the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Aug 2026
Coastal North America In Situ Chlorophyll and Optical Dataset
Abstract. Chlorophyll is a crucial climate variable and remotely sensed satellite chlorophyll estimates are essential to understanding large-scale biological processes. Current ocean color chlorophyll algorithms, however, are less accurate in optically complex inland and coastal waters. As a result, remote sensing algorithms are not able to fully resolve chlorophyll concentrations in optically complex coastal regions leading to greater uncertainty in regions of high productivity. This can further lead to errors in regional biogeochemical models and hinder fisheries and ecosystem monitoring and research. In order to develop more accurate coastal chlorophyll retrievals, in situ chlorophyll measurements are needed for algorithm calibration and validation. Here the methods used to develop a single cohesive standardized in situ chlorophyll, chromophoric dissolved organic matter (CDOM), and remote sensing reflectance (Rrs) dataset from multiple public data repositories are described. The final product is a North American dataset of quality controlled chlorophyll measurements and associated metadata. The dataset includes more than 700,000 chlorophyll values with coincidental CDOM and Rrs data when available. Methodological flags such as chlorophyll measurement method, triplicate samples, and in vitro/in vivo flags distinguish between data collection methods. The dataset includes regularly formatted metadata across all sources to ensure due credit to the programs collecting the samples. The resulting dataset is then matched to satellite chlorophyll data to examine the difference in chlorophyll retrievals across multiple water types and regions. This dataset is currently being used to validate a new coastal chlorophyll algorithm in development and to assess chlorophyll retrievals. All methods and data are publicly available at doi.org/10.5281/zenodo.20140178 (Milton et al., 2026).
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RC1: 'Comment on essd-2026-599', Emmanuel Boss, 07 Sep 2026
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AC1: 'Reply on RC1', Gianna Milton, 22 Oct 2026
Thank you for your thoughtful comments and helpful suggestions. The co-authors and I addressed your concerns and edited the manuscript accordingly.
- Regarding the units of CDOM, the definition for CDOM in this paper is based on the data repositories' definitions. SeaBASS defines CDOM as: Concentration of chromophoric dissolved organic matter (units = mg m^-3), as does BCO-DMO. We excluded any absorption of CDOM and only included fluorometric CDOM with units of mg m^-3. To clear up the confusion, we are changing the acronym from CDOM to FDOM (fluorometric CDOM) throughout the manuscript.
- We agree that the validation exercise with OC-CCI is not the core of the manuscript, and therefore removed this analysis. We did, however, retain the optical water type analysis because it provides additional context about the optical properties of coastal waters. An inversion comparison with local Rrs is beyond the scope of this paper.
- Thank you for pointing out this nuance. We updated “Chlorophyll” to “Phytoplankton” in the abstract accordingly (line 13).
- We agree that the “North American” shapefile was incomplete. We updated the shapefile used to now include Central America and all the islands in the Gulf for a more well-defined North American region (line 96-97). Additionally, we’ve added relevant data from the datasets in those missing regions to the dataset (Table 2). We’ve removed the shapefile plot (Figure 1) in response to another reviewer, but Figure 2 shows all data gathered in new areas.
- Thank you for mentioning the missing SeaBASS dataset. We added the Arctic SeaBASS data that was not in Version 1 of the datasets to the updated Version 2 (Table 2 and Figure 1). All figures have been updated accordingly with this additional data. We did not, however, include the additional suggested datasets. This project focuses on collecting data from curated/aggregated data repositories. Searching for individual datasets in various research data repositories is beyond the scope of this project.
Citation: https://doi.org/10.5194/essd-2026-599-AC1
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AC1: 'Reply on RC1', Gianna Milton, 22 Oct 2026
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RC2: 'Comment on essd-2026-599', Qianguo Xing, 15 Sep 2026
The datasets of Chl-a, CDOM, and Rrs are of great significance for the accurate retrieval of Chl-a in global coastal waters. Although this work provides a set of relevant datasets for the North American coastal zone, this dataset boasts rich and highly variable characteristics, making it of considerable value for the retrieval of water properties in coastal waters.
It is suggested that the figures and tables in this manuscript can be further improved. E.g., Fig. 1 can be removed, as other figures including Figs. 3, 4, 5, 6 and so on are sufficient to demonstrate the coverage and scope of the datasets. Some figures can be merged into one figure.
It is also recommended to further visualize the intrinsic properties of the dataset, such as the spatiotemporal distribution maps of Chl and CDOM, as well as the spectral features and variability of Rrs. A reference example of this reasonable presentation can be found in Fig.2 of the published paper: Inverting the Concentrations of Chlorophyll-a and Chemical Oxygen Demand in Urban River Networks Using Normalized Hyperspectral Data https://www.mdpi.com/1424-8220/25/22/7004
The poor consistency presented in Fig.14 indicates that the current dataset is not applicable for Chl-a retrieval under the OC-CCI framework, which may produce misleading results. Instead, this dataset can deliver greater practical value when applied to Chl-a and/or CDOM retrieval tasks for the hyperspectral PACE mission.
Additionally, the caption of Fig.14 needs to be corrected.
It is speculated that the data points with Chl-a values of 0 μg/L and 1157.1 μg/L may be incorrectly collected. The extremely high value of 1157.1 μg/L is very likely associated with floating algal aggregations to which the Chl-a algos are not applicable. Moreover, there may be no 1 km × 1 km valid pixel that can match those high Chl-a values, i.e., values larger than 50/100 μg/L.The abbreviations Rrs, rrs, CDOM, cdom, chl and so on, should be kept consistent throughout the full text, in line with the standardized expression conventions of oceab color.
Citation: https://doi.org/10.5194/essd-2026-599-RC2 -
AC2: 'Reply on RC2', Gianna Milton, 22 Oct 2026
Thank you for your helpful comments and suggestions. The team and I discussed your concerns and edited the manuscript accordingly.
- We agree with your suggestion to revise and combine some of the figures. We removed Figure 1 and merged Figures 3, 5, and 6 into a single figure.
- We appreciate your suggestion to create a figure to better visualize the intrinsic properties of chlorophyll. Due to the number of datapoints, with some areas having thousands of points over multiple years, it is not possible to create a single plot that clearly shows the broad range in concentrations over time and space. We did, however, add a figure showing the spectral features of Rrs vs chlorophyll as you suggested. (Line 255, Figure 3)
- We agree with your assessment of the OC-CCI results. In response to this and other reviewer comments about the relevance of OC-CCI matchups, we decided to remove the OC-CCI chlorophyll analysis from the manuscript. We did, however, retain the optical water type analysis because it provides additional context about the optical properties of coastal waters.
- Thank you for noting the incorrect figure caption, the caption was corrected to “Extracted in situ chlorophyll vs OC-CCI chlorophyll grouped by Optical Water Type, where the top row is Case 1 waters and the bottom row is Case 2 waters, colored by distance from shore (km). The red line is the 1:1 reference line.” (line 331)
- We agree that the extremely high chlorophyll values (up to 1157 mg m^-3) are suspicious. We removed all chlorophyll measurements above 200 mg m^-3 from the dataset. For chlorophyll values reported as 0.0, it is unknown whether these values are true zeros below the detection limit of sensors or an error in the original database. We added text that they should be used with caution (line 216-218), and they are flagged in the dataset accordingly.
- Thank you for mentioning the inconsistent abbreviations. We updated the abbreviations throughout the manuscript.
Citation: https://doi.org/10.5194/essd-2026-599-AC2
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AC2: 'Reply on RC2', Gianna Milton, 22 Oct 2026
Data sets
Coastal North America In Situ Chlorophyll and Optical Dataset Gianna Milton, Kimberly Hyde, Ivona Cetinic, Maxwell Beal, Ryan Vandermeulen, Haley Synan https://doi.org/10.5281/zenodo.20140178
Interactive computing environment
Coastal Chlorophyll Gianna Milton https://nefsc.github.io/READ-EDAB-GMilton-CoastalChlorophyll/intro.html
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Review of “Coastal North America In Situ Chlorophyll and Optical Dataset” by Milton et al.
Reviewer: Emmanuel Boss, UMaine.
This paper generate a new compilation dataset for coastal North America which include in-situ chlorophyll, colored dissolved organic material (CDOM) and remote sensing reflectance (Rrs) the latter two only if available). The initiative is important as it could be used to validate and design algorithms to obtain chlorophyll and CDOM from Rrs.
The paper is well written and of interest to ESSD. HOWEVER, it has some major problems that need to be addressed before publication. I do realize that this paper has already been a huge effort to execute.
There are also Arctic data at:
Massicotte, P, Amiraux, R, Amyot, MP, Archambault, P, Ardyna, M, Arnaud, L, Artigue, L, Aubry, C, Ayotte, P, Bécu, G, Bélanger, S, Benner, R, Bittig, HC, Bricaud, A, Brossier, É, Bruyant, F, Chauvaud, L, Christiansen-Stowe, D, Claustre, H, Cornet-Barthaux, V, Coupel, P, Cox, C, Delaforge, A, Dezutter, T, Dimier, C, Domine, F, Dufour, F, Dufresne, C, Dumont, D, Ehn, J, Else, B, Ferland, J, Forget, MH, Fortier, L, Galí, M, Galindo, V, Gallinari, M, Garcia, N, Gérikas Ribeiro, C, Gourdal, M, Gourvil, P, Goyens, C, Grondin, PL, Guillot, P, Guilmette, C, Houssais, MN, Joux, F, Lacour, L, Lacour, T, Lafond, A, Lagunas, J, Lalande, C, Laliberté, J, Lambert-Girard, S, Larivière, J, Lavaud, J, LeBaron, A, Leblanc, K, Le Gall, F, Legras, J, Lemire, M, Levasseur, M, Leymarie, E, Leynaert, A, Lopes dos Santos, A, Lourenço, A, Mah, D, Marec, C, Marie, D, Martin, N, Marty, C, Marty, S, Massé, G, Matsuoka, A, Matthes, L, Moriceau, B, Muller, PE, Mundy, CJ, Neukermans, G, Oziel, L, Panagiotopoulos, C, Pangrazi, JJ, Picard, G, Picheral, M, Pinczon du Sel, F, Pogorzelec, N, Probert, I, Quéguiner, B, Raimbault, P, Ras, J, Rehm, E, Reimer, E, Rontani, JF, Rysgaard, S, Saint-Béat, B, Sampei, M, Sansoulet, J, Schmechtig, C, Schmidt, S, Sempéré, R, Sévigny, C, Shen, Y, Tragin, M, Tremblay, JÉ, Vaulot, D, Verin, G, Vivier, F, Vladoiu, A, Whitehead, J, Babin, M. 2020. Green Edge ice camp campaigns: understanding the processes controlling the under-ice Arctic phytoplankton spring bloom. Earth Syst Sci Data 12(1): 151-176. doi:10.5194/essd-12-151-2020.
Massicotte, P, Amon, RMW, Antoine, D, Archambault, P, Balzano, S, Belanger, S, Benner, R, Boeuf, D, Bricaud, A, Bruyant, F, Chaillou, G, Chami, M, Charriere, B, Chen, J, Claustre, H, Coupel, P, Delsaut, N, Doxaran, D, Ehn, J, Fichot, C, Forget, MH, Fu, PQ, Gagnon, J, Garcia, N, Gasser, B, Ghiglione, JF, Gorsky, G, Gosselin, M, Gourvil, P, Gratton, Y, Guillot, P, Heipieper, HJ, Heussner, S, Hooker, SB, Huot, Y, Jeanthon, C, Jeffrey, W, Joux, F, Kawamura, K, Lansard, B, Leymarie, E, Link, H, Lovejoy, C, Marec, C, Marie, D, Martin, J, Martin, J, Masse, G, Matsuoka, A, McKague, V, Mignot, A, Miller, WL, Miquel, JC, Mucci, A, Ono, K, Ortega-Retuerta, E, Panagiotopoulos, C, Papakyriakou, T, Picheral, M, Prieur, L, Raimbault, P, Ras, J, Reynolds, RA, Rochon, A, Rontani, JF, Schmechtig, C, Schmidt, S, Sempere, R, Shen, Y, Song, GS, Stramski, D, Tachibana, E, Thirouard, A, Tolosa, I, Tremblay, JE, Vaitilingom, M, Vaulot, D, Vaultier, F, Volkman, JK, Xie, HX, Zheng, GM, Babin, M. 2021. The MALINA oceanographic expedition: how do changes in ice cover, permafrost and UV radiation impact biodiversity and biogeochemical fluxes in the Arctic Ocean? Earth System Science Data 13(4): 1561-1592. doi:10.5194/essd-13-1561-2021.
Dear authors. I am often wrong. If you find my comments off the mark feel free to contact me and if convinced, I would be more than happy to change them.