Rrs(λ) IOPs. What to do with the retrieved IOPs?

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2 Rrs(λ) IOPs What to do with the retrieved IOPs?

3 Chlorophyll concentration: [Chl] Examples: Carder et al (1999), GSM (2002), GIOP (2013) R ( ) F( a( ), ( )) rs a( ) aw( ) M1 aph( ) M 2 adg( ) a ph () a : Varies with temperature in Carder et al (1999); ph() Fixed gloally in GSM (Maritorena et al 2002); Varies with [Chl] in GIOP (Werdell et al 2013)

4 Why chlorophyll concentration? 1. Estimate gloal primary production (PP) food chain; caron cycle One of the principal applications of satellite ocean color data is to derive net primary production (NPP). --- McClain (Annu. Rev. Mar. Sci., 2009) On a gloal scale, marine phytoplankton consume fifty thousand million tones of caron every year in a process referred to as primary production. -- IOCCG Report #2

5 Elements of photosynthesis: CO2 + H2O + phytoplankton + light + nutrient chemical energy

6 Components for PP quantification: 1. Phytoplankton 2. Light penetration 3. Photosynthesis parameters (Platt and Sathyendranath, 2007)

7 Traditional strategy: [chl] centered system Light field Ocean color spectrum [chl] Works for waters where IOPs co-vary with [Chl].

8 IOP-ased approach: color a,, a ph IOPs auxi. K d () PP Works for most waters.

9 1.1 Estimation of diffuse attenuation coefficient (K d ) (for light field) K d (490) Rrs( 1 ) Rrs( ) 2 Or, K d () Chl

10 K or K d d (490) (490) a 1 1 a X 2 2 X a 3 with X or X Lwn(488) Lwn(551) Rrs(488) Rrs(551)

11 Ratio-derived Kd (Darecki and Stramisk, 2004)

12 Aas (1987) two-stream solution: cos( ) d L d z cl 4 L( ', ') ( ', ' ) d w' d dz d dz E E u d ( a r ) E r u E ou u ou ( a r r d d E ) E od od

13 ou u od d od d E r E r ce E dz d ) ( u u u d d d d E r E r a E dz d u u d d d d R r r a K ou u od d d E r E r a E dz d ) ( ( ) d d a D K Empirical approximation:

14 K ( ) a v d s v 4.18( e 10 a ) R a& rs Kd No division of Case 1 or Case 2 waters. (Lee et al. 2005)

15 der d (490) / 5 3 1:1 Araian Sea GOM Baltic K d (490) [m -1 ] K d (490) K d (490) - measured [m -1 ] (IOP-model) Oceanic & Coastal waters (Lee et al. 2005) mea K ) (490 K d (IOP-model) Araian Sea GOM Baltic K - measured [m -1 ] d (490)

16 K d [m -1 ] K d [m -1 ] Other wavelengths: profile-kd IOPs-Kd profile-kd IOPs-Kd Y Data 0.04 Y Data 0.02 (a) Wavelength X Data [nm] 0.1 () Wavelength X Data [nm] (Lee et al 2013)

17 1.2 Attenuation of PAR (K PAR ) PAR( z) PAR(0) e K PAR z Good for earlier days, Not so good for the 21 st century (Morel, 1988, JGR)

18 K PAR 1 ln z PAR( z) PAR(0) PAR( z) E0 (,0) e K (, z) z d K PAR is light-quality weighted! Change of light quality with depth: (Kirk 1994) Light at deeper depth is associated with lower attenuation coefficient

19 Broadand attenuation is light dependent, so no-longer additive!

20 PAR( z) PAR(0) e K par ( z) z K PAR ( z) K 1 K2 (1 z) Key: K PAR varies with depth, especially in the upper water column! 0.5 Euphotic depth (z eu ): PAR( z) PAR(0) 1% z eu K 4.6 PAR ( z eu )

21 1.3 Euphotic depth (Z eu ) [Chl] PAR(0) R rs Z eu (water clarity) PAR(z eu ) PAR( z eu ) PAR(0) 1%

22 [Chl] (empirical) approach: Rrs(λ) [Chl] z eu [Chl] approach (Morel 1988)

23 IOP approach: Rrs(λ) a(λ)& (λ) K PAR (z) z eu K PAR ( z) K 1 ( a(490) & (1 z) 2 a(490) & (490) 0. 5 K (490))

24 Rrs derived Z eu [m] Rrs-derived Z eu [m] Water clarity (z eu ) IOP approach 80 1:1 1: Measured z eu [m] Measured Z eu [m] (Lee et al 2005)

25 Gloal distriution of Z eu water clarity (m)

26 Z eu can e measured with modern optical-electronic system

27 1.4 IOP ased PP estimation: PP z) E (, t, z) a ph(, z) d dt ( 0 φ: quantum yield of photosynthesis represents photosynthesis Ocean color K d, a ph PP ϕ (Kiefer and Mitchell, 1983, L&O)

28 Remotely-estimated PP compared with measured PP calculated production (mol/l/day) 10 a ph -centered Chl-centered 1: measured production (mol/l/day) (Lee et al., Appl. Opt., 1996) (Lee et al., JGR, 2011)

29 Why a ph ased approach is likely etter? Essence of present satellite Chl product: R R rs rs (440) (550) R rs G a(550) a(440) a w w Chl (440) (550) fun p p R R rs rs (440) (550) ( 1 ) ( ) 2 R R rs rs (440) (550) a(550) a(440) a a w w (550) (440) a a dg dg (550) (440) a a * ph * ph (550) Chl (440) Chl Change of R rs and ratio not necessarily represents change of [Chl]!

30 (Szeto et al 2011, JGR)

31 Ratio-derived Chl The change of Rrs ratio really reflects change of total asorption! (Lee et al, 2010, JGR)

32 Brief summary aout current satellite Chl product: The map of Rrs ratio represents a map of total asorption coefficient. The GSM-derived Chl represents more of phytoplankton asorption coefficient. To accurately retrieve spatially and temporally varying Chl from Rrs, we need: 1. Remove the influence of detritus/cdom and particles 2. Take into account the spatial/temporal variation of a* ph A promising effort: (Carder et al, 1999, JGR)

33 p : (Behrenfeld and Falkowski, 1997) p m a * ph P is also dependent on a * ph, which is not a constant either for a given Chl nor for varying Chl! (Platt et al 2008, RSE)

34 Another example of using remotely sensed IOPs for PP Caron ased Production Model (CPM) (Behrenfeld et al 2005; Westerry et al 2008) Chl C / f ( I g ) actually a ph from GSM p IOP-ased growth rate NPP Chl Z eu h( I 0 ) / f ( I g ) a ph from GSM IOP-ased NPP

35 2. Example of other applications of IOP products color [SPM] a,, a ph [chl] {others} K d () K PAR IOP Z SD, Z eu PP Works for most waters.

36 2.1 Secchi depth (Z SD ) Water clarity

37 empirical approach: (Olmanson et al 2008)

38 IOP approach: a( 490) & (490) (Doron et al 2007)

39 2.2 Water mass classification (Arnone et al 2004)

40 2.3 HAB identification-1 a ph (657) (Carnizzaro et al 2006)

41 2.3 HAB identification-2 QAA R rs (λ) a ph (λ) = a(λ) a dg (λ) a w (λ) (Lee and Carder 2004)

42 K. revis (Craig et al 2006)

43 2.4 Bloom dynamics Distriution of a ph (440) at Luzon Street (Shang et al, 2012)

44 Monthly distriution of a ph (440) at Luzon Street (Shang et al, 2012)

45 2.5 Gloal physiology of ocean phytoplankton IOP φ: quantum yield of fluorescence (Behrenfeld et al 2009) (Behrenfeld et al 2009)

46 2.6 Salinity estimation (Vodacek et al 1997)

47 SSS = x a CDOM + y (Castellio et al 1999)

48 2.7 pco2 estimation Lohrenz and Cai (2006) pco2 = f(t, S, Chl)

49 2.8 Concentrations of mineral particles (SPM; TSM) Many pulications in the literature p [SPM] (Neukermans et al 2009)

50 Key Points: 1. Many applications traditionally uilt around [Chl] can e uilt around IOPs. 2. Remote sensing and applications centered around IOPs avoided, when necessary, concentration-normalized optical properties. 3. With IOPs as inputs, many products, e.g. Kd, Zeu, PP, could e estimated easily and more accurately. 4. When IOPs are known, many other applications could e carried out. Be creative!!!