VIIRS Ocean Color multi-sensor gap-filled analysis (Level 4) is produced with input from the VIIRS multi-sensor (SNPP + NOAA-20) daily merged chlorophyll and monthly climatology using the DINEOF method of interpolation for gap-filling. The chlorophyll algorithm used is OCI. The NOAA ocean color science team provides the gap-filled data file to NOAA CoastWatch. CoastWatch converts these to the NetCDF product and serves them.
Ocean Color Level 4 VIIRS multi-sensor (SNPP + NOAA-20), daily, global, gap-filled analysis chlorophyll-a is produced through NOAA Multi-Sensor Level 1 to Level 2 processing system (MSL12). A 6-month animation of the Level-4 analysis can be seen here.
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. From these visible spectral measurements, along with simultaneous measurements in the near infrared (NIR) and the short wave infrared (SWIR) wavelengths, the color of the ocean, or normalized water leaving radiances (nLw), can be calculated. Then, the nLws are used to derive other ocean properties such as the concentration of chlorophyll-a (chlor-a or sometimes chl, which is the green pigment responsible for photosynthesis and therefore an indicator of the amount of phytoplankton biomass in the ocean water) and the coefficient of extinction for downwelling irradiance (Kd(PAR) and Kd(490) which are related to water clarity).
The ocean color datasets described here are from the Visible and Infrared Imaging Radiometer Suite (VIIRS) sensor aboard the Suomi-NPP satellite (SNPP) which was launched in November 2011 and also aboard the NOAA-20 satellite launched in November 2017. NOAA ocean color data are processed using NOAA Multi-Sensor Level 1 to Level 2 processing system (MSL12) developed by the NOAA/STAR Ocean Color Team [Wang et al., 2013]. Both near real-time and delayed (2 weeks), science quality products are available.
An excerpt from the EOS article by Mikelsons et al., 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—both 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’s 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’s 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 v1.3 for both SNPP plus NOAA-20.
The EOS article goes on to explain the approach to the Level-4, multi-sensor, gap-filled analysis [Mikelsons et al., 2019]. "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.
Daily, with 2-week delay
24+ hours (Delayed)
Daily, with 2-week delay
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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–A457, 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, 10,347–10,360, https://doi.org/10.1002/jgrd.50793.
C. Hu, Z. Lee, B.A. Franz, (2012) "Chlorophyll a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference" Journal of Geophysical Research, 117 (2012) (C01011, doi: 01010.01029/02011JC007395
Alvera-Azcarate, A., et al. (2005), Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions: Application to the Adriatic Sea, Ocean Model., 9, 325–346, 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, 1,839–1,856, 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.