1 Retrieval of ocean properties using multispectral methods S. Ahmed, A. Gilerson, B. Gross, F. Moshary Students: J. Zhou, M. Vargas, A. Gill, B. Elmaanaoui, K. Aran Spectral Algorithm Development for Sensing of Coastal Waters Separation of Overlapping Elastic Scattering and Fluorescence from Algae in Seawater through Polarization Discrimination

2 Reflectance curves from the 2002 cruise in Peconic Bay, Long Island Spectral Algorithm Development for Sensing of Coastal Waters

3 Ratio algorithm performance – Eastern Long Island Blue / GreenNIR Spectral Ratio In homogeneous waters where only Chlorophyll varies Blue / Green works only in Case I (see later) NIR Ratios work well in both Case I and Case II but may be limited by small signals in open waters

4 1- Chlorophyll absorption can be probed effectively using band ratios 2- In presence of TSS and CDOM, Blue-Green ratios are contaminated. 3- Red-NIR algorithms are much less sensitive to TSS, CDOM. 4- The channels effectively probe the ChL absorption feature and the 730 channel effectively calculates the backscatter since water abs dominates Absorption/Backscatter features

5 Blue-GreenThree Band NIR ratios Very high spread in the Blue-Green Ratio due to CDOM and TSS randomized variability. This aspect is not relevant to the Red/NIR algorithms Simulation

6 Multispectral versus Hyperspectral assessment of GOES-R Coastal Water Imager Future sensors (GOES-R) need to decide between multispectral or hyperspectral mode. Hyperspectral channels are very important for shallow water retrieval Preliminary tests compared multispectral vs hyperspectral sensing schemes based on Hydrolight Radiative transfer derived bio- optical model.

7 Shallow Water Bio-Optical Model Based on Hydrolight RT simulations (Carder et al) PPhytoplankton Absorption at 440nmDeepShallow GGelbstoff Absorption at 440nmDeepShallow XBackscatter Amplitude at 440 nmDeepShallow YBackscatter Power ExponentDeepShallow HOcean Column DepthShallow BBottom Surface AlbedoShallow Parameterized Shallow Water Model Parameters Remote Sensing Reflectance Spectra

8 Inversion error versus measurement noise for all 6 parameters Normalized Parameter Retrieval Error Noise (%)

9 Results Hyperspectral channels are absolutely needed to reduce errors in shallow bottom heights and bottom reflectance (Panels 1 and 5) Ocean column parameters are also much better retrieved using Hyperspectal configuration except for spectral slope of backscatter parameter which makes sense since this parameter caused only broad modification of the reflectance spectra. (Panel 6)

10 Chl retrieval in Productive Case I waters can be obtained by both conventional blue-green type algorithms as well as NIR ratio algorithms TSS and CDOM variability in case II waters makes blue/green ratios useless but three band NIR ratios are very insensitive to these parameters Ratio algorithms for case II waters need thorough testing with in-situ monitoring using a consistent field testing protocol. The effects of atmospheric correction to assess the sensitivity of the various two and three ratio algorithms need to be explored. Development and sensitivity analysis of simultaneous atmosphere /ocean parameter retrieval using both multispectral and hyperspectral algorithms

11 Separation of Overlapping Elastic Scattering and Fluorescence from Algae in Seawater through Polarization Discrimination Objective: Separate overlapping fluorescence and elastic scattering spectra of algae excited by white light Method: Utilize polarization properties of elastically scattered light and unpolarized nature of excited fluorescence to separate the two Applications: Use fluorescence obtained as indication of Chl concentration even in turbid waters Obtain elastic scattering spectra free of overlapping fluorescence for ocean color work

12 Reflectance curves from the 2002 cruise in Peconic Bay, Long Island

13 Fluorescence Height Wavelength, nm Fluorescence Height Reflectance Traditional method of the fluorescence height calculation over baseline

14

15 Experimental Setup Illuminator Nozzle θ Spectrometer L C P2 FP WL P1 i2i2 i1i1 L – lens, FP – fiber probe, A – aperture, P1, P2 – polarizers, C – cuvette with algae, WL – water level. Objects tested: algae Isochrysis sp., Tetraselmis striata, Thalassiosira weissflogii, “Pavlova”, concentrations up to 4x10^6 cells/mL, algae with clays.

16 Polarized Illumination Near zero if no depolarization valid for spherical particles Generally validated using laser induced fluorescence but significant error results due to scattering component

17 Extracted Fluorescence Algae Isochrysis sp. (brown algae spherical d ≈ 5 µm) Algae Tetraselmis striata (green algae slightly ellipsoidical d ≈ 12 µm) Technique with polarized light

18 Unpolarized source Light scattered by the algae illuminated by unpolarized light has some degree of polarization and can be also analyzed using polarization discrimination with the same linear regression approach Algae Isochrysis sp. (brown algae spherical d ≈ 5 µm)

19 Algae with clay Clay – Na-Montmorillonite, particle size 2-4 µm Reflectance curves for algae with clay, C s = mg/l Fluorescence magnitude retrieved from algae with different concentrations of clay

20 Extraction of fluorescence in the waters with rough surface (lab experiments) Unpolarized light Probe above the water, probe vertical No windWind speed above the surface ≈ 9.5 m/s Sample time increased to 10s from 1s Algae Isochrysis. Concentration ~4.0 mln cells/ml.

21 Extraction of fluorescence in the waters of Shinnecock Bay, Long Island Chl concentration about 8 µg/l June 2004 Ratio between 2 polarization components is close to linear

22 Simulation Model for Case 2 Waters - Reflectance - Backscattering coefficient - Absorption coefficient - Absorption coefficient of phytoplankton - Absorption coefficient of CDOM - Absorption coefficient of minerals - Energy of emitted fluorescence Input [Mobley, 1994] [Bricaud, et al., 1981] [Morel, 1991] [Stramski, et al., 2001] [Morel, 1977] [Gower, et al., 1999]

23 Half of fluorescence is superimposed on polarization components as a spectrum with Gaussian shape centered at 685 nm Output Polarization components of reflectance are calculated from Mie code for 45° illumination (30° in water) & vertical observation Simulation model for case 2 waters Fluorescence is retrieved using polarization technique A and B are determined from fitting outside fluorescence zone where Polarization components of were used for calculation of reflectance polarization components -scattering function at 150°, which was used as average value for calculating backscattering

24 Simulation Model Results Fluorescence retrieval from reflectance spectra for different concentrations of mineral particles: a) C = 5 mg/m3, b) C = 50 mg/m3.

25 Results of fluorescence retrieval, comparison with baseline method Comparison of retrieved fluorescence peak to assumed values for a range of mineral particle concentrations using both polarization discrimination and baseline subtraction

26 Conclusions/Future Work Separation of Chlorophyll Fluorescence from scattering using polarization discrimination has been demonstrated for 4 types of algae with different shapes, sizes of particles Implementation of the technique using both white light and sun light sources has proven successful in the lab and in the field conditions Fluorescence extraction has been obtained even with the presence of high concentration of scattering medium Validation with laser induced fluorescence has been performed Extraction of fluorescence is successful for all illumination angles with polarized light, up to 50 deg for unpolarized light.

27 Conclusions/Future Work Magnitude of fluorescence peak extracted from reflectance spectra through polarization technique does not change with the concentration of scattering medium up to 200 mg/l. Computer simulations show that fluorescence can be successfully retrieved for most water conditions typical for coastal zones with accuracy 7-11%. “Fluorescence height” over baseline strongly overestimates actual and retrieved fluorescence height and these values do not correlate with each other for different concentrations of mineral particles. Future simulations should include effects of multiple scattering and atmosphere on polarization components and fluorescence retrieval process.

28 Long Island Field Measurements

29 Bio-Optical Model 1 Due to column and water floor respectively

30 is the absorption coefficient due to phytoplankton is the absorption coefficient due to water is the absorption coefficient due to gelbstoff is the backscattering of water is the backscattering by particulate matters Bio-Optical Model 2

31 taken from tabulated values in Lee et all. is the phytoplankton absorption coefficient at 400 nm which varies with the CHLOROPHYLL concentration. G is the gelbstoff absorption at 440nm is dependent on Bio-Optical Model 3

32 is the backscattering coefficient of particulates at 440 nm gives an indication of the size particles. Bio-Optical Model 4 The parameters in the reflectance model to be retrieved are: Particulate scatter Water bottom (lambertian) Using sand based normalized spectral response