Level 2 VIIRS Ocean Color produced by NOAA/STAR Ocean Color Team through NOAA Multi-Sensor Level 1 to Level 2 processing system (MSL12) using the Ocean Color improved satellite data record (OC-SDR, which is Level 1b).

The current VIIRS science quality collection, released in CoastWatch as of 07 August 2017, is produced from MSL12 v1.2* using OC-SDR v04.

Ocean Color satellite sensors measure visible light at specific wavelengths which leaves the surface of the ocean and arrives at the top of the atmosphere where the sensor is located. nLw, can be calculated. nLws are used to derive other ocean properties such as the concentration of chlorophyll-a (chlor-a, chlora, or sometimes chl , which is the green pigment responsible for photosynthesis and therefore and indicator of the amount of phytoplankton biomass in the ocean water) and the coefficients for attenuation of downwelling irradiance (Kd(PAR) and Kd(490) which are related to water clarity).

The ocean color datasets described here are from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor aboard the Suomi-NPP satellite (SNPP) which was launched in November 2011. The VIIRS SNPP ocean color science quality collection differ in several ways from the near real-time products (Table 1).

*Note that the metadata in the NetCDF files show v1.20 from the beginning of the collection (2 Jan. 2012) up tthrough 24 April 2017 and v1.21 from 25 April 2017 forward. This version change did not affect retrieval values for the standard products served by CoastWatch.

Table 1. Comparison of primary processing differences for VIIRS SNPP near real time versus science quality ocean color data.
Parameter Near real-time Science Quality
Latency ~12 hours (best effort) Delayed 15 days
Sensor Data Record (SDR) IDPS Operational SDR NOAA/STAR
Ocean Color improved SDR
Ancillary Data Predicted Assimilated
Spatial Coverage May have gaps Complete

Standard VIIRS SNPP ocean color data Level 2 products from MSL12 v1.2(both near real-time and science quality) include:

  • Normalized water-leaving (nLw) radiance at 6 visible bands (nominal center wavelengths)
    • M1 (410nm)
    • M2 (443nm)
    • M3 (486nm)
    • M4 (551nm)
    • M5 (671nm)
    • I1 (638 nm)
  • Chlorophyll-a concentration
  • Diffuse attenuation coefficient at 490 nm (Kd(490)), and
  • Diffuse attenuation coefficient of photosynthetically active radiation (Kd(PAR))
  • QA Score (a quantitative assessment of VIIRS ocean color performance with respect to an empirical catalog of in situ spectra, Wei et al., 2016; see reference tab)

See Table 2 for quick reference matrix of additional science quality VIIRS ocean color products. The Ocean Color Granule Selector is a tool to interactively preview, choose and download L2 granule files. Data files are in NetCDF4/CF but differ from past NOAA CoastWatch products in their use of hierarchical groups to store attributes, variables, and dimensions. CoastWatch Utilities Software has been updated to work with these files. Additional metadata can be obtained using THREDDS services such as ISO or OPeNDAP.

Table 2. Quick reference matrix of science quality VIIRS ocean color products available from CoastWatch.
Product Description Processing Level Nominal Spatial Resolution Chl-a nLws Kd(PAR) Kd(490) QA Score
Daily granule global swath @750 m L2 750 m X X X X X
Daily merged global sectorized * L3 750 m in progress at CoastWatch in progress at CoastWatch in progress at CoastWatch in progress at CoastWatch not currently available
7-day merged global sectorized * L3 750 m in progress at CoastWatch in progress at CoastWatch in progress at CoastWatch in progress at CoastWatch not currently available
True monthly merged global sectorized * L3 750 m in progress at CoastWatch in progress at CoastWatch in progress at CoastWatch in progress at CoastWatch not currently available
Daily merged global single file L3 4 km X X X X X
7-day merged global single file L3 4 km X X X X X
Monthly merged global single file L3 4 km X X X X X

*See Figure 1 for description and identification of sectors.

Level-2 science quality ocean color products are organized by Year and Day-of-the-Year. Filenames will appear as follows: where:

V: Sensor VIIRS
2017: Year of observation (YYYY)
187: Day of year for the observation (DDD)
003435: Hour, minutes, and seconds of the start of observation (hhmmss) in UTC
NPP: Spacecraft S-NPP
SCINIR: Sensor data record source and environmental data record algorithm.
Indicates the standard Science Quality output for distribution
L2: Processing level. L2 includes calibrated and geolocated geophysical products

Map of sector tiles of global VIIRS data
Figure 1. Twenty-four sectors identified for file naming convention. Sectors enable downloads of select subset regions from global high resolution VIIRS ocean color science quality data.


Key Description
Platform/Sensor Morning (1030): S-NPP / VIIRS
Measurement/Products Normalized water-leaving radiance (nLw) at 5 wavelengths (410, 443, 486, 551, 671 nm) Chlorophyll-a, Kd(490), Kd(PAR) and NOAA unique products (products tailored to support NOAA missions)
DOI n/a
Sample Filename
Dataset Type n/a
Processing Level L2 (MSL12) and L3
Spatial Coverage Global (single file, reduced resolution) and Sectorized Global (full resolution)
Temporal Coverage 02 January 2011 through 15 days before present (15 day delay on Science Quality)
Resolution 750m @ Nadir; ~1.7km at swath edge full resolution in L2 granules and L3 mapped to 24 sector files (see description tab). And 4 km L3 reduced resolution global mapped in one file.
Projection Satellite native swath granule (L2) and Mapped: WGS84 Mercator, Albers EA, Geographic (L3)
Latency ~12-24 h
Swath Width ~3060km
Sample Frequency daily
Temporal Repeat Orbital 16 days
Orbital Period 101 Minutes
Orbit Sun-synchronous, altitude 824km, inclination 98.74
Data Provider Creator: NOAA STAR
Release Place: College Park, MD, USA
Keywords NOAA, MSL12, VIIRS , near real-time, science quality, chlorophyll, water-leaving radiance, diffuse attenuation coefficient, L2
Formats NetCDF , PNG, TIFF, HDF

Data Access

Data are available through the following servers:
Service Resource Locator

Daily, global, Level 2 granule/swath data access from granule selector tool:


Daily, global, Level 2 granule/swath (nominal 750 m)

Global, Level 3 merged single file, ~4 km


Top Level of Science Quality, Life of Mission THREDDS catalog

Daily, global, Level 2 granule/swath (nominal 750 m)THREDDS Catalog

Global, Level 3 merged single file, ~4 km

Global, Level 3 merged sectorized files, ~750 m (see sector map under description tab)*

*Note, these are currently in process for MSL12 v1.2 and are filling in as completed.

[Please acknowledge "NOAA CoastWatch/OceanWatch" when you use data from our site and cite the particular dataset DOI as appropriate.]


    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.

  • Sun, J., X. Xiong, E. Waluschka, and M. Wang, 2016, "Suomi National Polar- Orbitting Partnership Visible Infrared Imaging Radiometer Suite polarization sensitivity analysis", Appl. Opt., 55, 7645-7658 (2016). doi:10.1364/AO.55.007645
  • Wang, M. and S. Son, 2016, "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
  • Sun, J. and M. Wang, 2016, "VIIRS reflective solar bands calibration progress and its impact on ocean color products", Remote Sens., 8, 194 (2016). doi:10.3390/rs8030194
  • Sun, J., M. Chu, and M. Wang, 2016, "Degradation nonuniformity in the solar diffuser bidiretional reflectance distribution function", Appl. Opt ., 55, 6001-6016 (2016). doi:10.1364/AO.55.006001
  • Wang, M., 2016, "Rayleigh radiance computations for satellite remote sensing: accounting for the effect of sensor spectral response function", Opt. Express, 24, 12414-12429 (2016). doi:10.1364/OE.24.012414
  • Wang, M., P. Naik, and S. Son, 2016, "Out-of-band effects of satellite ocean color sensors", Appl. Opt ., 55, 2312-2323 (2016). doi:10.1364/AO.55.002312
  • Wang, M., W. Shi, L. Jiang, and K. Voss, 2016, "NIR- and SWIR-based on-orbit vicarious calibrations for satellite ocean color sensors", Opt. Express, 24, 20437-20453 (2016). doi:10.1364/OE.24.020437
  • Sun, J. and M. Wang, 2015, "On-orbit calibration of Visible Infrared Imaging Radiometer Suite reflective solar bands and its challenges using a solar diffuser", Appl. Opt ., 54, 7210-7223 (2015). doi:10.1364/AO.54.007210
  • Sun, J. and M. Wang, 2015, "On-orbit characterization of the VIIRS solar diffuser and solar diffuser screen", Appl. Opt ., 54, 236-252 (2015). doi:10.1364/AO.54.000236
  • Sun, J. and M. Wang, 2015, "Radiometric calibration of the Visible Infrared Imaging Radiometer Suite reflective solar bands with robust characterizations and hybrid calibration coefficients", Appl. Opt ., 54, 9331-9342 (2015). doi:10.1364/AO.54.009331
  • Son, S. and M. Wang, 2015, "Diffuse attenuation coefficient of the photosynthetically available radiation Kd(PAR) for global open ocean and coastal waters", Remote Sens. Environ., 159, 250-258 (2015). doi:10.1016/j.rse.2014.12.011
  • Wang, M., W. Shi, L. Jiang, X. Liu, S. Son, and K. Voss, 2015, "Technique for monitoring performance of VIIRS reflective solar bands for ocean color data processing", Opt. Express, 23, 14446-14460 (2015). doi:10.1364/OE.23.014446
  • Xiong, X., J. Sun, J. Fulbright, Z. Wang, and J. Butler, 2015, "Lunar calibration and performance for S-NPP VIIRS reflective solar bands", IEEE Trans. Geosci . Remote Sens., 54, 1052-1061 (2015). doi:10.1109/TGRS.2015.2473665
  • Sun, J. and M. Wang, 2014, "Visible Infrared Imaging Radiometer Suite solar diffuser calibration and its challenges using solar diffuser stability monitor", Appl. Opt ., 53, 8571-8584 (2014). doi:10.1364/AO.53.008571
  • Mikelsons, K., M. Wang, L. Jiang, and M. Bouali, 2014, "Destriping algorithm for improved satellite-derived ocean color product imagery", Opt . Express, 22, 28058-28070 (2014). doi:10.1364/OE.22.028058
  • Sun, J., M. Wang, L. Tan, and L. Jiang, 2014, "An efficient approach for VIIRS RDR to SDR data processing", IEEE Geosci . Remote Sens. Lett ., 11, 2037-2041 (2014). doi:10.1109/LGRS.2014.2317553
  • Jiang, L. and M. Wang, 2014, "Improved near-infrared ocean reflectance correction algorithm for satellite ocean color data processing", Opt. Express, 22, 21657-21678 (2014). doi:10.1364/OE.22.021657
  • Jiang, L. and M. Wang, 2013, "Identification of pixels with stray light and cloud shadow contaminations in the satellite ocean color data processing", Appl. Opt., 52, 6757-6770 (2013). doi:10.1364/AO.52.006757
  • Wang, M., X. Liu, L. Tan, L. Jiang, S. Son, W. Shi, K. Rausch, and K. Voss, 2013, "Impacts of VIIRS SDR performance on ocean color products", J. Geophys . Res. Atmos., 118, 10347-10360 (2013). doi:10.1002/jgrd.50793

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Updated: 14 September 2017