1. Primary Productivity from Space
Agouron Summer Course on Microbiology
May 31st, 2012
Angelicque White, Oregon State University

2. Outline Starting simple – satellites, sensors & orbits
Abridged version of water leaving radiance & chl
A few slides on color scales
Current models of oceanic primary productivity

3. I. Satellites , Sensors & Orbits
NASA has more than a dozen satellites orbiting the earth … covers vegetation indexes, fire maps, ocean temperature, ocean currents, weather, precipitation, sea surface height

4. I. Satellites , Sensors & Orbits
All satellites consist of
a power source (generally solar),
an antenna (to receive and transmit data)
and a payload (sensors!)

5. I. Satellites , Sensors & Orbits
Geostationary Orbit
~35,786 km altitude
W to E over the equator
Views same location
Can document evolving systems
High temporal resolution
Lower spatial resolution
GOES SST and Korean GOCI, Elektro-L
Polar Orbit
650 – 850 km altitude
Travels over poles:
Sees whole globe
Low temporal resolution
Higher spatial resolution
Ocean color et al.

6. Photos to the video we can enjoy were captured by the Russian weather satellite, Electro-L every half hour beginning on May 14th, and end on May 20th, The satellite is placed on a geostationary earth orbit, this is exactly km ( mi) above the Earth's equator and follows the direction of the Earth's rotation. An object in such an orbit has an orbital period equal to the Earth's rotational period, and thus appears motionless, at a fixed position in tPhotos taken by satellite are a combination of visible and near-infrared wavelengths, depicting the Earth in a way not visible to the human eye.

7. I. Satellites , Sensors & Orbits
Sea-viewing Wide Field-of-view Sensor = SeaWiFS in a Day
The gap here is caused by the satellite tilting as it passes over sunglint

8. I. Satellites , Sensors & Orbits
Altimeter
(GEOSAT, TOPEX, ERS, AVISO,…)
Scatterometer
(SeaWinds, Quikscat)
Sea Surface Salinity
(Aquarius 2010)
Ocean Color
(CZCS, SeaWiFS, MODIS,…)
Sea Surface Temperature (AVHRR, MODIS,…)

9. Outline Starting simple – satellites, sensors & orbits
Abridged version of water leaving radiance & chl
A few slides on color scales
Current models of oceanic primary productivity

10. II. Abridged Water Leaving Radiation
See through clouds
Clouds block these
Ocean Color
Microwave SST, altimeter, scatterometer (winds)
SST
(IR)
This figure is from the mentioned website, where you can find detailed explainations of the Spectrum and how NASA uses it for remote sensing.

11. II. Abridged Water Leaving Radiation
Rrs = Remote sensing radiance
Normalized water leaving radiance (nLw)= Rrs (l) corrected for atmospheric contributions, solar irradiance and zenith angle
light that upwells from below the surface (due to absorption and scattering by phytoplankton)

12. II. Abridged Water Leaving Radiation
Rrs ~ >0.9(Atmosphere) + <0.1(Sea surface)
Top of the
Atmosphere (TOA)
Sea surface
10 mg chl a m-3
0.01 mg chl a m-3
CZCS visible and NIR bands
1.0
Radiance
0.05
0.01
Wavelength

13. II. Abridged Water Leaving Radiation
Reflectance
Pure Seawater
Wavelength
400
700
0.07 mg chl m-3
8.7 mg chl m-3
Biology
absorbance
Mention spectral bands of sensors
scattering

14. II. Abridged Water Leaving Radiation
nrW (550)
CHL, mg m-3
nrW (443)
CHL, mg m-3
Gor
nrW (443)/ (550)
CHL, mg m-3
From Gordon et al. 1988

15. II. Abridged Water Leaving Radiation: CHL
R = nrW (443)/ (550)
C= CHL, mg m-3
Gor
In addition to this and other empirical methods for estimating chl, there are semi-analytical methods that also produce CDOM and backscatter (443nm)
From Gordon et al. 1988

16. II. Abridged Water Leaving Radiation: CHL
Passive measurement
Measures light emitted from the ocean (careful to distinguish between ‘emission’ and ‘reflection’)
Most of the signal (>90%) at the satellite is NOT ocean color but atmospheric and surface reflections
Actual ocean signal measured is normalized water leaving radiance, often denoted nLw (after correction for atmospheric signal) and viewing angle + incoming solar irradiance
Chlorophyll is derived empirically or semi-analytically from nLw

17. Outline Starting simple – satellites, sensors & orbits
Abridged version of water leaving radiance & chl
A slide or two on color scales
Current models of oceanic primary productivity

18. III … ‘True Color’ Images
R + G + B = ‘True color’ nm
The ‘brightness’ of incoming reflectance in different spectral bands can be combined to generate images, works just like the human eye or a digital camera

20. ‘False color’
Radiance transformed to product concentrations

21. Outline Starting simple – satellites, sensors & orbits
Abridged version of water leaving radiance & chl
A slide or two on color scales
Current models of oceanic primary productivity (PP)
VGPM
CbPM

22. IV. Model Ocean Primary Productivity (PP)

23. IV. Models of Ocean PP: The VGPM
PPeu (light, chl, SST dependent max. photosynthetic rate, daylength - DIRR)
Remove effect of light, daylength and chl
Origin – Cullen  Falko & Behrenfeld

24. IV. Models of Ocean PP: The VGPM
Vertically Generalized Productivity Model (VGPM)
Calculates euphotic-zone integrated PP (PPeu) as
PPeu = × Pbopt × [ E0 × (E0 +4.1)-1] × CSAT × Zeu × DIRR
satellite-based inputs:
satellite chl, CSAT (used as a light harvesting term and for calculation of the euphotic depth
Sea surface temperature, used to parameterize maximum in water C-fixation per unit chl, Pbopt
Sea surface daily PAR (E0)

25. IV. Models of Ocean PP: The VGPM
PPeu = × Pbopt × [ E0 × (E0 +4.1)-1] × CSAT × Zeu × DIRR
CTOT
Zeu
Morel & Berthron (1989)

26. IV. Models of Ocean PP: The VGPM
PPeu = × Pbopt × [ E0 × (E0 +4.1)-1] × CSAT × Zeu × DIRR
But see karl et al.

27. IV. Models of Ocean PP: The VGPM
PPeu = × Pbopt × [ E0 × (E0 +4.1)-1] × CSAT × Zeu × DIRR
PBopt [ mg C (mg chl)-1 hr-1]
7th order polynomial fit
SST (oC)
‘Eppley’ Exponential fit
From Behrenfeld and Falkowski, 1997

28. SST
PBOPT

29. IV. Models of Ocean PP: The VGPM
Global annual phytoplankton carbon fixation ~ 45 Pg C

30. IV. Models of Ocean PP: The VGPM
Errors and improvement
Errors in E0 and Dirr are very small (<5%)
Errors in Zeu are larger, but minimal compared to others …
Error in determining Csat accurately can be large
Huge errors in Pbopt , much of which may be related to variability in field measurements (i.e. the validation dataset) and SST as poor regional predictor

31. IV. PP Models: A C-based approach
Rationale for the C-based productivity model (CbPM)
Really interested in C but we can measure chlorophyll
So we use models that relate chl and phytoplankton physiology to light and then estimate net primary production
The problem: These estimates don’t always agree well with in situ measurements
Potential solution: We know a lot about the carbon to chlorophyll ratio of phytoplankton (Chl:C) and how it changes in response to changes in nutrients and light
If we can quantify this parameter from space, we should be able to do better at quantifying the physiological status of phytoplankton (that is, growth rate, ) and use this in models.

32. Remote-sensing advances: Backscatter
IV. Models of Ocean PP: The CbPM
Remote-sensing advances: Backscatter
Semi-analytical models separate scattering vs absorbing components of the Rrs spectra
This means we can use satellite data to get
chlorophyll, CDOM and
particle backscattering (bbp)
empirical relationships exist between cp and bbp

33. IV. Models of Ocean PP: The CbPM
Carbon derived from phytoplankton backscatter at 443nm via an inversion approach = nonlinear minimization that solves for three unknown quantities which together best reproduce the satellite-measured spectral reflectance
1. Chl
2. bbp(443) – the scattering coefficient at 443 nm due to particles
3. CDOM, the absorption by colored dissolved and detrital matter at 443nm
Maritorena et al. (2002)

34. IV. Models of Ocean PP: The CbPM
Carbon = [bbp (443) – bbp(443)NAP ] * 13,000 mg C m-2
The scalar of 13,000 mg C m2 was chosen to give average satellite Chl:C values = and average phytoplankton contribution to total particulate organic carbon of 30%
60% of the ocean in the physiology domain < 0.1
From Behrenfeld et al. 2005

35. IV. Models of Ocean PP: The CbPM
~CHL:C
CHL
CHL:C
bbp

36. IV. Models of Ocean PP: The CbPM
Chl:C – Chl:Cm=0
Chl:C maxTN – Chl:Cmin
m (0-1) a
Growth rate
after Behrenfeld et al. (2005)

37. IV. Models of Ocean PP: The CbPM
Chl:C – Chl:Cm=0
Chl:C (N,T) – Chl:Cm=0

38. IV. Models of Ocean PP: The CbPM
1. Start with satellite-derived bbp
2. Use empirical relationships to get phytoplankton C from bbp
3. Calculate phytoplankton Chl:C
4. Use Chl:C and the information it provides about phytoplankton physiology to determine phytoplankton growth rates ().
5. NPP =   C (approximately) * volume function

39. * if z<MLD, Chl:C = satellite estimates
SeaWiFS
FNMOC
WOA01
INPUTS
nLw
Kd(490)
PAR(0+)
MLD
NO3
Maritorena et al. (2001)
Austin & Petzold (1986)
DNO3 > 0.5 mM
bbp
chl
Kd(l)
Ed(l)
zno3, Dzno3
Morel (1988) C
Chl:C
Photoacclimation
PAR(z)
DChl:Cnut
Light limitation
NPP m
OUTPUTS
* if z<MLD, Chl:C = satellite estimates
* red arrows indicate relationships exist ONLY when z>MLD
* Run with 1° x1° monthly mean climatologies ( )

40. IV. Models of Ocean PP: The CbPM
mg C m-2 d-1
mg C m-3 d-1

41. IV. Models of Ocean PP: The CbPM
large spatial (& temporal) differences in carbon-based NPP from chl-based results (e.g., > ±50%)
differences due to photo acclimation and nutrient-stress related changes in Chl : C

42. IV. Models of Ocean PP: The CbPM
Issues:
Ratio of two biomass terms (error propagation)
Dependence of inversion approach on separation of absorbing (CDOM and CHL) and scattering terms (bb443 )
Limited data for model validation (growth rate and Cph:CHL)
mmax not well resolved
Particulate backscatter not uniquely algal in origin and particulate carbon ≠ phytoplankton carbon

43. Summary of Productivity Models
Major derivations of NPP are NPP a (CHL) or NPP a(CHL:C)
Despite challenges in getting physiology based transfer functions and accuracy of satellite retrievals… Satellite NPP models have revolutionized oceanography
Phylogeny – HABS ---- Weather- Productivity --- Physics

44. Applications
a) In Permanently stratified biomes (annual mean T>15°C)
b) Between decreasing VGPM and CHLSAT is related to
c) Increased stratification/warming
Band ratio CHL
VGPM NPP

45. Applications ALOHA NPP ~ VGPM ALOHA CHL ~ CHL SAT
Period of Reduced NPP inferred from remote sensing
ALOHA CHL ~ CHL SAT

46. 1981 NASA Propaganda poster
Satellite oceanography was born in that was the year of the first AVHRR for SST on the weather satellites and later that year NASA launched Nimbus-7 with the coastal zone color scanner (CZCS) and SEASAT which carried the first altimeter, SAR, wind scatterometer and microwave radiometer (SMMR). SEASAT was launched on June 27, 1978 and lasted only 100 days but ushered in the age of satellite oceanography. By 1981 NASA had a satellite oceanography program headed by Stan Wilson (from ONR), and this was one of the posters created by the PO Program Manager Bill Patzert to help sell the value of satellite oceanography and lobby for the next generation of satellites.
1981 NASA Propaganda poster

47. Acknowledgements Thanks: Agouron Institute & Course Organizers
Input/Slides: M. Behrenfeld, R. Letelier C. Davis, T. Westberry, and P. Strutton

48. PP = Chla * Eo * aph* Фp PP = Biomass * µ
IV. Model Ocean Primary Productivity (PP)
PP = Chla * Eo * aph* Фp
PP = Biomass * µ
3000
1000
300
100 30
mg C m-3 d-1
CHL

49. IV. Models of Ocean PP: The VGPM
mg C m-2 d-1
2007 Annual Mean
Standard VGPM
Pbopt = polynomial of SST
Eppley VGPM
Pbopt = exponential of SST

50. IV. Models of Ocean PP: The CbPM
Maritorena et al. (2002)

51. Applications