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

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

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 www.nasa.gov

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

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.

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, 2011. The satellite is placed on a geostationary earth orbit, this is exactly 35.786 km (22.236 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.

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

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,…)

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

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. http://missionscience.nasa.gov/ems/01_intro.html

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)

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

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

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

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

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

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

III … ‘True Color’ Images R + G + B = ‘True color’ 670 551 443 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

‘False color’ Radiance transformed to product concentrations

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

IV. Model Ocean Primary Productivity (PP)

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

IV. Models of Ocean PP: The VGPM Vertically Generalized Productivity Model (VGPM) Calculates euphotic-zone integrated PP (PPeu) as PPeu = 0.66125 × 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)

IV. Models of Ocean PP: The VGPM PPeu = 0.66125 × Pbopt × [ E0 × (E0 +4.1)-1] × CSAT × Zeu × DIRR CTOT Zeu Morel & Berthron (1989)

IV. Models of Ocean PP: The VGPM PPeu = 0.66125 × Pbopt × [ E0 × (E0 +4.1)-1] × CSAT × Zeu × DIRR But see karl et al.

IV. Models of Ocean PP: The VGPM PPeu = 0.66125 × 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

SST PBOPT

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

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

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.

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

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)

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 = 0.010 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

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

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)

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

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

* 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 (1999-2004)

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

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

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

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

Applications a) In Permanently stratified biomes (annual mean T>15°C) b) Between 2000-2006 decreasing VGPM and CHLSAT is related to c) Increased stratification/warming Band ratio CHL VGPM NPP

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

1981 NASA Propaganda poster Satellite oceanography was born in 1978. 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

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

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 0 0.1 1 10 100 CHL

IV. Models of Ocean PP: The VGPM mg C m-2 d-1 200 400 600 800 2007 Annual Mean Standard VGPM Pbopt = polynomial of SST Eppley VGPM Pbopt = exponential of SST http://www.science.oregonstate.edu/ocean.productivity/

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

Applications