Extensive and comprehensive field reference measurements are essential for any satellite ocean color program (e.g.: Werdell et al., 2003; Valente et al., 2022; Massicotte et al., 2023; Nechad et al., 2015). In line with this need, over a period exceeding two decades, the Coastal Atmosphere & Sea Time Series (CoASTS) and the Bio-Optical mapping of Marine optical Properties (BiOMaP) field programs led to the collection of field bio-optical measurements to support ocean color applications embracing bio-optical modelling and satellite ocean color validation (Zibordi and Berthon, 2024). The CoASTS and BiOMaP programs, established by the Marine Optical Laboratory of the Joint Research Centre (JRC) in collaboration with several European research institutions, ensured: (i) time-series measurements at the “Acqua Alta” Oceanographic Tower (AAOT) site in the northern Adriatic Sea characterized by moderately optically complex waters (Berthon et al., 2002; Zibordi et al., 2002); and (ii) geographically distributed ship-based measurements across major European Seas exhibiting a variety of water types (Berthon et al., 2008; Zibordi et al., 2011).

This work presents the CoASTS-BiOMaP data set of hyperspectral absorption coefficients determined applying spectrophotometric methods to field marine water samples. Opposite to the multi-spectral absorption coefficients included in the previous dataset of radiometric data products (Zibordi and Berthon 2024), this complementary dataset comprises absorption coefficients exhibiting 3 nm spectral resolutions at 2 nm increment for pigmented and non-pigmented particles in the 400–750 nm interval, and for colored dissolved organic matter in the 350–680 nm one. This unique dataset is expected to fulfil application needs for bio-optical investigations supporting the new generation of hyperspectral satellite ocean color sensors (e.g., Werdell et al., 2026).

CoASTS time-series started in 1995 with monthly sampling frequency, later reduced to bi-monthly (or less) up to the end of the program in 2016. CoASTS measurements comprised in-water optical and hydrographic profiles, water samples from near surface, 8 and 14 m depth, meteorological data, and cloud cover and sea state observations. Bio-optical characteristics (Berthon et al., 2002) are representative of marine regions with optical properties generally determined by moderate concentrations of sediments and colored dissolved organic matter (CDOM) and occasionally by phytoplankton and its degradation components, i.e., Case-1 waters (IOCCG, 2000).

BiOMaP ship-based campaigns were performed from 2000 up to 2022 in the major European seas (Berthon et al., 2008; Zibordi et al., 2011): the Baltic Sea exhibiting waters characterized by a high concentration of CDOM; the Adriatic Sea, Black Sea, Iberian Shelf, Greenland Sea, Ligurian Sea, and North Sea, exhibiting diverse concentrations of CDOM and suspended particulate matter (SPM); and finally the Eastern and Western Mediterranean Seas with oligotrophic and mesotrophic Case-1 waters. BiOMaP stations included the same measurements and observations as CoASTS, but water sampling was limited to the near-surface layer.

Data consistency across the two programs was ensured through the application of standardized instruments, community measurement methods, extended quality control schemes and consolidated processing.

Out of the 176 CoASTS field campaigns, only the 125 performed from December 1998 leading to a total of 634 stations were considered for the creation of the current dataset. This choice has been suggested by the objective to populate the dataset with those data fulfilling standardized methods and data reduction processes. Conversely, all the 36 BiOMaP campaigns for a total of 1915 stations, were considered.

Details on the CoASTS and BiOMaP measurement stations included in this work are summarized in Tables 1 and 2, respectively. The reader is also addressed to Zibordi and Berthon (2024) for figures presenting the temporal and geographic distribution of the overall CoASTS and BiOMaP stations, respectively.

CoASTS and BiOMaP laboratory measurements ensured the determination of the following spectral absorption coefficients from discrete water samples collected at approximately 1 m depth:

aph and adt for pigmented and for non-pigmented particles, respectively;

Details on the applied measurement methods are given in the following subsections.

CoASTS-BiOMaP hyperspectral data of absorption coefficients are accessible in tabular form at 10.1594/PANGAEA.993510.

Details on the quantities constituting the dataset are provided in Table 5. Nevertheless, the reader is addressed to Zibordi and Berthon (2024) for a comprehensive description of all the field and laboratory measurements complementing the hyperspectral absorption coefficients matter of this work. It is anticipated that the number of measurement stations included in the present dataset is slightly higher than the one constituting the multi-spectral dataset. This is explained by the quality control applied to hyperspectral data solely centred on absorption coefficients and fully independent of radiometric quantities (see Zibordi and Berthon, 2024). This solution allows for maximizing the number of quality data included in each dataset, which may not necessarily support the same scientific investigations. Specifically, some ays spectra were not included in the hyperspectral dataset because of unrealistically low values in the ultraviolet and blue spectral regions. Figures 1–3 display BioMaP and CoASTS aph(λ), adt(λ) and ays(λ) spectra for the different marine regions. These spectra exhibit bio-optical features typical of the Eastern and Western Mediterranean Seas (EMED and WMED) oligotrophic/mesotrophic waters, and of the Baltic Sea (BLTS) CDOM dominated waters. Between these two extremes, there are spectra exhibiting optical characteristics determined by variable concentrations of SPM and CDOM such as those from the Black Sea (BLKS) and Adriatic Sea (AAOT and ADRS), and those from the North Sea (NORS) characterized by relatively high concentrations of non-organic particles.

Figure 1 displays the aph spectra for the various marine regions. The increase in the mean values of aph(λ) around 440 nm from approximately 0.01 m⁻¹ for the Eastern Mediterranean Sea (EMED) waters to values approaching 0.2 m⁻¹ for the Baltic Sea (BLTS) and North Sea (NORS) ones, is noteworthy. Some North Sea spectra from samples collected off the Belgian coast exhibit values of aph higher at 400 nm than at 440 nm. This is explained by high concentrations of pheophytin leading to an increase of the absorption coefficient toward 400 nm. Finally, a few aph spectra were noted to exhibit unrealistic negative values beyond approximately 500 nm. This is explained by limits of the measurement methodology in the presence of a low concentration of particles largely associated with oligotrophic regions. These spectra were not excluded from the data set expecting their application can be still relevant if the absorption coefficients are corrected for their background in the near-infrared.

The adt(λ) spectra displayed in Fig. 2 exhibit the expected exponential decay from 400 to 750 nm, even though sometimes affected by minor irregularities such as the shoulders in the blue-green or small residual peaks in the red spectral regions, which indicate a potential uncomplete bleaching of accessory pigments. Mean values of adt at 400 nm consistently increase from approximately 0.01 m⁻¹ for the Mediterranean Sea (either EMED and WMED) waters, exceed 0.06 m⁻¹ for the Adriatic Sea (AAOT) ones, and reach 0.11 m⁻¹ for the Baltic Sea (BLTS). The background values of adt(λ) in the near-infrared reflect actual absorption by non-living particles. Figure 3 displays the ays(λ) spectra in the 350–680 nm spectral interval. The mean values of ays at 350 nm largely vary across marine regions. In agreement with the oligotrophic nature of the Eastern Mediterranean Sea (EMED) waters and the highly absorbing Baltic Sea (BLTS) ones, a minimum value of 0.13 m⁻¹ is observed for the first while a maximum of 2.2 m⁻¹ characterizes the second.

Of major interest are the slopes Sdt and Sys defining the exponential decay with wavelength of adt(λ) and ays(λ). These are determined through the exponential fit of adt(λ) and ays(λ) versus λ as 3adtλ=Adtexp-Sdtλ-412+Bdt and 4aysλ=Aysexp-Sysλ-412+Bys, where Adt and Ays indicate the absorption coefficients fitted at 412 nm, Sdt and Sys the slope of the exponential function, and Bdt and Bys account for the background. The background term Bys in Eq. (4) is included to account for any non-null value that may still affect aysλ in the red spectral region regardless of the removal of the bias determined in the 670–680 nm interval for each spectrum (see Sect. 3.2).

Zibordi and Berthon (2024) constructed multispectral adt(λ) and ays(λ) values at the nominal center-wavelengths 412, 443, 490, 510, 555, 665 nm with a spectral resolution of 10 nm from the hyperspectral values presented in this work, but imposing a generic exponential decay with wavelength of the absorption coefficients. This solution, not detailed in Zibordi and Berthon (2024), was suggested by the need to minimize any intra-band noise that may impact bio-optical applications. The slopes Sdt determined with the multispectral adt(λ) data in the 412–665 nm interval (see Zibordi and Berthon, 2024) exhibit mean values varying from 0.009 nm⁻¹ for the Eastern Mediterranean Sea (EMED) up to 0.013 for the North Sea (NORS). Similarly, the slopes Sys determined with the multispectral ays(λ) data also restricted to the 412–665 nm interval, show mean values ranging from 0.012 nm⁻¹ for the Eastern Mediterranean Sea (EMED) up to 0.019 nm⁻¹ for the Baltic Sea (BLTS).

This new work provides the opportunity to explore the fitting parameters determined with Eqs. (3) and (4), using hyperspectral adt(λ) and ays(λ) values. The additional analysis has relevance to address the impact of residual pigments absorption in adt(λ), and also the impact of diverse fitting intervals for ays(λ) benefitting of measurements extending down to 350 nm. In all cases, Eqs. (3) and (4) have been solved using the Interactive Data Language (IDL; NV5 Geospatial Solutions, Broomfield, CO, USA) Curvefit function relying on a gradient-expansion algorithm to compute the non-linear least squares fit.

Fitting results for the adt(λ) hyperspectral data are summarized in Table 6 for the 400–730 nm interval and alternatively in Table 7 for the combined 400–412 & 480–620 & 710–730 nm ones. These latter results are intended to support an evaluation of the impact of residual pigments absorption affecting adt(λ), clearly evident in some spectra illustrated in Fig. 2 between 410 and 480 nm, as well as between 620 and 710 nm.

Fitting results for the ays(λ) hyperspectral data are given in Table 8 for the 350–650 nm interval and alternatively in Table 9 for the 350–550 nm interval. This latter interval is here preferred to the 350–500 nm commonly applied for determining Sys. The choice is supported by the need to enforce a more robust fit of the ays(λ) data from the Eastern Mediterranean Sea (EMED) oligotrophic waters most affected by measurement uncertainties (see Sect. 3.2).

The comparison of the mean Adt and Sdt values in Tables 6 and 7 for the diverse marine regions exhibits equivalent results regardless of the fitting interval (i.e., the values show maximum differences generally within 0.001 m⁻¹ for the same region), which suggests minor impact of residual pigment absorption in adt(λ) fit. Still, the slightly lower mean residuals Rdt characterizing some the marine regions and increasing with the range of the adt(λ) values, indicate better fitting when restricting the analysis to the combined 400–412 & 480–620 & 710–730 nm intervals.

As expected, the Adt and Sdt values in Tables 6 and 7 well compare with those presented in Zibordi and Berthon (2024). This confirms the appropriateness of the solution applied to construct the multispectral data from the hyperspectral ones. Nevertheless, the mean Rdt values shown in Tables 6 and 7 are much higher than from those previously determined for the multispectral data (see Table 6 in Zibordi and Berthon, 2024). These differences are explained by the exponential fit applied for the determination of the multispectral data minimizing intra-band noise and consequently increasing fitting performance.

The comparison of the mean Ays and Sys in Tables 8 and 9 shows decreased values of the first and often slightly increased values of the latter, when restricting the fit to the 350–550 nm spectral interval with respect to 350–650 nm. The differences with the mean Sys determined with the multispectral values in the 412–665 nm interval (see Table 7 in Zibordi and Berthon, 2024) are explained by the lack of ays(λ) values below 412 nm.

Equivalent to Rdt, the mean Rys across the diverse marine regions naturally increases with the range of the ays(λ) values and exhibit appreciable lower values for the fitting restricted to 350–550 nm with respect to 350–650 nm. Still, residuals Rys in Table 9 would exceed those in Table 8 when quantified for the same spectral interval 350–650 nm. As per adt(λ), the much lower mean residuals Rys given in Zibordi and Berthon (2024) with respect to those shown in Tables 8 and 9, are explained by the method applied for the construction of the ays(λ) multispectral data to minimize intra-band noise.

Ternary plots of ays, adt and aph at 442 nm as a percent of the total absorption (i.e., ays(442)+adt(442)+aph(442)) are shown in Fig. 4. Consistent with the equivalent plots presented in Zibordi and Berthon (2024), very few cases suggest dominance of absorption by particles with aph and adt values clustering near the upper and lower right vertices, respectively. Indeed, the majority of cases show predominance of absorption by CDOM with ays values clustered nearby the lower left vertex. While this is naturally expected for the Black Sea (BLKS) optically complex waters and for all the Baltic Sea (BLTS) highly absorbing ones, peculiar is the pronounced clustering observed for the Eastern Mediterranean Sea (EMED) oligotrophic waters, and also for some of the Western Mediterranean Sea (WMED) oligotrophic/mesotrophic ones. This outcome for either EMED and WMED waters, conflicts with the Case-1 water classification. Still, equivalent findings were reached by Pérez et al. (2016), further confirming the presence of a significant CDOM background in the Mediterranean Sea waters

Minor differences between Fig. 4 and the equivalent one published in Zibordi and Berthon (2024) are explained by the diverse number of spectra (see Sect. 4 and compare the values of N provided in Table 7 of Zibordi and Berthon (2024) with those in Tables 6–9 of this work).

As an additional analysis, scatter plots of aph(442) versus aph(676) in m⁻¹ are displayed in Fig. 5 to address the impact of regional pigments. The corresponding correlation coefficients (not shown) are within 0.96–0.99, thus indicating consistent linear relationships. The average of the aph(442)/aph(676) ratios, as expressed by the values of μ, decreases from oligotrophic (i.e., EMED and WMED) to more eutrophic waters (e.g., NORS and BLTS), with the exception of the high value observed for the Black Sea (BLKS). This is explained by a higher mean cell size of phytoplankton in eutrophic environments (Margalef, 1978; Chisholm, 1992), and consequently by an increased packaging effect in conjunction with a lower proportion of photoprotective pigments (Bricaud et al., 1995; Ciotti et al., 2002), which lead to lower aph(442)/aph(676) ratios with respect to oligotrophic environments (Ciotti et al., 2002; Babin et al., 2003).

A comparison of the present dataset with published data for diverse geographical areas is a subtle task which encompasses most of the difficulties intrinsic of assembling measurements from different sources. Nevertheless, such a comparison is attempted for some marine regions. For example, regarding the average aph around 440 nm, the gradient observed between the oligotrophic Western Mediterranean Sea (WMED) and, the eutrophic Baltic (BLTS) and North (NORS) seas (0.02±0.02 to 0.25±0.20 m⁻¹, respectively, where the complementary ± value refers to 1 standard deviation) is consistent with that provided in Babin et al. (2003; 0.01–0.1 to 0.1–0.9 m⁻¹, respectively). The intermediate values reported for the Adriatic Sea (0.05±0.03 m⁻¹) are in the lower side of the range of values observed by Babin et al. (2003; 0.10–0.50 m⁻¹). The average aph(440) determined for the Greenland Sea (GRLS; 0.05±0.03 m⁻¹), is comparable to that observed by Kowalczuk et al. (2019) in the Arctic (0.03–0.09 m⁻¹). Finally, some poor correspondence is observed for the Black Sea (BLKS) between Churilova et al. (2025; 0.01–0.13 m⁻¹) and the present study (0.11±0.17 m⁻¹), still explained by differences in the sampled regions of the same basin.

As additional comparisons, the slopes characterizing the exponential decrease of adt and ays with wavelength are analysed and results presented in Tables 10 and 11 for Sdt and Sys, respectively. A general agreement among studies is evident for both Sdt and Sys, although the spectral fitting intervals applied in the various studies might differ. These agreements also confirm that Sys and Sdt show values in the range of approximately 0.016–0.022 m⁻¹ and 0.010–0.013 m⁻¹, respectively, across marine regions.

A more detailed comparison is considered worthless. In fact the variability due to the sampling in diverse sub-regions and seasons, combined with the use of diverse instrumentation, sampling and analysis protocols, may largely mark differences in the observed natural variability of individual quantities. This is a major reason for the recent attempt to impose standardization of measurement protocols and processing schemes, or at least the provision of related meta-data information for measurements resulting from multiple sources (Werdell et al., 2003). One important feature of the CoASTS-BiOMaP dataset is thus the adoption of identical and consolidated instrumentation, sampling and analysis protocols and also processing solutions applied across different times and geographical regions.

Interested researchers can download the CoASTS-BiOMaP data set at 10.1594/PANGAEA.993510 (Berthon and Zibordi, 2026).

The CoASTS and BiOMaP measurement programs, established by the JRC Marine Optical Laboratory in collaboration with various European institutions, from 1995 up to 2022 produced a time-series and geographically distributed bio-optical data at the AAOT site in the northern Adriatic Sea and in European Seas, respectively. The measurements from the two programs included the same quantities rigorously obtained by applying standardized instruments, community measurement methods, extended quality control schemes and consolidated processing.

Complementary to a previous work that introduced the CoASTS-BiOMaP dataset comprising the near surface multispectral bio-optical data (see Zibordi and Berthon, 2024), this work describes the CoASTS and BiOMaP dataset of hyperspectral absorption coefficients aph(λ) and adt(λ) by pigmented and non-pigmented particles, respectively, as well as those by colored dissolved organic matter ays(λ), obtained applying spectrophotometric measurements to field samples.

The slopes Sdt determined from the exponential fit of adt(λ) in the 400–730 nm interval exhibit mean values varying from 0.009 nm⁻¹ for the Eastern Mediterranean Sea up to 0.013 for the North Sea. These values do not significantly vary when restricting the fit to the combined 400–412 & 480–620 & 710–730 nm intervals minimizing the potential impact of residual pigments absorption in adt(λ).

The slopes Sys determined with ays(λ) in the 350–650 nm interval show mean values ranging from 0.015 nm⁻¹ for the Eastern Mediterranean Sea up to 0.021 nm⁻¹ for the Baltic Sea. These values slightly increase when restricting the fit to the 350–550 nm interval as a result of an higher weight of the short wavelength values.

An analysis focussed on aph(λ) shows aph(442)/aph(676) ratios decreasing from oligotrophic to eutrophic waters. This is explained by an increasing mean cell size of phytoplankton toward eutrophic waters.

This unique dataset has relevance for bio-optical investigations supporting the exploitation of advanced hyperspectral satellite ocean color sensor data.