Ocean Color Reprocessing 2022.0


Reprocessing 2022.0 is a multi-mission reprocessing to incorporate updates in instrument calibration, vicarious calibration, new ancillary sources and algorithm improvements. The affected missions are CZCS, SeaWiFS, OCTS, MERIS, MODIS on Aqua and Terra, VIIRS on SNPP and NOAA-20, OLCI on Sentinel-3A and Sentinel-3B, GOCI, and HICO

Instrument Calibration

In this reprocessing, the instrument calibration will be updated for the MODIS and VIIRS instruments to utilize the additional on-board (solar/lunar) calibration measurements collected since the R2018.0 reprocessings, and to incorporate advancements in modeling of sensor radiometric degradation from the on-board calibrators.

The MERIS data are now based on ESA's 4th reprocessing

The OLCI Sentinel-3A data are based on ESA's processing baseline collection 002 and OLCI Sentinel-3B on ESA's processing baseline collection 001.

The updates for the on-orbit calibration for SNPP-VIIRS include:

  • an extension of lunar/solar time-series with new observations
  • a revised model for fitting lunar time-series
  • temporal gain adjustments for impact of modulated RSRs on ocean/atmosphere signal
  • updated relative detector corrections to reduce striping for bands M1-M7

Vicarious Calibration

For all missions where it is possible, a vicarious calibration to the Marine Optical Buoy (MOBY) is used to minimize residual error in instrument absolute calibration and systematic bias in the atmsopheric correction algrothm.

All NASA ocean color missions since SeaWiFS utilize a vicarious calibration to the Marine Optical Buoy (MOBY) to minimize residual error in instrument absolute calibration and systematic bias in the atmospheric correction algorithm. As part of the R2018 reprocessing, the MOBY time-series was also reprocessed to incorporate an updated protocol for calculating the sea-air transmission coefficient that is used to calculate water leaving radiances (Lw) from MOBY measurements.

Historical data processing protocols recommended assuming a constant transmission coefficient for calculating water leaving radiances (Lw) from underwater measurements of upwelling radiance (Lu0-). The constant value 0.543 had been an accepted simplification, calculated using inputs of 500 nm, 25 C, and 35 ppt. However, using a fixed value regardless of wavelength resulted in a small spectral bias across Lw spectra, causing MOBY data to be approximately 1.3% too high at 380 nm, decreasing inversely with wavelength to be approximately 1.1% too low at 700 nm. Note that this was a general data processing issue, not a problem with MOBY.

This issue and its specific impacts on MOBY data were described by Voss and Flora (2017), and the refined advice for calculating the sea-air transmission coefficient was also included in the most recent update to community-based AOP protocols (Zibordi et al. 2019). Based on those recommendations, the MOBY time-series was reprocessed. The R2018 reprocessing included an updated sea-air transmission coefficient calculation that incorporated wavelength-specific inputs as well as temperature measurements to remove the described bias and improve the accuracy of satellite retrievals through the updated vicarious calibration.

Radiometric biases for several sensors (e.g. OLCI, VIIRS-NOAA20) relative to the long time series sensors remained following vicarious calibration to MOBY. As we were unable to resolve these, a model-based vicarious calibration using chlorophyll climatology (following Werdell et al. 2007, Applied Optics) is used for those sensors. Details on this approach will be provided.

Product and Algorithm Changes


The ocean color chlorophyll algorithm (OCx) coefficients were updated to match those provided in O'Reilly and Werdell (2019).

Particulate Inorganic Carbon

Several improvements to the particulate inorganic carbon algorithm were made, including:
  • The three-band algorithm (Gordon et al., 2001) is now being activated properly in high turbidity features
  • In the two-band algorithm (Balch et al., 2005), a variable backscattering cross section (bb*) model applicable to diverse coccolithophore assemblages has been implemented. Previously, a constant bb* value which is applicable to the ubiquitous species, Emiliana huxleyii was used.
    The variable bb* value is based on results from the field that show a strong relationship between high bb* values in regions of low coccolithophore abundance and lower bb* values in regions of high coccolithophore abundance.
    The net effect of this change is that in coccolithophore-poor regions, the PIC concentrations will now be a little lower and in coccolithophore-rich regions (within blooms, for example), the concentrations will now be a little higher. The patterns of PIC generally remain unchanged.
  • High light (HILT) values in the three-band code are now flagged as "not a valid value"
  • The minimum acceptable PIC value was lowered from 1.18e-5 to 1e-5 mol (m^-3) based on new field observations
  • The minimum acceptable three-band retrieval was lowered from 0.003 to 0.0005 mol(m^-3) based on new field observations

Inherent Optical Properties

Raman Scattering

A correction to the remote sensing reflectances based on an estimate of Raman scattering (McKinna et al., 2014) based on Westberry et al., (2013) is applied when computing the inherent optical properties.

Absorption and Backscattering spectra for Water

A failure to exclude blank lines in the read routine for the static absoprtion and scattering coefficients for water combined with two erroneously uncommented lines that were introduced in the header of the ASCII "water_spectra.dat" file when it was updated with the R2014 reprocessing to use the backscattering coefficients for pure seawater developed by Zhang et al. (2009) resulted in an unintended 3nm spectral shift these data. To correct this issue, the a netCDF version of the water spectra data was created and the reader updated to support this format.

Ancillary Data Sources

The NCEP-based meteorological and OMI-TOMS/TOVS/TOAST ozone ancillary sources are replaced by corresponding fields obtained from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) produced by NASA Goddard's Global Modeling and Assimilation Office (GMAO). The data are available on a delay from real-time. For the near real-time processing, the meteorological and ozone data will be derived from the GMAO GEOS-IT system which uses the same model and assimilation sources, but in a forward stream processing.

The NOAA OI SST V2 sea surface temperature ancillary data are replaced with the the GHRSST Level 4 CMC (Canadian Meterological Centre) Global Foundation Sea Surface Temperature product. The GHRSST CMC product has been used for SST processing since the SST R2019 reprocessing

A modified version of the GEBCO_2020 Grid is now used as the land/water mask and surface elevation inputs to l2gen. The GEBCO_2020 Grid is a global bathymetric product that provides global coverage of elevation data in meters on a 15 arc-second grid. It replaces the previously used bathymetery (ETOPO1_ocssw.nc), land mask (landmask_GMT15ARC.nc) and water mask (watermask.dat) files. The modifications to the original GEBCO_2020 Grid include the addition of a binary water mask and a water surface height array used for large lakes above sea level with bathymetry recorded in the GEBCO data set.

Multi-scattering Epsilon

The aerosol model and optical properties determination is done in multiple scattering space. It is based on the Ahmad et al. (2010) aerosol models as outlined in Ahmad and Franz (2018)

Along with the modifications to the aerosol tables to support the multi-scattering epsilon approach, the range of aerosol optical thicknesses covered by the tables was increased and the spacing of the tabular AOT values in the table was modified to improve the interpolation of the table and mitigate extrapolations beyond the table values.

Straylight Masking

The straylight flag for MODIS and VIIRS is a simple dilation of the CLDICE and HILT flags. For MODIS, this is a 7x5 diamond-shaped kernel (7 pixesl by 5 lines centered on the flagged pixel). A similar kernel is used for VIIRS, but one that varies in size based on the VIIRS data aggregation zone. Following the recommendation of Hu et al., (2019), the kernel has been reduced to a 3x3 diamond-kernel.

Product Formats

No format changes will be included in this reprocessing. The file formats and product suites are identical to those emplyed in the R2014.0 multi-mission reprocessing.
The only exception is for the MERIS data, as those were not reprocessed with the R2014 configuration. The MERIS data formats will now be consistent with the other supported missions.


Comparison of results relative previous reprocessing and relative to in situ measurements (where available) will be provided for each sensor as each is reprocessed.


Ahmad, Ziauddin, Bryan A. Franz, Charles R. McClain, Ewa J. Kwiatkowska, Jeremy Werdell, Eric P. Shettle, and Brent N. Holben. 2010. “New Aerosol Models for the Retrieval of Aerosol Optical Thickness and Normalized Water-Leaving Radiances from the SeaWiFS and MODIS Sensors over Coastal Regions and Open Oceans.” Applied Optics 49 (29): 5545. https://doi.org/10.1364/AO.49.005545.

Ahmad, Ziauddin, and Bryan A. Franz (2018), Uncertainty in aerosol model characterization and its impact on ocean color retrievals, in PACE Technical Report Series, Volume 6: Data Product Requirements and Error Budgets (NASA/TM-2018 – 2018-219027/ Vol. 6), edited by I. Cetinić, C. R. McClain and P. J. Werdell, NASA Goddard Space Flight Space Center Greenbelt, MD.

Balch, W. M., et al. (2005). "Calcium carbonate budgets in the surface global ocean based on MODIS data." Journal of Geophysical Research Oceans 110(C7): C07001, doi:07010.01029/02004JC002560.

GEBCO Compilation Group (2020) GEBCO 2020 Grid (doi:10.5285/a29c5465-b138-234d-e053-6c86abc040b9)

Gordon, H. R., et al. (2001). "Retrieval of coccolithophore calcite concentration from SeaWiFS imagery." Geochemical Research Letters 28(8): 1587-1590.

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.

McKinna, L.I.W., Werdell, P.J. and C.W. Proctor. 2016. "Implementation of an analytical Raman scattering correction for satellite ocean-color processing." Optics Express, 24(14), A1123-A1137, doi: 10.1364/OE.24.0A1123

O'Reilly, J.E., and Werdell, P. J. (2019). Chlorophyll algorithms for ocean color sensors - OC4, OC5 & OC6, Remote Sensing of Environment 229: 32-47, https://doi.org/10.1016/j.rse.2019.04.021.

Voss, K. J., & Flora, S. (2017). Spectral dependence of the seawater-air radiance transmission coefficient. Journal of Atmospheric and Oceanic Technology, 34(6), 1203-1205. https://doi.org/10.1175/JTECH-D-17-0040.1

Westberry et al. (2013) Influence if Raman scattering on ocean color inversion models, Applied Optics, 52(22), 5552-5561, https://doi.org/10.1364/AO.52.005552

Zibordi, G., Voss, K. J., Johnson, B. C., & Mueller, J. L. (2019). Protocols for Satellite Ocean Colour Data Validation: In Situ Optical Radiometry. IOCCG Ocean Optics and Biogeochemistry Protocols for Satellite Ocean Colour Sensor Validation, Volume 3.0, IOCCG, Dartmouth, NS, Canada. http://dx.doi.org/10.25607/OBP-691