcription and data entry errors. These corrections alter some of the results in our original data table, mainly to increase and strengthen the number of significant relations we found (e.g., for sector B2 and whole sea area); all previous main results remain robustly significant. Following discussions with Dr. Lynam, two clarifications of statements made in Attrill et al. (2007) are also required. Page 482, Results, last line of first column: ‘‘There were no...robust, consistent relations between jellyfish frequency and any environmental variables for B and D… contrary to the findings of previous shorter time series (Lynam et al. 2005).’’ The Lynam et al. (2004, 2005) papers presented no data for the D sector and found no link in the B sector, contrary to our revised results. Page 482, Discussion, paragraph 1, last sentence: ‘‘… positive association … North of Scotland (Lynam et al. 2005) … does not appear to be maintained.’’ Our paper did not report on any data that covered Lynam et al.’s (2005) North of Scotland area so the statement is not directly supported, although their positive relation North of Scotland, when considered in conjunction with inflow, may agree with the C2 and B2 results of Attrill et al. (2007).

The potential use of mapped extent and distribution of habitats as indicators of Good Environmental Status (GES)

The Healthy and Biologically Diverse Seas Evidence Group (HBDSEG) has been tasked with providing the technical advice for the implementation of the Marine Strategy Framework Directive (MSFD) with respect to descriptors linked to biodiversity. A workshop was held in London to address one of the Research and Development (R&D) proposals entitled: ‘Mapping the extent and distribution of habitats using acoustic and remote techniques, relevant to indicators for area/extent/habitat loss.’ The aim of the workshop was to identify, define and assess the feasibility of potential indicators of benthic habitat distribution and extent, and identify the R&D work which could be required to fully develop these indicators. The main points that came out of the workshop were: (i) There are many technical aspects of marine habitat mapping that still need to be resolved if cost-effective spatial indicators are to be developed. Many of the technical aspects that need addressing surround issues of consistency, confidence and repeatability. These areas should be tackled by the JNCC Habitat Mapping and Classification Working Group and the HBDSEG Seabed Mapping Working Group. (ii) There is a need for benthic ecologists (through the HBDSEG Benthic Habitats Subgroup and the JNCC Marine Indicators Group) to finalise the list of habitats for which extent and/or distribution indicators should be considered for development, building upon the recommendations from this report. When reviewing the list of indicators, benthic habitats could also be distinguished into those habitats that are defined/determined primarily by physical parameters (although including biological assemblages) (e.g. subtidal shallow sand) and those defined primarily by their biological assemblage (e.g. seagrass beds). This distinction is important as some anthropogenic pressures may influence the biological component of the ecosystem despite not having a quantifiable effect on the physical habitat distribution/extent. (iii) The scale and variety of UK benthic habitats makes any attempt to undertake comprehensive direct mapping exercises prohibitively expensive (especially where there is a need for repeat surveys for assessment). There is a clear need therefore to develop a risk-based approach that uses indirect indicators (e.g. modelling), such as habitats at risk from pressures caused by current human activities, to develop priorities for information gathering. The next steps that came out of the workshop were: (i) A combined approach should be developed by the JNCC Marine Indicators Group together with the HBDSEG Benthic Habitats Subgroup, which will compile and ultimately synthesise all the criteria used by the three different groups from the workshop. The agreed combined approach will be used to undertake a final review of the habitats considered during the workshop, and to evaluate any remaining habitats in order to produce a list of habitats for indicator development for which extent and/or distribution indicators could be appropriate. (ii) The points of advice raised at this workshop, alongside the combined approach aforementioned, and the final list of habitats for extent and/or distribution indicator development will be used to develop a prioritised list of actions to inform the next round of R&D proposals for benthic habitat indicator development in 2014. This will be done through technical discussions within JNCC and the relevant HBDSEG Subgroups. The preparation of recommendations by these groups should take into account existing work programmes, and consider the limited resources available to undertake any further R&D work.

CoastColour Round Robin datasets: a database to evaluate the performance of algorithms for the retrieval of water quality parameters in coastal waters.

The use of in situ measurements is essential in the validation and evaluation of the algorithms that provide coastal water quality data products from ocean colour satellite remote sensing. Over the past decade, various types of ocean colour algorithms have been developed to deal with the optical complexity of coastal waters. Yet there is a lack of a comprehensive intercomparison due to the availability of quality checked in situ databases. The CoastColour Round Robin (CCRR) project, funded by the European Space Agency (ESA), was designed to bring together three reference data sets using these to test algorithms and to assess their accuracy for retrieving water quality parameters. This paper provides a detailed description of these reference data sets, which include the Medium Resolution Imaging Spectrometer (MERIS) level 2 match-ups, in situ reflectance measurements, and synthetic data generated by a radiative transfer model (HydroLight). These data sets, representing mainly coastal waters, are available from doi:10.1594/PANGAEA.841950. The data sets mainly consist of 6484 marine reflectance (either multispectral or hyperspectral) associated with various geometrical (sensor viewing and solar angles) and sky conditions and water constituents: total suspended matter (TSM) and chlorophyll a (CHL) concentrations, and the absorption of coloured dissolved organic matter (CDOM). Inherent optical properties are also provided in the simulated data sets (5000 simulations) and from 3054 match-up locations. The distributions of reflectance at selected MERIS bands and band ratios, CHL and TSM as a function of reflectance, from the three data sets are compared. Match-up and in situ sites where deviations occur are identified. The distributions of the three reflectance data sets are also compared to the simulated and in situ reflectances used previously by the International Ocean Colour Coordinating Group (IOCCG, 2006) for algorithm testing, showing a clear extension of the CCRR data which covers more turbid waters.