NOAA VIIRS Ocean Color Products over Global Open Oceans and Turbid Coastal/Inland Waters
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1 NOAA VIIRS Ocean Color Products over Global Open Oceans and Turbid Coastal/Inland Waters Chris Brown Menghua Wang NOAA/NESDIS/STAR E/RA3, Room 3228, 5830 University Research Ct. College Park, MD 20740, USA IOCCG Meeting; Rome, 6-8 March 2018 Website for VIIRS ocean color images, data and Cal/Val: Acknowledgements: This work was supported by JPSS/VIIRS funding. We thank MOBY team for in situ opccs data, VIIRS Cal/Val PIs and their collaborators in support of VIIRS Cal/ Val accvices. 1
2 VIIRS Ocean Color EDR & Cal/Val Teams EDR Name Organiza9on Funding Agency Task Lead Menghua Wang (OC EDR & Cal/Val Lead), L. Jiang, X. Liu, W. Shi, S. Son, L. Tan, X. Wang, J. Sun, K. Mikelsons, M. Chu, V. Lance, M. Ondrusek, E. Stengel, C. Kovach NOAA/NESDIS/ STAR JPSS/NJO Leads Ocean Color EDR Team & Cal/Val Team OC products, algorithms, SDR, EDR, Cal/Val, vicarious cal., refinements, data processing, reprocessing, algorithm improvements, sotware updates, data validacons and analyses Ocean Color Robert Arnone Sherwin Ladner, Adam Lawson, Jen Bowers U. Southern MS, NRL, QineCQ Corp., SDSU JPSS/NJO Satellite matchup tool (SAVANT) Golden Regions, Cruise parccipacon and support WAVE_CIS (AERONET-OC site) operacon Carol Johnson NIST JPSS/NJO Traceability, AERONET Uncertainty Nicholas Tufillaro, Curt Davis OSU JPSS/NJO Ocean color validacon, Cruise data matchup West Coast Burt Jones, MaNhew Ragan USC JPSS/NJO Eureka (AERONET Site) Alex Gilerson, Sam Ahmed CUNY JPSS/NJO LISCO (AERONET site), Cruise data and matchup Chuanmin Hu USF JPSS/NJO NOAA data concnuity, OC data validacon Ken Voss & MOBY team Miami JPSS/NJO Marine OpCcal Buoy (MOBY) Zhongping Lee, Jianwei Wei UMB JPSS/NJO Ocean color IOP data validacon and evaluacon Ocean color opccs matchup Working with: NOAA CoastWatch, VIIRS SDR team, DPA/DPE, Raytheon, NOAA OC Working Group, NOAA various line-office reps, NOAA NCEI, NOAA OCPOP, IOCCG, NASA, ESA, EUMETSAT, etc. Collaborators: D. Antoine (BOUSSOLE), B. Holben (NASA-GSFC), G. Zibordi (JRC-Italy), R. Frouin (for PAR), and many others. 2
3 VIIRS Spectral Bands for Ocean Color VIIRS on Suomi National Polar-orbiting Partnership (SNPP), Oct. 28, 2011, VIIRS-NOAA-20, Nov. 18, 2017, Joint Polar Satellite System (JPSS)-2 J2, ~2021, and J3 & J4 (up to ~2038) VIIRS (NOAA-20) VIIRS (SNPP) MODIS Ocean Bands (nm) Other Bands (nm) Ocean Bands (nm) Ocean Bands (nm) Ocean Bands (nm) Other Bands (nm) 411 (M1) 642 (I1) (M2) 867 (I2) (M3) 1603 (I3) (M4) SWIR Bands SWIR Bands 667 (M5) 1238 (M8) (M6) 1604 (M10) (M7) 2258 (M11) VIIRS nominal center wavelength VIIRS has ocean spectral bands similar to MODIS VIIRS-NOAA-20---Operational Satellite Ocean Color Sensor! Spatial resolution for VIIRS M-band: 750 m, I-band: 375 m 3
4 VIIRS-SNPP and NOAA-20 Chl-a Images (January 6, 2018) SNPP SNPP & NOAA-20 Merged NOAA-20
5 Impact of Tropical Cyclone Cebile over Indian Ocean
6 NOAA Team Effort: End-to-End Ocean Color Data Processing Ø NOAA Ocean Color Team has been developing/building the capability for the End-to-End satellite ocean color data processing including: Level-0 (or Raw Data Records (RDR)) to Level-1B (or Sensor Data Records (SDR)). Level-1B (SDR) to ocean color Level-2 (Environmental Data Records (EDR) using the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing---measurement-based data processing system. Level-2 to global Level-3 (routine daily, 8-day, monthly, and climatology data/images). Validation of satellite ocean color products (in situ data and data analysis capability). Ø Support of in situ data collections for VIIRS Cal/Val activities, e.g., MOBY, AERONET-OC sites (3 sites operation, added Lake Erie site), NOAA dedicated Cal/Val cruises (2014, 2015, 2016, 2018,) Ø On-orbit instrument calibration (solar and lunar) for ocean color data processing: J. Sun and M. Wang, Radiometric calibration of the VIIRS reflective solar bands with robust characterizations and hybrid calibration coefficients, Appl. Opt., 54, , Ø On-orbit vicarious calibration using MOBY in situ data: M. Wang, W. Shi, L. Jiang, and K. Voss, NIR- and SWIR-based on-orbit vicarious calibrations for satellite ocean color sensors, Opt. Express, 24, , Ø RDR (Level-0) to SDR (Level-1B) data processing (efficient RDR to SDR processing): Sun, J., M. Wang, L. Tan, and L. Jiang, An efficient approach for VIIRS RDR to SDR data processing, IEEE Geosci. Remote Sens. Lett., 11, , Ø Ocean Color Viewer (OCView) Online display and monitoring of ocean color product imagery. Ø Ocean Color Data Analysis and Processing System (OCDAPS) IDL-based VIIRS ocean color data visualization and processing package Wang, X., X. Liu, L. Jiang, M. Wang, and J. Sun, VIIRS ocean color data visualization and processing with IDL-based NOAA-SeaDAS, Proc. SPIE 9261, 8 Nov Ø Work with users to meet their requirements.
7 Measurements done just after Hurricane Matthew in the region October Ondrusek, M., V. P. Lance, M. Wang, E. Stengel, C. Kovach, R. Arnone, S. Ladner, W. Goode, A. Gilerson, S. Ahmed, A. El-Habashi, R. Foster, M. Ottaviani, J. I. Goes, H. Gomes, K. McKee, J. W. Kang, C. Hu, J. Cannizzaro, S. Sun, D. English, B. C. Johnson, Z. P. Lee, L. Zoffoli, J. Lin, N. Tufillaro, I. Lalovic, J. Nahorniak, C. O. Davis, M. Twardowski, N. Stockley, and K. J. Voss, Report for Dedicated JPSS VIIRS Ocean Color Calibration/Validation Cruise October 2016, NOAA Technical Report NESDIS 151, V. P. Lance (ed.), NOAA National Environmental Satellite, Data, and Information Service, Silver Spring, Maryland,
8 Summary of VIIRS Ocean Color EDR Products Inputs: VIIRS M1-M7, I1, and the SWIR M8, M10, and M11 bands SDR data Terrain-corrected geo-location file Ancillary meteorology and ozone data Operational (Standard) Products (9): Normalized water-leaving radiance (nl w s) at VIIRS visible bands M1-M5, and I1 (638 nm) Chlorophyll-a (Chl-a) concentration Diffuse attenuation coefficient for the downwelling spectral irradiance at the wavelength of 490 nm, K d (490) Diffuse attenuation coefficient of the downwelling photosynthetically available radiation (PAR), K d (PAR) QA Score for data quality (nl w (λ) spectra) (Wei et al., 2016) Level-2 quality flags Experimental Products: Inherent Optical Properties (IOP-a, IOP-a ph, IOP-a dg, IOP-b b, IOP-b bp ) at VIIRS M2 or other visible bands (M1-M5) from the Quasi-Analytical Algorithm (QAA) (Lee et al., 2002) Photosynthetically Available Radiation (PAR) (R. Frouin) Chl-a from ocean color index (OCI) method (Hu et al., 2012; Wang and Son, 2016) Others, e.g., user specific products (e.g., Chl-a anomaly and Chl-a anomaly ratio) Ø Data quality of ocean color EDR are extremely sensitive to the SDR quality. It requires ~0.1% data accuracy (degradation, band-to-band accuracy )!
![NOAA/STAR VIIRS Ocean Color MSL12 - Both NRT and mission-long science quality data A]ribute Near-Real Time Science Quality Delayed Mode Processing System: MSL12 MSL12 Best effort, as soon as possible](/docs-images/92/108526458/images/9-5.jpg "Latency: (~6-24h) Best effort, 2 week delay SDR: IDPS OperaConal SDR OC-improved IDPS SDR Ancillary Data: Global Forecast System (predicted) Science Quality (assimilated) SpaCal Coverage: May be gaps")
9 NOAA/STAR VIIRS Ocean Color MSL12 - Both NRT and mission-long science quality data A]ribute Near-Real Time Science Quality Delayed Mode Processing System: MSL12 MSL12 Best effort, as soon as possible Latency: (~6-24h) Best effort, 2 week delay SDR: IDPS OperaConal SDR OC-improved IDPS SDR Ancillary Data: Global Forecast System (predicted) Science Quality (assimilated) SpaCal Coverage: May be gaps due to various issues Complete global coverage Reprocessing: No Yes, ~2-3 years or as needed Menghua Wang
10 VIIRS Climatology Ocean Color Product Image ( )
11 VIIRS Climatology QA Score Image ( ) Atlantic Ocean Med Sea Some Coastal Regions Wei, J., Z. Lee, and S. Shang (2016), A system to measure the data quality of spectral remote-sensing reflectance of aquatic environments, J. Geophys. Res., Oceans, 121, , doi: /2016jc
12 VIIRS Mission-long QA Score (global 8-day mean value) Mean = 0.93 Mean = 0.90 Excellent data quality (nl w (λ) data) over open oceans, and OC data over coastal/inland waters are reasonable (need to be improved) Mean = 0.80 Higher score in winter Blue: Global Oligotrophic Waters Green: Global Deep Waters (depth > 1km) Lower score in summer Red: Regions with depth 1km, e.g., Coastal/inland waters VIIRS science quality OC data are processed using the MSL12!
13 VIIRS-derived Daily Chl-a Green: Global Deep Waters (depth > 1km) Blue: Global Oligotrophic Waters Slight Chl-a drop over oligotrophic waters VIIRS science quality OC data are processed using the MSL12!
14 VIIRS-derived 8-day nl w (551) Green: Global Deep Waters (depth > 1km) Blue: Global Oligotrophic Waters Quite stable in VIIRS-derived nl w (551)! VIIRS science quality OC data are processed using the MSL12!
15 Matchup of AERONET-OC In Situ versus VIIRS OC-SDR NIR USC
16 USC Statistics of VIIRS OC (NIR) vs. In-Situ (AERONET-OC) OC-SDR The NIR Method RATIO (SAT/ENV) DIFFERENCE (SAT-ENV) AVG MED STD No AVG MED STD %Diff nl w (410) nl w (443) nl w (486) nl w (551) nl w (671) nl w All
17 Coastal and Inland Waters Some examples from global coastal and inland waters Turbid Coastal Regions: US East Coast, including the Chesapeake Bay, Outer banks, etc. China East Coast, including the Bohai Sea, Yellow Sea, East China Sea La Plata Estuary Turbid Inland Lakes: Lake Taihu (China) Lake Okeechobee (US) Great Lakes (US)
18 VIIRS (OC-SDR) Climatology Images ( ) K d (490) ( m -1 in log scale) US East Coast NIR SWIR NIR-SWIR
19 VIIRS (OC-SDR) Climatology Images ( ) K d (490) ( m -1 in log scale) NIR SWIR NIR-SWIR China East Coast
20 VIIRS Climatology ( ) using the NIR-SWIR Approach Chl-a K d (490) La Plata nl w (443) log: log: nl w (551) nl w (671) nl w (862)
21 Ocean Color Data Access through NOAA CoastWatch/OceanWatch/PolarWatch Program organization is Hub and Spokes HUB is in College Park, MD (Washington DC area) SPOKES are US Regional Nodes Caribbean/Gulf of Mexico Node and OceanWatch; NOAA/ OAR/AOML; Research; Miami, FL Central Pacific and Pacific OceanWatch NOAA/NMFS/PIFSC; Fisheries; Honolulu, HI East Coast Node NOAA/NOS/NCCOS; Ocean Service; Silver Spring, MD Great Lakes Node; NOAA/ OAR/GLERL; Research, Ann Arbor, MI West Coast Node; NOAA/ NMFS/SWFSC; Fisheries; Monterey, CA * NOAA PolarWatch; NOAA/ NMFS/SWSFC and NOAA/ NESDIS/STAR Direct access via FTP, HTTPS, THREDDS, ERDDAP and/or InteracCve Search Tools: Near Real-Cme Data Search Mapped Products (L3) Granule and Daily InteracCve Granule Display Limited Near Real-Cme and Science Quality (product dependent) L1/L2 products InteracCve map display SpaCal Data Search Limited Near Real-Cme and Science Quality (product dependent) L1/L2 products InteracCve map display
22 Ocean Color Data Access through NOAA CoastWatch/OceanWatch InteracCve Search Tool Examples Granule selector Spatial Search
23 Conclusions For global open ocean, ocean color products can be routinely generated with good data quality. For coastal and inland waters, satellite ocean color data can be derived with reasonable accuracy. However, there are still some outstanding issues, in particular, for cases with strongly-absorbing aerosols, e.g., dust, smoke, pollutions from nearby big cities, etc. For such cases, aerosol vertical profile info is necessary. VIIRS-SNPP global ocean color products have been routinely produced using the NIR-, SWIR-, and NIR-SWIR-based atmospheric correction algorithms, providing necessary satellite data for various applications in open oceans, coastal and inland waters, as well as for further improving data quality. Our evaluation results show that VIIRS-SNPP is now capable of providing high quality global ocean color products in support of science research and operational applications. We are in unprecedented time for satellite ocean color remote sensing, VIIRS-SNPP, VIIRS- NOAA-20, OLCI-Sentinel-3A, SGLI-GCOM-C, OLCI-Sentinel-3B, in VIIRS Images and Cal/Val: VIIRS Ocean Color Data:
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