Remote-sensing reflectance (sr-1): The ultimate objective of RS: Retrieval useful/important environmental information How? algorithm!

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2 Remote-sensing reflectnce sr - : R L,0 E,0 d L E d The ultimte ojective of RS: Retrievl useful/importnt environmentl informtion Ho? lgorithm!

3 Empiricl explicit or implicit No need: Bio-opticl models Semi-nlyticl Yes: Bio-opticl models lgeric, LUT, optimiztion Bottom Up Strtegy BUS Top Don Strtegy TDS

4 lgorithm inputs outputs IOPs or [Chl] etc L or R

5 Chl centered nd-rtio lgorithms O Reilly et l 998

7 Empiricl: Lee et l 998

8 Physics-sed lgorithms mechnistic Remote-sensing reflectnce sr - : R L,0 E,0 d L E d Ho is R relted to ter s opticl iogeochemicl properties?

9 Physics-sed lgorithms mechnistic Ho is R relted to ter s opticl iogeochemicl properties? Rditive Trnsfer Eqution: d L cl L ' ', d d l d Lu, z cl z u, L ' ', d d z loss gin dr z θ Lθ

10 ',, 0 ' ' ' sin ', ', ', ', ', ', / 0 0 S od f L L S d E d d L f k c D r 4 ', ', i i i p q r Alert nd Moley 003 : ', ' ', p p g g r Lee et l ', ', i i i g r Prk nd Ruddick ] [ln ] [ln ' ] ', ln[ j j i ij i P r Vn Der Woerd nd Psterkmp 008 Exct solution: 0 ', 0 ', d u E L r Zneveld 995

11 , ;, g g g r i i i Gordon et l 988: ',, ', Chl g r Morel et l 993, 996, 00:

12 r R? R E d L 0 E d 0 t E E d 0 E u 0 L R t E L n L t 0 r R Solve R for IOPs or in-ter constituents? t n L u 0.5r.7 r

13 To sic strtegies:. Bottom-up strtegy BUS: Assume e kno the spectrl shpes of the opticlly ctive components. Top-don strtegy TDS: Only need the spectrl shpe informtion hen it is necessry

14 Wht re e fcing in RS lgorithms? R F, R F,,,, p R R R F F F n,,, n,,, # of unknons > # of equtions! n,,, n,,, n p p p An ill formulted mth prolem! Hve to increse # of equtions or decrese # of unknons! n

15 . Bottom-up strtegy BUS: p λ = λ + xi λ λ = λ + xi λ Build-up n R spectrum lock-y-lock: M M 3 p M g d

16 Bio-opticl models forrd model

17 Exmple of one prmeter hypepectrl λ model: Bricud et l 995: Modeling spectrum A Chl B Lee 994; Lee et l 998: 0 ln P P P = 440

18 Exmple of to prmeter model: Ciotti et l 00

19 Multiple prmeter model: Hoepffner nd Sthyendrnth, 993

20 Exmples of D Gr spectr.0 HOPE 0. HOPE 3.0 GSM Wvelength [nm]

21 Asorption components: spectrum shpes S 440 e S: nm - Bricud et l 98

22 p 440 R G

23 R G M M M 3 p M 3 p 3-vrile model to descrie n R spectrum Sthyendrnth et l 989 M -3 re velength independent vriles! Then they could e derived y compring the modeled R spectrum ith the mesured R spectrum. Spectrl rnges used for solutions e.g. exmples of BUS: The lue-green domin: e.g., Hoge nd Lyon 996, Crder et l 999 The red-infrred domin: e.g., Binding et l 0 The entire spectrum spectrl optimiztion: e.g., Bukt et l 995, Lee et l 994,996,999, Mritoren et l 00, Boss nd Roesler 006, Brndo et l 0,Werdell et l 03 Look-Up-Tles LUT: e.g., Crder et l 99; Moley et l 005

24 R [sr - ] Spectrl Optimiztion Mtching eteen mesured nd modeled R Mod. R Me. R Quntittive mesure of the closure error function: R ~ ~ n R R R R R R Wvelength [nm]

25 Algorithms using informtion in the red-infrred nds Gitelson et l 007 To nds Three nds Chl f R75x R67x Chl f R75x R67x R75x R70x

26 To-, three-, four-nd rtios in the red-infrred domin: red red red red red p red p red red M M R R Proper contt of R t λ nd λ then leds to M. 3 p M M G R 3 3 p p M M M M G R

27 . Top-don strtegy TDS: R G R & x Clrity Secchi depth, light depth, TSM/SPM, etc Remote sensing mesures the totl effect: Wter clrity or turidity is lso mesure of totl effect. Exmples of TDS: Loisel & Strmski 000, QAA Lee et l, 00; Smyth et l 006; Dorn et l 007.

28 The Qusi-Anlyticl Algorithm QAA Forrd modeling:,,etc R R QAA:, R

29 The dt flo of QAA: r η p 0 F r 0, 0, 0 0 p 0 p Lee et l F3 r, p,

30 Logic ehind QAA nd its updted veions: 0.4 sorption coefficient m pure ter [C] = 0.03 [C] =.0 [C] = 5.0 λ velength nm For reference velength, λ 0, vrition of λ 0 is limited. Knon λ 0, enles clcultion of λ 0 from R λ 0 ; propgte λ 0 to λ, then enles clcultion of λ from R λ. No need of spectrl model of x λ in this process! λ 0

31 When 550 nm s the reference velength λ log r r443 r 5 r 550 = r r Empiricl!

32 555 m [m - ] [m - ] 555 m -

33 r λ = R λ/ R λ 0 0 * 4 g r g g g u g 0 =0.089,g =0.5 0 g g r R r {& } Invert R:

34 p u u550 p p

35 η Empiricl: r exp 0.9 r55x dt v4 v r 443/r 550

36 p u u

37 , ,

38 , g ,

39 4/ 443 S [nm - ] dt v dt v r 443/r r 443/r r 443 / r 550 e S 4434 S , r / r 550 Empiricl!

40 Correction of Rmn-Scttering contriution: R T R R E Rm R Rm Moley 0, Westerry et l 03 Solve for R -Rm nd IOPs through itertion

41 Empiricl scheme: R E RF: Rmn fctor T R RF Lee et l 03 Lee nd Huot, 04

42 / ] [ j i R R f Chl / i i i j j j j p j i p i j i f R R [Chl] is ctully n IOP product Wht is nd-rtio derived [Chl]?

43 Key Points:. Vrious inveion lgorithms for IOPs hve een developed; ut more/etter ones re lso expected.. BUS derives every component fit, then simultneously derives the totl opticl property. Assume the spectrl shpes of the opticlly ctive components re ell chrcterized! BUS relies more on the ccurcy of forrd io-opticl model 3. TDS derives totl fit, then decompose to seprte components. TDS relies more on the ccurcy of R mesurement

Chl distribution of the global oceans (OBPG)

Chl distribution of the global oceans (OBPG) Chl distriution of the glol ocens OBPG / ] [ j i R R f Chl / i i i j j j j p j i p i j i f R R [Chl] is ctully n IOP product Remote-sensing reflectnce sr - : R L,0 E,0 d L E d The ultimte ojective of RS: