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

Overview


Chlorophyll-a (chl-a) satellite model performance varies by region and satellite sensor. 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/), it is recommended to use the latest reprocessing/version 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 41-53° N 49-75° 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 43.1–46.2° N 63.1-68.8° W OCX-SPMCor Not included in this summary, see Wilson et al. 2024 for details.
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).

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

  • 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 within 24 hours and 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 within 24 hours of each other on the same calendar day appears to yield the best results, so that is the restriction used in model training.

Disclaimer: The evaluation metrics of the R2018.0 reprocessing here might have slight differences from those presented in Clay et al 2019 due to changes in exact matchup criteria and the order in which the matchups are filtered. The overall message is the same, however, when possible, the latest reprocessing (R2022.0 as of February 2023) should be used.



Matchup stats and data access

Reprocessing Region Sensor Matchup years Model Intercept Slope R2 Num. obs. RMSLE Data access
v6.0 Northwest Atlantic OC-CCI 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 Contact author for more info
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
R2022.0 Northwest Atlantic MODIS-Aqua 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
SeaWiFS 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
VIIRS-SNPP 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
OLCI-S3A and OLCI-S3B 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 MODIS-Aqua 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 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 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
OLCI-S3A and OLCI-S3B 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 2016-2021 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
R2018.0 Northwest Atlantic MODIS-Aqua 2002-2014 OCI -0.0672 0.8386 0.4488 508 0.3714 Contact author for more info
POLY4 0.0000 1.0000 0.5740 508 0.3341 Contact author for more info
GSMGS -0.0150 1.0172 0.5050 469 0.3672 Contact author for more info
SeaWiFS 1999-2010 OCI -0.0351 0.6737 0.5544 336 0.3201 Contact author for more info
POLY4 0.0000 1.0000 0.6216 336 0.3086 Contact author for more info
GSMGS 0.0109 0.9274 0.5804 304 0.3166 Contact author for more info
VIIRS-SNPP 2012-2014 OCI -0.1175 0.7281 0.3790 172 0.3725 Contact author for more info
POLY4 0.0000 1.0000 0.5514 172 0.3279 Contact author for more info
GSMGS 0.0069 1.2068 0.3992 161 0.4475 Contact author for more info
Gulf of Saint Lawrence MODIS-Aqua 2002-2019 OCI 0.1334 1.2088 0.1927 2816 0.4779 Contact author for more info
POLY4 0.2860 1.1972 0.2612 2816 0.5158 Contact author for more info
GSMGS 0.3169 1.1186 0.1897 1904 0.5394 Contact author for more info
EOF 0.0077 0.8045 0.4107 2709 0.3030 Contact author for more info
SeaWiFS 1997-2010 OCI 0.1731 0.8625 0.3828 1294 0.4352 Contact author for more info
POLY4 0.2866 1.0757 0.4110 1294 0.5199 Contact author for more info
GSMGS 0.4607 1.1437 0.3230 1025 0.6811 Contact author for more info
EOF -0.1033 0.8550 0.3920 1221 0.4138 Contact author for more info
VIIRS-SNPP 2012-2019 OCI -0.0111 1.1959 0.1384 1945 0.4574 Contact author for more info
POLY4 0.2122 1.2255 0.2227 1945 0.4809 Contact author for more info
GSMGS 0.1736 0.5528 0.2252 118 0.3570 Contact author for more info
EOF 0.0112 0.7411 0.3109 1808 0.3095 Contact author for more info
Northeast Pacific MODIS-Aqua 2007-2016 OCI 0.0215 0.9655 0.5946 461 0.3678 Contact author for more info
POLY4 0.0000 1.0000 0.6666 461 0.3342 Contact author for more info
GSMGS 0.0356 1.1767 0.6196 387 0.3949 Contact author for more info
SeaWiFS 2006-2010 OCI -0.0421 0.8508 0.6283 40 0.3017 Contact author for more info
POLY4 0.0000 1.0000 0.7507 40 0.2515 Contact author for more info
GSMGS -0.0387 0.9041 0.7658 38 0.2375 Contact author for more info
VIIRS-SNPP 2012-2016 OCI -0.0417 0.9296 0.6273 332 0.3411 Contact author for more info
POLY4 0.0000 1.0000 0.6891 332 0.3150 Contact author for more info
GSMGS -0.0085 1.1313 0.5812 289 0.3965 Contact author for more info



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, the “POLY4” and “GSM_GS” used in the GoSL are actually the versions tuned to the NWA.

v6.0

Northwest Atlantic

OC-CCI

Gulf of Saint Lawrence

OC-CCI

Northeast Pacific

OC-CCI

R2022.0

Northwest Atlantic

MODIS-Aqua

SeaWiFS

VIIRS-SNPP

OLCI-S3A and OLCI-S3B

Gulf of Saint Lawrence

MODIS-Aqua

SeaWiFS

VIIRS-SNPP

OLCI-S3A and OLCI-S3B

Northeast Pacific

OLCI-S3A and OLCI-S3B

R2018.0

Northwest Atlantic

MODIS-Aqua

SeaWiFS

VIIRS-SNPP

Gulf of Saint Lawrence

MODIS-Aqua

SeaWiFS

VIIRS-SNPP

Northeast Pacific

MODIS-Aqua

SeaWiFS

VIIRS-SNPP



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