Variations of Estuarine Turbid Plumes and Mudflats in Response to Human Activities and Climate Change Dragon-3 project id

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1 Variations of Estuarine Turbid Plumes and Mudflats in Response to Human Activities and Climate Change Dragon-3 project id Chinese PI(s) Prof. SHEN Fang( 沈芳 ) Prof. ZHOU Yunxuan( 周云轩 ) European PI(s) Dr. David DOXARAN Dr. Suhyb SALAMA

2 Review of our work Dynamics of river plume in Yangtze estuary Suspended minerial Particulate Matter (SPM) of river plume SPM retrieval in extremely turbid waters Calibration and validation of SPM retrieval from the multi-sensor, e.g., Envisat/MERIS, Terra/Aqua/MODIS, FY-3/MERSI, GOCI Seasonal and annual SPM variation in 1-year time series Diurnal SPM variation using GOCI Suspended pigment particulate matter (Phytoplankton) of river plume

3 Chlorophyll-a Retrieval in Turbid Estuarine and Coastal Waters Fang SHEN, Yuli CHEN, Leonid Sokoletsky, Yunxuan Zhou State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, 262

4 Motivation A challenge remains for chlorophyll-a concentration (Chla) estimation using remote sensing in SPM rich waters Popular algorithms of Chla retrieval concentrate on turbid productive waters without the presence of much SPM A few algorithms on turbid eutrophic waters containing much SPM Previous algorithm (SCI) based on an empirical model Four algorithms sensitivity to SPM rich waters, e.g., two-band ratio, three-band, FLH and SCI algorithms

5 Datasets In situ dataset (during , cruises for collecting Rrs, IOPs and environmental parameters) Simulated dataset (Hydrolight code)

6 In situ Rrs spectra

7 Modeled Rrs Lee et al. (23) model for Rrs Kubelka-Munk model Lee model: (Shen et al.21, Sokoletsky et al. 213) for Rrs r = G ( G), G = b b / a+b b Numerical model Hydrolight R rs =.52 / (1-1.17r)

8 Modeled Rrs Lee et al. (23) model for Rrs Kubelka-Munk model (Shen et al.21, Sokoletsky et al. 213) for Rrs K-M model: Numerical model Hydrolight r = x / (1+x+sqrt(1+2x)), x = b b / a R rs =.52 / (1-1.17r)

9 Modeled Rrs Lee et al. (23) model for Rrs Kubelka-Munk model (Shen et al.21, Sokoletsky et al. 213) for Rrs Numerical model Hydrolight

10 Absorption coefficients modeling a ( λ) = S a ( λ) + (1 S ) a ( λ) * * * Chl f pico f micro S = [.1,.2,.3] f a * ( λ ) a * ( λ) pico micro a ( λ) = a ( λ )exp( S ( λ λ )) CDOM CDOM CDOM (Zhang et al., 213) 1 λ = acdom ( λ ) =.2m S CDOM =.15 44nm a ( λ) = a ( λ )exp( S ( λ λ )) * * SPM SPM SPM λ = 44nm a ( λ ) =.2m * 1 SPM S SPM =.2 backscattering coefficients modeling b bc λ λ n m ph ( λ) = ph ( ) b =.47, λ = 66 nm, m = 1, n =.795 b λ λ bspm ( λ) = bbspm ( λ )( ) n n=.4114 b b (532) SPM λ =532nm.3 C g m b b= SPM SPM 3 1 /, b /.183; C > g m b b= 3 1 /, b /.34;

11 Inputs for Hydrolight Parameter Input Sun angle(from zenith) Wind(m/s) 5 Wavelength(nm) 4~9, step=2nm C Chla (mg/m 3 ) 1-5 mg/m 3,n=1 (nonlinear, log) C SPM (g/m 3 ) 1-1g/m 3, n=2 (linear) a CDOM (44)(m.1 ).2m -1 Sea Surfer Temperature 2 Salinity(psu) 3 Sky model Semi-empirical sky model(based on RADTRAN) Depth Indefinite inelastic scattering of water default values

12 Results Rrs_Lee model nm Rrs_Hydorlight Rrs_Lee model nm Rrs_Hydorlight Rrs_Lee model nm Rrs_Lee model nm Rrs_Hydorlight Rrs_Hydorlight

13 Rrs_Lee model Rrs in situ Rrs_Lee model 56 nm 79 nm Rrs in situ Simulated Rrs vs. in situ Rrs Rrs_Lee model Rrs_Lee model 681 nm Rrs in situ 754 nm Rrs in situ

14 Sensitivity analysis 相对变化率 R R i rs rs = 1% Rrs R rs i Rrs 表示不含有该种成分时的水体的 rs 值 表示不同水体主要成分度水体的 Rrs 值, Rrs affected by SPM concentration Rrs affected by Chlorophyll-a concentration

15 Sensitivity analysis Rrs affected by a_g(44) while Chla=1ug/l, SPM:5,5,1

16 Sensitivity analysis a t-w 变化 Rrs 的影响 b bt-w 变化 Rrs 的影响

17 Four algorithms for Chla retrieval Chla ~ R ( λ )/ R ( λ ) Two-band ratio: rs 1 rs 2 Three-band: 1 Chla ~ R ( λ ) R 1 ( λ ) R ( λ ) rs 1 rs 2 rs 3 FLH (Fluorescent Light Height): Ch ~ R ( 2) R ( 3) ( λ3 λ2) ( λ λ ) la rs λ rs λ + Rr λ R s λ3 3 1 ( s( 1) r ( )) λ -λ chl Rrs λ4 + R λ2 R λ4 R λ3 λ4-λ2 λ4-λ 2 H = R ( λ2) R ( 4) ( R ( 1) R ( 4) ) rs λ + λ λ rs λ rs rs 4-λ1 ( rs rs ) rs ( ) 4 3 SCI (Synthetic Chlorophyll Index): H = ( ) ( ) ( ) SCI=H chl H

18 Two-band ratio, three-band, FLH and SCI algorithms SPM:1-1 mg/l Chla: 1-5 ug/l

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20 Two-band Three-band FLH SCI

21 Two-band ratio for Chla retrieval Three-band for Chla retrieval FLH for Chla retrieval SCI for Chla retrieval

22 Remarks The challenge remains Different scales between middle spectral resolution (1-2nm) for retrieval and high spectral resolution (e.g. 1-3 nm) for modelling Atmospheric correction is still on question

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