7. Requirements for future ocean color
satellite sensors
Menghua Wang, IOCCG Lecture Series 2014--Atmospheric Correction
Significant efforts are needed for
improvements of water color products in the
inland & coastal regions:
Turbid Waters
(violation of the NIR black ocean assumption)
Strongly-Absorbing Aerosols
(violation of non- or weakly absorbing aerosols)
!  Requires the shortwave infrared (SWIR) bands
with high SNR performance
!  Requires UV bands for detecting absorbing aerosols
Menghua Wang, IOCCG Lecture Series 2014--Atmospheric Correction
7. Sensor requirements
Table 2.1. The logical or ideal sequence of steps for determining sensor spectral coverage and
performance requirements.
Requirements Flow Steps 1. Science objectives & questions 2. Products & product accuracy requirements 3. Algorithms & spectral band selection 4. Bio-­‐optical algorithm accuracy requirements 5. Lwn or Rrs accuracy requirements 6. TOA radiance accuracy requirements 7. Single set of “most stringent” spectral accuracy requirements 8. Sensor spectral calibration & character-­‐
ization requirements & test specifications Description Define what science i ssues the mission is to address, e.g., global carbon budget, coastal zone management. Outline what derived products are needed to a ddress the science q uestions, e.g., net primary production, total suspended matter. Identify the bio-­‐optical algorithms to b e used to derive th required products and what spectral bands are needed fo each algorithm. The atmospheric correction bands are identified at this step. Determine the accuracy of the bio-­‐optical algorithms needed to address the science questions. Based on the algorithm accuracy r equirement, q uantify t spectral Lwn or Rrs accuracy r equired (assumes a “perfe bio-­‐optical algorithm. By propagating the Lw’s to the top of the atmosphere usi the typical atmospheric parameter values, e.g., aerosol optical thickness, determine an acceptable partitioning o bio-­‐optical and atmospheric correction algorithm uncertainties to arrive at a top-­‐of-­‐atmosphere r adiance uncertainty b udget that will achieve the Lwn or Rrs spec
accuracy requirements. Because different bio-­‐optical products require various spectral accuracies, synthesize one set of spectral accuracies that satisfies all product accuracy r equiremen Based on the TOA spectral radiance accuracy requiremen specify the sensor calibration accuracies f or the various sensor sensitivity parameters, e.g., r adiometric linearity, polarization, temperature, out-­‐of-­‐band r esponse. Menghua Wang, IOCCG Lecture Series 2014--Atmospheric Correction
IOCCG Report (2012)
Menghua Wang, IOCCG Lecture Series 2014--Atmospheric Correction
IOCCG Report (2012)
Produc t s Normalized water-­‐leaving radiances or remote sensing r eflectances Chlorophyll-­‐a Diffuse attenuation Inherent optical properties Particulate inorganic carbon Particulate organic carbon Primary production Colored dissolved organic matter Yellow substance Bleached particle absorption Photosynthetically available radiation Fluorescence line height Euphotic depth Total suspended matter Trichodesmium concentration Particle size distribution Dissolved organic matter/carbon Phytoplankton physiological properties (C:Chl, f luorescence yield, growth r ate, etc.) Other phytoplankton pigments (e.g., chlorophyll-­‐b and –c, phycoerythrin) Atmospheric corrections & m asks: Aerosol properties (type, AOT etc..) Cirrus detection Water vapor corrections Spectral Bands/Considerations Specific wavelength + atmospheric correction bands 360, 385, 400, 412, 443, 425, 490, 510 (Chl sensitive) 555, 565 (baseline/non-­‐Chl sensitive) 670, 710, 748 (highly turbid waters) 490, 555 for Kd(490) Spectral inversion algorithms (inversions i mprove as spectral inputs increase) 443,555,670,765,865 443, 490, 555 Derived from other derived products (Chl-­‐a, SST, bp, etc.), algorithm dependent 350-­‐555 nm, spectral inversion algorithms (inversions improve as spectral inputs i ncrease) Multiple wavelengths from 400 – 700nm 667, 678, 710, 748 Derived using I OP or CHL based algorithms 412, 443, 555, 617, 640 1020 (high concentrations) Neural network algorithm: multiple b ands (412-­‐709, excluding fluorescence band) 495, 545, 625 IOP derived 350-­‐555 nm (regional algorithms) Algorithms to b e determined in the future. Additional spectral bands (Table 5) not a nticipated. Chlorophyll-­‐b: 655 Carotenods: 470 Phycoerythrin: 490, 550 Phycocyanin: 620 TBD (depends on how the classification scheme, e.g., size classes, specific phytoplankton groups like diatoms, cyanobacteria, coccolithophores, dinoflagellates, etc.) 710 350, 748, 865, 1020, 1245, 1640, 2130 (atmospheric correction bands; water type dependent) 1375 820 Menghua Wang, IOCCG Lecture Series 2014--Atmospheric Correction
IOCCG Report (2012)
Table 4.1 also provides the typical radiances Ltyp, the required bandwidth, as well as the required SNR. The Ltyp at the wavelengths
common to the SeaWiFS and MODIS sensors were derived from actual experience with those sensors (MODIS values were
scaled to the SeaWiFS values). The Ltyp of the remaining bands were calculated using the Thuillier (2003) solar irradiance (F0) and
an inter- or extrapolation of the Ltyp/F0 ratios of the SeaWiFS/MODIS bands.
350 360 385 412 425 443 460 475 490 510 532 555 583 617 640 655 665 678 710 748 765 820 865 1245 1640 2135 15 15 15 15 15 15 15 15 15 15 15 15 15 15 10 15 10 10 15 10 40 15 40 20 40 50 Ltyp 74.6 72.2 61.1 78.6 69.5 70.2 68.3 61.9 53.1 45.8 39.2 33.9 28.1 21.9 19.0 16.7 16.0 14.5 11.9 9.3 8.3 5.9 4.5 0.88 0.29 0.08 Lmax 356 376 381 602 585 664 724 722 686 663 651 643 624 582 564 535 536 519 489 447 430 393 333 158 82 22 Lmin 50 42 32 28 19 10 3.8 2.2 0.2 0.08 0.02 Menghua Wang, IOCCG Lecture Series 2014--Atmospheric Correction
Lhigh 125 101 78 66 52 38 19 16 5 2 0.8 SNR-­ spec 300 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1000 1400 1000 600 600 600 600 250 180 100 L unit: W/m2 um str
IOCCG Report (2012)
NASA Pre Aerosol, Cloud, and Ecosystems Mission (PACE)
Menghua Wang, IOCCG Lecture Series 2014--Atmospheric Correction
NASA PACE SDT Report (2012)
8. Some Recent Results---VIIRS Products
Website for VIIRS ocean color data and Cal/Val:
http://www.star.nesdis.noaa.gov/sod/mecb/color/
Menghua Wang, IOCCG Lecture Series 2014--Atmospheric Correction
The Ocean Color and Other Useful Spectral Bands for
VIIRS, MODIS, and SeaWiFS
VIIRS!
MODIS
SeaWiFS
Ocean Bands
(nm)
Other Bands
(nm)
Ocean Bands
(nm)
Other Bands
(nm)
Ocean Band
(nm)
410 (M1)
443 (M2)
486 (M3)
—
551 (M4)
671 (M5)
745 (M6)
862 (M7)
640 (I1)
865 (I2)
1610 (I3)
412
443
488
531
551
667
748
869
645
859
469
555
SWIR Bands
1240
1640
2130
412
443
490
510
555
670
765
865
SWIR Bands
1238 (M8)
1610 (M10)
2250 (M11)
VIIRS has similar SWIR bands as MODIS
!VIIRS
nominal center wavelength
Spatial resolution for VIIRS M-band: 750 m, I-band: 375 m
Menghua Wang, IOCCG Lecture Series 2014--Atmospheric Correction
VIIRS Climatology Chlorophyll-a Image
(April 2012 to December 2013)
Log scale: 0.01 to 64 mg/m3
Generated using NOAA-MSL12 for VIIRS ocean color data processing
Menghua Wang, NOAA/NESDIS/STAR
10
VIIRS Climatology Kd(490) Image
(April 2012 to December 2013)
LogLog
scale:
scale:
0.010.01
to 64
to mg/m
2 m-13
Generated using NOAA-MSL12 for VIIRS ocean color data processing
Menghua Wang, NOAA/NESDIS/STAR
11
VIIRS Ocean Color EDR Monitoring Sites
1. MOBY Site; 2. South Pacific Gyre; 3. Chesapeake Bay; 4. US East Coast; 5. AERONETOC CSI Site; 6. AERONET-OC LISCO Site; 7. AERONET-OC USC Site.
Website:
http://www.star.nesdis.noaa.gov/sod/mecb/color/
12
Comparison of VIIRS NOAAMSL12 results with MOBY in
situ data.
Bad calibration
Note:
Vicarious calibration gains
applied since May 2012.
Gains derived using MOBY
data.
VIIRS ocean color products
reached Beta status in January
2013, and plan to reach
Provisional status in March
2014.
13
AERONET-CSI nLw Time Series
In Situ
VIIRS (NOAA-MSL12)
14
AERONET-CSI
(Gulf of Mexico)
nLw(λ) scatter plot
15
Performance in Coastal and Inland Waters (1)
(US East Coast—October 2013 Monthly)
NOAA Operational IDPS
MODIS-NASA/OBPG
MSL12-NIR
MSL12-SWIR
16
Performance in Coastal and Inland Waters (2)
(China East Coast—October 2013 Monthly)
IDPS
MODIS-NASA/
OBPG
MSL12-NIR
MSL12-SWIR
17
The Existing VIIRS Calibration Issue
MODIS-­‐Aqua global oligotrophic water Chl-­‐a from 2002 to 2013 (green), overploHed with VIIRS data from 2012 to 2013 (red) MODIS-­‐Aqua VIIRS (NOAA-­‐MSL12) •  VIIRS and MODIS-­‐Aqua match each other quite well in 2012. •  They have noMceable difference in 2013 (biased low from VIIRS). •  Since MODIS-­‐Aqua has a reasonable Chl-­‐a annual repeatability, It is confirmed that VIIRS SDR has calibraMon issues, in parMcular, for the M4 (551 nm) band (biased low), at least for 2013. Geostationary Ocean Color Imager (GOCI)
"  GOCI was successfully launched on board of the Korean
Geostationary Satellite, Communication, Ocean and Meteorological
Satellite (COMS), with other two payloads (Meteorological Imager
and Ka-band satellite communication) on June 27, 2010. "  GOCI is the first ocean color sensor in Geostationary orbit.
"  Korea Ocean Satellite Center (KOSC) of the Korea Institute of
Ocean Science & Technology (KIOST) has been responsible for
initial test and following operational mission such as Cal/Val,
algorithm development, and data distribution.
Menghua Wang, NOAA/NESDIS/STAR
GOCI Sensor Information
Al,tude 35,857 km Sensor type Staring-­‐frame capture Spa,al resolu,on 500 m Temporal resolu,on 1 hour (8 images per day 9AM-­‐4PM in local Mme) Spectral range 6 visible and 2 NIR bands, 412, 443, 490, 555, 660, 680, 745, 865 nm, but no SWIR bands Coverage Menghua Wang, NOAA/NESDIS/STAR
Local area around Korean peninsular (2500 × 2500 km2 in 16 slots) Geosta,onary Ocean Color Imager (GOCI) Kd(490) (3-­‐25-­‐2012) 09:00
10:00
11:00
12:00
14:00
15:00
16:00
Box1 13:00
Diurnal Changes (Box1) Bohai Sea
Menghua Wang, IOCCG Lecture Series 2014--Atmospheric Correction
Derived from
NOAA-MSL12
Wang et al. (2012; 2013)
21
GOCI NOAA-MSL12 Kd(490) (2012-03-25)
Menghua Wang, IOCCG Lecture Series 2014--Atmospheric Correction
9. Summary
Menghua Wang, IOCCG Lecture Series 2014--Atmospheric Correction
Ocean Color Remote Sensing
Sensor-Measured
“Green” ocean
Blue ocean
Atmospheric Correction (removing >90% sensor-measured signals)
Calibration (0.5% error in TOA >>>> 5% in surface)
From H. Gordon
Menghua Wang, IOCCG Lecture Series 2014--Atmospheric Correction
Summary
#  At satellite altitude, ~90% of sensor-measured signal over ocean comes from the
atmosphere and ocean surface. Thus, it is crucial to have accurate atmospheric
correction and sensor calibration.
#  Atmospheric correction is for deriving normalized water-leaving radiance spectra by
removing all atmospheric and surface radiance contributions.
#  SeaWiFS, MODIS, MERIS have been providing high quality ocean color products in
the global open oceans. #  Satellite data are useful for coastal and inland lake water quality monitoring,
assessment, and management.
#  For the turbid waters in coastal regions, some regional algorithms and approaches in
dealing with coastal complex ocean waters are useful (e.g., building regional
reflectance relationship from in situ measurements).
#  To deal with significant ocean contributions at the NIR bands in the turbid waters,
shortwave infrared (SWIR) bands can be used for atmospheric correction because of
significantly strong ocean absorption at the SWIR bands.
#  For atmospheric correction in dealing with strongly-absorbing aerosols (e.g., dust,
smoke), information of aerosol vertical distribution is required.
#  Future ocean color sensor needs to include UV bands for a better detection of
absorbing aerosols and requires SWIR bands with the required sensor SNR
characteristics for dealing with the turbid waters in coastal ocean regions and inland
lakes. Menghua Wang, IOCCG Lecture Series 2014--Atmospheric Correction
References
Castillo, D. et al., “Pre-Aerosol, Clouds, and ocean Ecosystem (PACE) Mission Science Definition Team Report,” NASA
PACE Mission Science Definition Team Report, 274 pp., October 2012.
IOCCG (2012), “Ocean-colour observations from a geostationary orbit,” D. Antoine (ed.), Reports of International
Ocean-Color Coordinating Group, No. 12, IOCCG, Dartmouth, Canada.
http://www.ioccg.org/reports_ioccg.html
IOCCG (2012), “Mission Requirements for Future Ocean-Colour Sensors,” McClain, C. R. and Meister, G. (eds.),
Reports of International Ocean-Color Coordinating Group, No. 13, IOCCG, Dartmouth, Canada.
http://www.ioccg.org/reports_ioccg.html Shi, W. and M. Wang, “Ocean reflectance spectra at the red, near-infrared, and shortwave infrared from highly turbid
waters: A study in the Bohai Sea, Yellow Sea, and East China Sea,” Limnol. Oceanogr., 59, 427–444, 2014. Son, S., M. Wang, and L. W. Harding Jr., “Satellite-measured net primary production in the Chesapeake Bay,” Remote
Sens. Environ., 144, 109–119, 2014. Wang, M., W. Shi, and L. Jiang, “Atmospheric correction using near-infrared bands for satellite ocean color data
processing in the turbid western Pacific region,” Opt. Express, 20, 741–753, 2012.
Wang, M., J. H. Ahn, L. Jiang, W. Shi, S. Son, Y. J. Park, and L. H. Ryu, “Ocean color products from the Korean
Geostationary Ocean Color Imager (GOCI),” Opt. Express, 21, 3835–3849, 2013. Wang, M., X. Liu, L. Tan, L. Jiang, S. Son, W. Shi, K. Rausch, and K. Voss, “Impacts of VIIRS SDR performance on
ocean color products,” J. Geophys. Res. Atmos., 118, 10,347–10,360, 2013. http://dx.doi.org/10.1002/jgrd.50793
Menghua Wang, IOCCG Lecture Series 2014--Atmospheric Correction
Questions?
Good Luck and Best Wishes!
Menghua Wang, IOCCG Lecture Series 2014--Atmospheric Correction