NOAA MSL12 multi-sensor DINEOF global gap-filled products: Chlorophyll-a, diffuse attenuation coefficient Kd(490), and suspended particulate matter (SPM)

The NOAA Multi-Sensor Level-1 to Level-2 (MSL12) Ocean Color, science quality, multi-sensor global gap-filled analysis includes chlorophyll-a, Kd(490), and SPM products. These global gap-free data are generated using the data interpolating empirical orthogonal function (DINEOF) method (Liu and Wang, 2022). The data that go into this product currently come from 3 instruments: the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite, VIIRS on the NOAA-20 satellite, plus the Ocean and Land Colour Instrument (OLCI) on the Sentinel 3A satellite from the Copernicus program of the European Union.

    Data Access
    Global Map projection displaying chlorophyll-a concentration

    Ocean Color satellite sensors measure visible light, at specific wavelengths, that leaves the surface of the ocean and arrives at the top of the atmosphere where the sensor (satellite platform) is located. From these visible spectral radiance measurements, along with simultaneous radiance measurements at the near-infrared (NIR) and the shortwave infrared (SWIR) wavelengths, the color of the ocean, or normalized water-leaving radiances (nLw(őĽ)), can be calculated. Then, the¬†nLw(őĽ) spectra are used to derive other ocean properties such as the concentration of chlorophyll-a (chlor-a or Chl-a) (Wang and Son, 2016), which is the green pigment responsible for photosynthesis and therefore an indicator of the amount of phytoplankton biomass in the ocean water, the diffuse attenuation coefficient at 490 nm¬†Kd(490) (Wang et al., 2009), and water suspended particulate matter (SPM) (Wei et al., 2021).¬†Kd(490) and SPM can be related to water turbidity and clarity.

    The NOAA Multi-Sensor Level-1 to Level-2 (MSL12) Ocean Color, science quality, multi-sensor global gap-filled analysis includes chlorophyll-a, Kd(490), and SPM products. These global gap-free data are generated using the data interpolating empirical orthogonal function (DINEOF) method (Liu and Wang, 2022). The data that go into this product currently come from 3 instruments: the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite, VIIRS aboard the NOAA-20 satellite, plus the Ocean and Land Colour Instrument (OLCI) on the Sentinel 3A satellite from the Copernicus program of the European Union. NOAA gets the OLCI Level-1B data from EUMETSAT, and ocean color data at NOAA are processed using the same NOAA MSL12 developed by the NOAA/STAR Ocean Color Team (Wang et al., 2013).

    Mikelsons and Wang¬†(2019)¬†explains the approach to the¬†Level-3, multi-sensor merged, global daily data product served by CoastWatch¬†at ~4 km spatial resolution in NetCDF format. SNPP and NOAA-20 operate along the same Sun-synchronous polar orbit that crosses the equator at about 13:30 local time‚ÄĒboth satellites travel around Earth from pole to pole in such a way that they observe the same areas at about the same time of day, no matter the season. There is about a 50-minute delay between the paths of NOAA-20 and SNPP, so NOAA-20‚Äôs path runs between two adjacent SNPP orbital paths and vice versa. Thus, the overlap of the spatial coverages in the two VIIRS sensors automatically fills each instrument‚Äôs data gaps (Mikelsons and Wang, 2019). In addition, ocean color data from the VIIRS SNPP and NOAA-20 have the same spatial and temporal resolution, and these data are processed using the same algorithm and software package (i.e., MSL12). Therefore, the statistics of their ocean color products are very similar, and the data can be merged into a global 9-kilometer resolution data set directly without adjustment (Liu and Wang, 2019). These multi-sensor daily merged products are derived from MSL12 for VIIRS SNPP and NOAA-20, as well as OLCI-Sentinel-3A (Liu and Wang, 2022).¬†

    Note that proper adjustments are applied to Chl-a, Kd(490), and SPM algorithms so that consistent ocean biological and biogeochemical products can be derived from the three satellite sensors (VIIRS-SNPP, VIIRS-NOAA-20, and OLCI-Sentinel-3A) (Wang et al., 2020).

    The EOS article by Liu and Wang (2019) explains the approach to the Level-4, multi-sensor, gap-filled analysis for the earlier, 2-sensor product (VIIRS SNPP plus VIIRS NOAA-20; also available on CoastWatch). "Even after the datasets from the two satellites are merged, some gaps remain. To complete the picture, the gap-filling application uses a mathematical technique based on the data interpolating empirical orthogonal function (DINEOF) (Alvera-Azcarate et al., 2005; Beckers and Rixen, 2003). This technique exploits the coherency over location and time of the data from the two satellites to infer a value at the missing location." CoastWatch serves this daily global gap-filled product at ~9 km spatial resolution in NetCDF. Go to the data access tab on this page for access.

    Please read Liu and Wang (2022) for details about the global gap-free ocean color products of Chl-a, Kd(490), and SPM derived from multi-satellite measurements of VIIRS-SNPP, VIIRS-NOAA-20, and OLCI-Sentinel-3A.

    Temporal Start Date
    February 9, 2018
    Temporal Coverage

    Daily

    Product Families
    Ocean Color
    Measurements
    Chlorophyll-a Concentration
    Diffuse Attenuation Coefficients
    Suspended Particulate Matter
    Processing Levels
    Level 4
    Latency Groups
    24+ hours (Delayed)
    Latency Details

    Daily, ~24h to 48h

    Spatial Resolution Groups
    2km+
    Spatial Resolution Details

    9km

    Data Providers
    NOAA
    NESDIS
    STAR
    CoastWatch
    Spatial Coverage

    Global

    Description

    180W - 180E
    90N - 90S

    MSL12

    Description

    Multi-Sensor Level-1 to Level-2 (MSL12) Ocean Color Data Processing System

    NOAA

    Description

    National Oceanic and Atmospheric Administration - 17 / 18 / 19 / 20 / 21

    Platform Type
    Low Earth Orbit Satellite (LEO)
    Instruments
    Organizations
    NASA
    NOAA
    Orbital Altitude
    834 km
    Equatorial Crossing Times
    13:25 asc

    Sentinel-3

    Description

    Sentinel - 3A / 3B

    Platform Type
    Low Earth Orbit Satellite (LEO)
    Instruments
    Organizations
    ESA
    EUMETSAT
    Copernicus
    Orbital Altitude
    814.5 km
    Orbital Period
    101 minutes
    Orbital Inclination
    98.65¬į
    Equatorial Crossing Times
    10:00 desc

    SNPP

    Description

    Suomi National Polar-orbiting Partnership

    Platform Type
    Low Earth Orbit Satellite (LEO)
    Instruments
    Organizations
    NASA
    NOAA
    Orbital Altitude
    833 km
    Equatorial Crossing Times
    13:25 asc

    OLCI

    Description

    Ocean and Land Colour Instrument

    Platforms
    Instrument Types
    Imager
    Organizations
    ESA

    VIIRS

    Description

    Visible Infrared Imaging Radiometer Suite

    Platforms
    Instrument Types
    Imager
    Organizations
    NASA

    Liu, X. and M. Wang, "Global daily gap-free ocean color products from multi-satellite measurements", Int. J. Appl. Earth Obs. Geoinf., 108, 102714 (2022). doi:10.1016/j.jag.2022.102714

    Wei, J., M. Wang, L. Jiang, X. Yu, K. Mikelsons, and F. Shen, "Global estimation of suspended particulate matter from satellite ocean color imagery", Journal of Geophysical Research: Oceans, 126, e2021JC017303 (2021). doi:10.1029/2021JC017303

    Liu, X. and M. Wang, "Filling the gaps in ocean maps", EOS, 100 (2019). doi:10.1029/2019EO136548

    Liu, X., and M. Wang (2019), Filling the gaps of missing data in the merged VIIRS SNPP/NOAA-20 ocean color product using the DINEOF method, Remote Sens., 11, 178, https://doi.org/10.3390/rs11020178.

    Mikelsons, K., and M. Wang (2019), Optimal satellite orbit configuration for global ocean color product coverage, Opt. Express, 27, A445‚ÄďA457,¬†https://doi.org/10.1364/OE.27.00A445

    Mikelsons, K., and M. Wang (2018), Interactive online maps make satellite ocean data accessible, Eos Trans. AGU, 99, https://doi.org/10.1029/2018EO096563.

    Liu, X. and M. Wang, "Gap filling of missing data for VIIRS global ocean color product using the DINEOF method", IEEE Trans. Geosci. Remote Sens., 56, 4464-4476 (2018). doi:10.1109/TGRS.2018.2820423

    Wang, M. and S. Son, "VIIRS-derived chlorophyll-a using the ocean color index method", Remote Sens. Environ., 182, 141-149 (2016). doi:10.1016/j.rse.2016.05.001

    Wang, M., et al. (2013), Impact of VIIRS SDR performance on ocean color products, J. Geophys. Res. Atmos., 118, 10,347‚Äď10,360,¬†https://doi.org/10.1002/jgrd.50793.

    Wang, M., S. Son, and L. W. Harding Jr., ‚ÄúRetrieval of diffuse attenuation coefficient in the Chesapeake Bay and turbid ocean regions for satellite ocean color applications,‚ÄĚ J. Geophys. Res., 114, C10011,¬†https://doi.org/10.1029/2009JC005286, 2009.

    Wang, M., L. Jiang, S. Son, X. Liu, and K. J. Voss, ‚ÄúDeriving consistent ocean biological and biogeochemical products from multiple satellite ocean color sensors,‚ÄĚ Opt. Express, 28, 2661‚Äď2682, 2020.¬†https://doi.org/10.1364/OE.376238

    Alvera-Azcarate, A., et al. (2005), Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions: Application to the Adriatic Sea, Ocean Model., 9, 325‚Äď346,¬†https://doi.org/10.1016/j.ocemod.2004.08.001.

    Beckers, J., and M. Rixen (2003), EOF calculations and data filling from incomplete oceanographic data sets, J. Atmos. Oceanic Technol., 20, 1,839‚Äď1,856,¬†https://doi.org/10.1175/1520-0426(2003)020<1839:ECADFF>2.0.CO;2.

    Algorithm Theoretical Basis Document (ATBD)

    Wang, M., X. Liu, L. Jiang and S. Son, "The VIIRS Ocean Color Products", Algorithm Theoretical Basis Document Version 1.0, 68 pp., June 2017.

    For more MSL12 processing documentation, please go to documentation tab here.

    ** When you use our data, please reference the product citation (if available) and acknowledge "NOAA CoastWatch" **