Contact: Stephanie.Clay (at) dfo-mpo (dot) gc (dot) ca

Overview


Chlorophyll-a (chl-a) satellite model performance varies by region, satellite sensor, and reprocessing/version. For the regions defined below, and the MODIS-Aqua, SeaWiFS, VIIRS-SNPP, or OLCI-A/B sensors (https://oceandata.sci.gsfc.nasa.gov/) and the OC-CCI multisensor product (https://www.oceancolour.org/), we recommend using the latest reprocessing of the data if available (currently R2022.0, or v6.0 for OC-CCI), along with the following chl-a models:

Region Abbrev Lats Lons Model Comments
Gulf of Saint Lawrence GoSL 45.2-52.2° N 55.6-72° W POLY4 EOF also performs well, but note the underestimated (overestimated) high (low) values. St. Lawrence Estuary omitted from training, values were too high.
Bay of Fundy BoF 43.1–45.8° N 63.54-68.8° W OCX-SPMCor
Northwest Atlantic NWA 39-82° N 42-95° W POLY4
Extended Northeast Pacific extNEP 46-60° N 122-162° W POLY4 Global OCI model also performs well in this region, with a slight underestimation (overestimation) of high (low) values.



If your region of interest extends beyond these boundaries or the data are unavailable, you can use one of NASA’s globally-tuned models (e.g. OCI).

CAUTION: In situ samples used to train POLY4 for the NWA were confined to the Scotian Shelf, southern Labrador Sea, and Grand Banks. As a result, you should use caution with these data in other areas or consider using the default globally-tuned model such as NASA’s OCI, with the exception of Baffin Bay where the NWA POLY4 model was tested further and found to perform well (click below for details).

NWA POLY4 model validation in Baffin Bay


In situ Turner chl-a data from Baffin Bay and some surrounding areas (see map) were later provided by Lisa Matthes (Marine Productivity Laboratory, Freshwater Institute, DFO), collected during KEBABB (Knowledge and Ecosystem Based Approach in Baffin Bay) expeditions from 2019-2024.

Satellite matchups using OC-CCI were evaluated (see plots below) and the performance of the NWA POLY4 model in this region (middle plot) was found to be similar to the areas where the model was trained, with the removal of most of the bias found in OCI (left plot). Retuning the coefficients using only the Baffin Bay matchups resulted in minimal improvement (right plot) and is therefore unnecessary.



Data & model details

Tools

The oceancolouR package contains the functions ocx(), gsm(), and eof_chl() to implement the chl-a models evaluated here. For eof_chl(), a training set is required for the region of interest. Using the R2022.0-reprocessed data, the re-optimized POLY4 and GSMGS are referred to as poly4v2 and gsmgsv2 - e.g. to use the POLY4 coefficients optimized for R2018.0-reprocessed data, you would use get_ocx_coefs(sensor, region, alg="poly4"), replacing sensor with one of modisaqua, seawifs, or viirssnpp, and region with nwa or nep. To use POLY4 with R2022.0-reprocessed data, you would retrieve the coefficients with get_ocx_coefs(sensor, region, alg="poly4v2").


Input satellite data and models

Each chl-a model uses remote-sensing reflectances (Rrs) as input. The wavebands used in the calculation are dependent on sensor and model. The Satellite Ocean colour and Phytoplankton Ecology group (SOPhyE) at the Bedford Institute of Oceanography uses daily 4km-resolution satellite data from NASA OBPG to calculate chl-a using different models for use in ocean observation and analysis. NASA OBPG reprocesses their datasets every few years as models improve, after which SOPhyE downloads the new datasets and re-optimizes the coefficients used in certain models. Information on reprocessing versions can be found here.

OCI is the standard chlor_a product distributed in files from NASA OBPG, which uses the empirical band ratio model OCx (O’Reilly et al 1998) in combination with a blend of the Hu CI algorithm (Hu et al 2012) for concentrations <= 0.35 mg m3. For the sensors of interest, the OCI product is also referred to by the following combination of acronyms:

  • MODIS-Aqua: OC3M, CI
  • SeaWiFS: OC4, CI
  • VIIRS-SNPP: OC3_VIIRS_SNPP, CI
  • OLCI: OC4, CI

POLY4 is a regionally-tuned version of OCx (Clay et al 2019).

OCXSPMCOR is a regionally-tuned version of OCx with an extra term to account for SPM concentration (Wilson et al 2024).

GSM_GS is a regionally-tuned version of the semi-analytical GSM model from Maritorena et al (2002). GS refers to the fact that the g coefficients from the original model are spectrally-dependent in this modification (Clay et al 2019).

EOF is a model that employs Principal Component Analysis, currently in use in the Gulf of Saint Lawrence (Laliberte et al 2018).


In situ data and matchup restrictions

In situ chl-a data from the regions of interest are used to retrain regional models. POLY4 and GSMGS models are both trained using in situ HPLC (High Performance Liquid Chromotography) data. The training set created to calculate EOF chl-a is composed of satellite matchups to in situ chl-a derived from Turner fluorescence, as HPLC data is not available for samples collected in the Gulf of Saint Lawrence.

In situ / satellite matchups used for model training must adhere to the following criteria:

  • In situ sample must be <= 10 metres from the surface

  • Satellite pixel and sample location must be within 10 kilometres of each other

  • The coefficient of variation (standard deviation over the mean) of the OCI chl-a values in the 5x5 pixel box must be <= 0.5

  • R2018.0 matchups only:

    • In the 3x3 pixel box around the matchup satellite pixel, at least 3 pixels must be valid
    • In situ sample and satellite pass must occur on the same calendar day

  • R2022.0 matchups only:

    • In the 5x5 pixel box around the matchup satellite pixel, at least half the non-land pixels must be valid, down to a minimum of 5 valid pixels
    • Time difference between in situ sampling time and satellite pass must be on the same calendar day (see below for details)
    • Solar zenith angle must be <= 75 degrees

Below is a quick comparison of MODIS-Aqua POLY4_v2 satellite chl-a against in situ HPLC chl-a using different restrictions on the difference in time allowed between the in situ sample and satellite pass for a matchup to be used in training and evaluation:

  1. Sample and pass must be within 12 hours and on the same calendar day
  2. Sample and pass must be within 12 hours
  3. Sample and pass must be within 24 hours and on the same calendar day
  4. Sample and pass must be within 24 hours

Using in situ sample/satellite matchups that are on the same calendar day appears to yield the best results, so that is the restriction used in model training.



Matchup stats and data access

Sensor Region Matchup years Model Intercept Slope R2 Num. obs. RMSLE Data access
OC-CCI v6.0 Northwest Atlantic 1999-2023 OCI -0.0616 0.6189 0.6615 1296 0.3070 Contact author for more info
POLY4 0.0000 0.9999 0.6861 1296 0.2894 CIOOS Atlantic ERDDAP
GSMGS -0.0038 1.0051 0.6382 1296 0.3141 Contact author for more info
Gulf of Saint Lawrence 1998-2024 OCI 0.1093 0.7626 0.4587 3620 0.2615 Contact author for more info
POLY4 0.0000 1.0000 0.4706 3620 0.2560 Contact author for more info
GSMGS -0.0063 1.0208 0.0849 3620 0.3887 Contact author for more info
EOF 0.0050 0.7014 0.4920 3601 0.2303 Contact author for more info
Northeast Pacific 2006-2022 OCI -0.0056 0.7799 0.7515 1345 0.2728 Contact author for more info
POLY4 0.0000 1.0000 0.7395 1345 0.2822 Contact author for more info
GSMGS -0.0046 1.0085 0.7152 1345 0.2975 Contact author for more info
Bay of Fundy 1999-2023 OCI 0.2083 0.6430 0.0001 466 0.4841 Contact author for more info
OCXSPMCOR -0.0020 0.9594 0.3225 462 0.3476 CIOOS Atlantic ERDDAP
OLCI-S3A/B R2022.0 Northwest Atlantic 2016-2021 OCI 0.0386 0.6832 0.5064 141 0.3079 NASA OBPG
POLY4 -0.0219 0.9712 0.6117 141 0.2638 Contact author for more info
GSMGS -0.0221 1.0077 0.2193 141 0.4193 Contact author for more info
Gulf of Saint Lawrence 2016-2022 OCI 0.2526 1.3464 0.2734 583 0.4505 NASA OBPG
POLY4 -0.0039 0.9914 0.3331 583 0.2814 Contact author for more info
GSMGS -0.1236 1.6942 0.0043 582 0.5954 Contact author for more info
EOF 0.0004 0.7795 0.5885 430 0.1771 Contact author for more info
Northeast Pacific OCI 0.1065 1.1826 0.4246 564 0.4888 NASA OBPG
POLY4 -0.0050 1.0027 0.5061 564 0.3835 Contact author for more info
GSMGS -0.2678 1.4792 0.1540 564 0.7367 Contact author for more info
MODIS-Aqua R2022.0 Northwest Atlantic 2002-2021 OCI -0.0666 0.6458 0.4402 789 0.3873 NASA OBPG
POLY4 0.0000 1.0000 0.5672 789 0.3590 CIOOS Atlantic ERDDAP
GSMGS 0.0000 1.0000 0.3088 789 0.4816 Contact author for more info
Gulf of Saint Lawrence 2002-2022 OCI 0.2647 1.1857 0.2619 2831 0.4828 NASA OBPG
POLY4 0.3757 1.2647 0.3114 2831 0.5561 Contact author for more info
GSMGS -0.0399 2.5907 0.0323 2831 0.9474 Contact author for more info
EOF 0.0239 0.7432 0.5497 2257 0.2420 CIOOS SLGO
SeaWiFS R2022.0 Northwest Atlantic 2003-2010 OCI -0.0266 0.7129 0.6374 121 0.3502 NASA OBPG
POLY4 0.0000 1.0000 0.6803 121 0.3255 Contact author for more info
GSMGS 0.0000 1.0000 0.3293 121 0.5076 Contact author for more info
Gulf of Saint Lawrence 1998-2010 OCI 0.2577 1.1426 0.3028 1433 0.4689 NASA OBPG
POLY4 0.4037 1.1349 0.3490 1433 0.5529 Contact author for more info
GSMGS 0.1637 -2.6799 0.0043 1433 1.1076 Contact author for more info
EOF 0.0247 0.7454 0.4757 1030 0.2542 CIOOS SLGO
VIIRS-SNPP R2022.0 Northwest Atlantic 2012-2021 OCI -0.0491 0.6530 0.4377 562 0.3658 NASA OBPG
POLY4 0.0000 1.0000 0.5911 562 0.3314 Contact author for more info
GSMGS 0.0000 1.0000 0.1315 562 0.5502 Contact author for more info
Gulf of Saint Lawrence 2012-2022 OCI 0.2054 1.2457 0.2364 1833 0.4711 NASA OBPG
POLY4 0.4173 1.1754 0.3129 1833 0.5670 Contact author for more info
GSMGS -0.0937 -2.7320 0.0002 1830 1.1028 Contact author for more info
EOF 0.0237 0.6710 0.4851 1478 0.2516 CIOOS SLGO



Detailed model comparison


Ocean color (which is used to derive chl-a and other variables) is considered an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). In the tabs below, the density plots that show the percent difference between satellite and in situ chl-a have vertical dashed lines indicating 30%, the recommended maximum uncertainty for this variable.

Warning: For OC-CCI and OLCI-A/B, GoSL has a regionally-tuned version of the POLY4 and GSM_GS models. For other sensors, “POLY4” and “GSM_GS” data within the NWA bounds always refer to the single model tuned using HPLC data across the entire NWA.

OC-CCI v6.0

Northwest Atlantic

Gulf of Saint Lawrence

Northeast Pacific

Bay of Fundy

OLCI-S3A/B R2022.0

Northwest Atlantic

Gulf of Saint Lawrence

Northeast Pacific

MODIS-Aqua R2022.0

Northwest Atlantic

Gulf of Saint Lawrence

SeaWiFS R2022.0

Northwest Atlantic

Gulf of Saint Lawrence

VIIRS-SNPP R2022.0

Northwest Atlantic

Gulf of Saint Lawrence



References


Clay, S.; Pena, A.; DeTracey, B.; Devred, E. Evaluation of Satellite-Based Algorithms to Retrieve Chlorophyll-a Concentration in the Canadian Atlantic and Pacific Oceans. Remote Sens. 2019, 11, 2609.

Hu, Chuanmin & Lee, Zhongping & Franz, Bryan. (2012). Chlorophyll a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference. Journal of Geophysical Research. 117. C01011. 10.1029/2011JC007395.

Hu, C., Feng, L., Lee, Z., Franz, B. A., Bailey, S. W., Werdell, P. J., & Proctor, C. W. (2019). Improving satellite global chlorophyll a data products through algorithm refinement and data recovery. Journal of Geophysical Research: Oceans, 124, 1524– 1543. https://doi.org/10.1029/2019JC014941

Laliberté, Julien & Larouche, Pierre & Devred, Emmanuel & Craig, Susanne. (2018). Chlorophyll-a Concentration Retrieval in the Optically Complex Waters of the St. Lawrence Estuary and Gulf Using Principal Component Analysis. Remote Sensing. 10. 10.3390/rs10020265.

Maritorena, Stephane & Siegel, David & Peterson, Alan. (2002). Optimization of a semianalytical ocean color model for global-scale application. Applied optics. 41. 2705-14. 10.1364/AO.41.002705.

O’Reilly, John & Maritorena, S. & Mitchell, B.G. & Siegel, David & Carder, Kendall & Garver, S.A. & Kahru, Mati & Mcclain, Charles. (1998). Ocean color chlorophyll algorithms for SeaWiFS. Journal of Geophysical Research. 103. 937-953. 10.1029/98JC02160.

Wilson, K.L., Hilborn, A., Clay, S. et al. Improving Satellite Chlorophyll-a Retrieval in the Turbid Waters of the Bay of Fundy, Canada. Estuaries and Coasts (2024). https://doi.org/10.1007/s12237-024-01334-x