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A global in situ data set for validation of ocean colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI) is presented. This version of the compilation, starting in 1997, now extends to 2021, which is important for the validation of the most recent satellite optical sensors such as Sentinel 3B OLCI and NOAA-20 VIIRS. The data set comprises in situ observations of the following variables: spectral remote-sensing reflectance, concentration of chlorophyll-a, spectral inherent optical properties, spectral diffuse attenuation coefficient, and total suspended matter. Data were obtained from multi-project archives acquired via open internet services or from individual projects acquired directly from data providers. Methodologies were implemented for homogenization, quality control, and merging of all data. Minimal changes were made on the original data, other than conversion to a standard format, elimination of some points, after quality control and averaging of observations that were close in time and space. The result is a merged table available in text format. Overall, the size of the data set grew with 148 432 rows, with each row representing a unique station in space and time (cf. 136 250 rows in previous version; Valente et al., 2019). Observations of remote-sensing reflectance increased to 68 641 (cf. 59 781 in previous version; Valente et al., 2019). There was also a near tenfold increase in chlorophyll data since 2016. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) are included in the final table. By making the metadata available, provenance is better documented and it is also possible to analyse each set of data separately. The compiled data are available at https://doi.org/10.1594/PANGAEA.941318 (Valente et al., 2022).

The MALINA oceanographic campaign was conducted during summer 2009 to investigate the carbon stocks and the processes controlling the carbon fluxes in the Mackenzie River estuary and the Beaufort Sea. During the campaign, an extensive suite of physical, chemical and biological variables were measured across seven shelf–basin transects (south–north) to capture the meridional gradient between the estuary and the open ocean. Key variables such as temperature, absolute salinity, radiance, irradiance, nutrient concentrations, chlorophyll a concentration, bacteria, phytoplankton and zooplankton abundance and taxonomy, and carbon stocks and fluxes were routinely measured onboard the Canadian research icebreaker CCGS Amundsen and from a barge in shallow coastal areas or for sampling within broken ice fields. Here, we present the results of a joint effort to compile and standardize the collected data sets that will facilitate their reuse in further studies of the changing Arctic Ocean. The data set is available at https://doi.org/10.17882/75345 (Massicotte et al., 2020).

This study establishes an optical inversion scheme for deriving the absorption coefficient of colored (or chromophoric, depending on the literature) dissolved organic material (CDOM) at the 440 nm wavelength, which can be applied to global water masses with near-equal efficacy. The approach uses a ratio of diffuse attenuation coefficient spectral end-members, i.e., a short- and long-wavelength pair. The global perspective is established by sampling “extremely” clear water plus a generalized extent in turbidity and optical properties that each span 3 decades of dynamic range. A unique data set was collected in oceanic, coastal, and inland waters (as shallow as 0.6 m) from the North Pacific Ocean, the Arctic Ocean, Hawaii, Japan, Puerto Rico, and the western coast of the United States. The data were partitioned using subjective categorizations to define a validation quality subset of conservative water masses (i.e., the inflow and outflow of properties constrain the range in the gradient of a constituent) plus 15 subcategories of more complex water masses that were not necessarily evolving conservatively. The dependence on optical complexity was confirmed with an objective methodology based on a cluster analysis technique. The latter defined five distinct classes with validation quality data present in all classes, but which also decreased in percent composition as a function of increasing class number and optical complexity. Four algorithms based on different validation quality end-members were validated with accuracies of 1.2 %–6.2 %, wherein pairs with the largest spectral span were most accurate. Although algorithm accuracy decreased with the inclusion of more subcategories containing nonconservative water masses, changes to the algorithm fit were small when a preponderance of subcategories were included. The high accuracy for all end-member algorithms was the result of data acquisition and data processing improvements, e.g., increased vertical sampling resolution to less than 1 mm (with pressure transducer precision of 0.03–0.08 mm) and a boundary constraint to mitigate wave-focusing effects, respectively. An independent evaluation with a historical database confirmed the consistency of the algorithmic approach and its application to quality assurance, e.g., to flag data outside expected ranges, identify suspect spectra, and objectively determine the in-water extrapolation interval by converging agreement for all applicable end-member algorithms. The legacy data exhibit degraded performance (as 44 % uncertainty) due to a lack of high-quality near-surface observations, especially for clear waters wherein wave-focusing effects are problematic. The novel optical approach allows the in situ estimation of an in-water constituent in keeping with the accuracy obtained in the laboratory.

A global compilation of in situ data is useful to evaluate the quality of ocean-colour satellite data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (including, inter alia, MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT and GeP&CO) and span the period from 1997 to 2018. Observations of the following variables were compiled: spectral remote-sensing reflectances, concentrations of chlorophyll a, spectral inherent optical properties, spectral diffuse attenuation coefficients and total suspended matter. The data were from multi-project archives acquired via open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenization, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The final result is a merged table designed for validation of satellite-derived ocean-colour products and available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) was propagated throughout the work and made available in the final table. By making the metadata available, provenance is better documented, and it is also possible to analyse each set of data separately. This paper also describes the changes that were made to the compilation in relation to the previous version (Valente et al., 2016). The compiled data are available at https://doi.org/10.1594/PANGAEA.898188 (Valente et al., 2019).

In addition to scattering coefficients, the light absorption coefficients of particulate and dissolved materials are the main factors determining the light propagation of the visible part of the spectrum and are, thus, important for developing ocean color algorithms. While these absorption properties have recently been documented by a few studies for the Arctic Ocean (e.g., Matsuoka et al., 2007, 2011; Ben Mustapha et al., 2012), the data sets used in the literature were sparse and individually insufficient to draw a general view of the basin-wide spatial and temporal variations in absorption. To achieve such a task, we built a large absorption database of the Arctic Ocean by pooling the majority of published data sets and merging new data sets. Our results show that the total nonwater absorption coefficients measured in the eastern Arctic Ocean (EAO; Siberian side) are significantly higher than in the western Arctic Ocean (WAO; North American side). This higher absorption is explained by higher concentration of colored dissolved organic matter (CDOM) in watersheds on the Siberian side, which contains a large amount of dissolved organic carbon (DOC) compared to waters off North America. In contrast, the relationship between the phytoplankton absorption (aϕ(λ)) and chlorophyll a (chl a) concentration in the EAO was not significantly different from that in the WAO. Because our semianalytical CDOM absorption algorithm is based on chl a-specific aϕ(λ) values (Matsuoka et al., 2013), this result indirectly suggests that CDOM absorption can be appropriately derived not only for the WAO but also for the EAO using ocean color data. Based on statistics, derived CDOM absorption values were reasonable compared to in situ measurements. By combining this algorithm with empirical DOC versus CDOM relationships, a semianalytical algorithm for estimating DOC concentrations for river-influenced coastal waters of the Arctic Ocean is presented and applied to satellite ocean color data.

A data set of radiometric measurements collected in the Beaufort Sea (Canadian Arctic) in August 2009 (Malina project) is analyzed in order to describe apparent optical properties (AOPs) in this sea, which has been subject to dramatic environmental changes for several decades. The two properties derived from the measurements are the spectral diffuse attenuation coefficient for downward irradiance, Kd, and the spectral remote sensing reflectance, Rrs. The former controls light propagation in the upper water column. The latter determines how light is backscattered out of the water and becomes eventually observable from a satellite ocean color sensor. The data set includes offshore clear waters of the Beaufort Basin as well as highly turbid waters of the Mackenzie River plumes. In the clear waters, we show Kd values that are much larger in the ultraviolet and blue parts of the spectrum than what could be anticipated considering the chlorophyll concentration. A larger contribution of absorption by colored dissolved organic matter (CDOM) is responsible for these high Kd values, as compared to other oligotrophic areas. In turbid waters, attenuation reaches extremely high values, driven by high loads of particulate materials and also by a large CDOM content. In these two extreme types of waters, current satellite chlorophyll algorithms fail. This questions the role of ocean color remote sensing in the Arctic when Rrs from only the blue and green bands are used. Therefore, other parts of the spectrum (e.g., the red) should be explored if one aims at quantifying interannual changes in chlorophyll in the Arctic from space. The very peculiar AOPs in the Beaufort Sea also advocate for developing specific light propagation models when attempting to predict light availability for photosynthesis at depth.

A next-generation in-water profiler designed to measure the apparent optical properties (AOPs) of seawater was developed and validated across a wide dynamic range of in-water properties. The new free-falling instrument, the Compact-Optical Profiling System (C-OPS), was based on sensors built with a cluster of 19 state-of-the-art microradiometers spanning 320–780 nm and a novel kite-shaped backplane. The new backplane includes tunable ballast, a hydrobaric buoyancy chamber, plus pitch and roll adjustments, to provide unprecedented stability and vertical resolution in near-surface waters. A unique data set was collected as part of the development activity plus the first major field campaign that used the new instrument, the Malina expedition to the Beaufort Sea in the vicinity of the Mackenzie River outflow. The data were of sufficient resolution and quality to show that errors – more correctly, uncertainties – in the execution of data sampling protocols were measurable at the 1% and 1 cm level with C-OPS. A theoretical sensitivity analysis as a function of three water types established by the peak in the remote sensing reflectance spectrum, Rrs(λ), revealed which water types and which parts of the spectrum were the most sensitive to data acquisition uncertainties. Shallow riverine waters were the most sensitive water type, and the ultraviolet and near-infrared spectral end members, which are critical to next-generation satellite missions, were the most sensitive parts of the spectrum. The sensitivity analysis also showed how the use of data products based on band ratios significantly mitigated the influence of data acquisition uncertainties. The unprecedented vertical resolution provided high-quality data products, which supported an alternative classification capability based on the spectral diffuse attenuation coefficient, Kd(λ). The Kd(320) and Kd(780) data showed how complex coastal systems can be distinguished two-dimensionally and how near-ice water masses are different from the neighboring open ocean. Finally, an algorithm for predicting the spectral absorption due to colored dissolved organic matter (CDOM), denoted aCDOM(λ), was developed using the Kd(320) / Kd(780) ratio, which was based on a linear relationship with respect to aCDOM(440). The robustness of the approach was established by expanding the use of the algorithm to include a geographically different coastal environment, the Southern Mid-Atlantic Bight, with no significant change in accuracy (approximately 98% of the variance explained). Alternative spectral end members reminiscent of next-generation (340 and 710 nm) as well as legacy satellite missions (412 and 670 nm) were also used to accurately derive aCDOM(440) from Kd

A series of papers have suggested that freshwater discharge, including a large amount of dissolved organic matter (DOM), has increased since the middle of the 20th century. In this study, a semi-analytical algorithm for estimating light absorption coefficients of the colored fraction of DOM (CDOM) was developed for southern Beaufort Sea waters using remote sensing reflectance at six wavelengths in the visible spectral domain corresponding to MODIS ocean color sensor. This algorithm allows the separation of colored detrital matter (CDM) into CDOM and non-algal particles (NAP) through the determination of NAP absorption using an empirical relationship between NAP absorption and particle backscattering coefficients. Evaluation using independent datasets, which were not used for developing the algorithm, showed that CDOM absorption can be estimated accurately to within an uncertainty of 35% and 50% for oceanic and coastal waters, respectively. A previous paper (Matsuoka et al., 2012) showed that dissolved organic carbon (DOC) concentrations were tightly correlated with CDOM absorption in our study area (r2 = 0.97). By combining the CDOM absorption algorithm together with the DOC versus CDOM relationship, it is now possible to estimate DOC concentrations in the near-surface layer of the southern Beaufort Sea using satellite ocean color data. DOC concentrations in the surface waters were estimated using MODIS ocean color data, and the estimates showed reasonable values compared to in situ measurements. We propose a routine and near real-time method for deriving DOC concentrations from space, which may open the way to an estimate of DOC budgets for Arctic coastal waters.