Chl-a model performance summary

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

Chlorophyll-a (chl-a) satellite model performance varies by region and satellite sensor. For the regions defined in the section below, and the MODIS-Aqua, SeaWiFS, VIIRS-SNPP, or OLCI-A/B sensors, it is recommended to use the latest reprocessing of the data if available (currently R2022.0), along with the following chl-a models:

1. Gulf of Saint Lawrence (GoSL): EOF chl-a
2. Bay of Fundy (BoF): OC_X-SPMCor
   (Not included in this summary, see Wilson et al. 2024 for details)
   
3. Northwest Atlantic (NWA): POLY4 chl-a
   
4. Extended Northeast Pacific (extNEP): POLY4 chl-a

SEE TABS BELOW FOR DATA AVAILABILITY!

Regions of interest

Bounding boxes for each of the regions of interest are defined as follows:

• GoSL: 41-53° N, 49-75° W
  
• BoF: 43.1–46.2° N, 63.1-68.8° W
  
• NWA: 39-82° N, 42-95° W
  
• extNEP: 46-60° N, 122-162° W

More info

Data & model details

Input 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 samples used for validation:
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.

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").

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.

Matchup restrictions

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.

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