New Algorithm for MODIS chlorophyll Fluorescence Height Retrieval: performance and comparison with the current product

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1 New Algorithm for MODIS chlorophyll Fluorescence Height Retrieval: performance and comparison with the current product I. Ioannou, J. Zhou, A. Gilerson, B. Gross, F. Moshary and S. Ahmed Optical Remote Sensing Laboratory, Department of Electrical Engineering, The City College of the City University of New York, New York, NY, ABSTRACT Our previous studies showed that the Fluorescence Line Height (FLH) product, which uses 3 NIR bands at 667, 678, and 746 nm on the MODerate-resolution Imaging Spectroradiometer (MODIS) sensor, and similar bands on MERIS sensor, is not reliable in coastal waters because of a peak in the elastic reflectance spectra which occurs due to the confluence of chlorophyll and water absorption spectra and which overlaps spectrally the chlorophyll fluorescence. This combination of two overlapping peaks makes fluorescence signal retrieval inaccurate. As a consequence, the present FLH algorithm significantly underestimates fluorescence magnitudes in coastal waters. To overcome this problem, we introduce a new and more accurate approach for the retrieval of FLH in turbid waters by the MODIS sensor, which exploits the correlation between the blue-green and red bands reflectance ratios. We show that by making use of the combined remote sensing reflectance s (Rrs) at nm, nm, 667nm and 678nm we can retrieve fluorescence accurately in case 2 waters even for low fluorescence quantum yield when fluorescence magnitudes are low. The derivation and validation of our algorithm was performed using extensive synthetic datasets which cover a large variability of parameters typical of coastal waters: with CDOM absorption at 400nm 0-2 m -1, mineral concentration 0-5g/m 3 and chlorophyll concentration of mg/m 3. In addition, we applied this proposed algorithm to MODIS satellite data and compared it with the traditional FLH algorithm. Keywords: fluorescence, retrieval, phytoplankton, CDOM, coastal waters 1. INTRODUCTION The chlorophyll fluorescence signal can be measured actively when stimulated by artificial light sources, a method that was introduced by Lorenzen 1 et al. (1966) as a way to monitor chlorophyll concentration ([Chl]) in sea water. Until today in situ active fluorescence measurements still remain the most efficient and widely used method to estimate quasisynoptically [Chl]. Sun Induced Chlorophyll Fluorescence (SICF) 2 in the water-leaving radiance was first reported by Morel and Prieur The use of SICF to observe variations in phytoplankton biomass attracted interest due to the fact that it may be of particular advantage for case II waters, 2 where suspended particulate matter and colored dissolved organic matter (CDOM) can interfere with chlorophyll algorithms that are based on the blue-green ratios. 3 It is well recognized that operational Fluorescence Line Height (FLH) algorithms, 4,5 that are based on the measurements of reflectance at three wavelengths in the fluorescence band, are sufficient for fluorescence retrieval in the open ocean where atmospheric correction algorithms work well and elastic reflectance in the fluorescence band is well approximated by the baseline curve due to the relatively weak elastic scattering signal which depends on [Chl] alone. Clearly, this is not the case in coastal areas. Application of the FLH algorithms in the coastal waters is still significantly complicated by a peak in the underlying elastic reflectance which spectrally overlaps and contaminates any fluorescence retrieval. The structure and nature of this NIR peak is the result of a modulation of the particulate elastic spectrum (from both algal and non algal particles) by the combined phytoplankton and water absorption spectra, where the confluence of the decreasing phytoplankton absorption and the increasing absorption of water with wavelength results in a local absorption minimum. This absorption minimum leads to the maximum in the reflectance spectra which are inversely related to the total Remote Sensing of the Ocean, Sea Ice, and Large Water Regions 2009, edited by Charles R. Bostater Jr., Stelios P. Mertikas, Xavier Neyt, Miguel Velez-Reyes, Proc. of SPIE Vol. 7473, SPIE CCC code: X/09/$18 doi: / Proc. of SPIE Vol

2 absorption. 7 To compensate for the effects of this overlap of fluorescence and elastic spectra, and improve the operational FLH algorithms for coastal waters, it is clear that suitable models which attempt to take into account the larger impact of the spectral variation of the underlying elastic reflectance peak must be developed. To overcome these problems, this paper proposes to explore and exploit potential relationships between the blue and green channels of the radiance spectra, whose ratio is the traditional proxy for [Chl] and which is an effective approach in the open ocean, and reflectance channels in the red, and examine whether these relationships can be used to arrive at a more effective fluorescence retrieval for coastal waters. We first review the traditional FLH approach. 2. REVIEW OF FLH MODIS fluorescence line height algorithm (FLH), as described in Letelier et. al. 1996, is a relative measure of the amount of radiance leaving the ocean surface, which is presumably a result of chlorophyll fluorescence. By constructing a baseline using bands (MODIS and 746.4nm) on either side of the fluorescence band, we can estimate the increase of the peak above the baseline resulting from the chlorophyll fluorescence (MODIS 676.8nm). The fluorescence measurement itself is made at nm as a compromise between measuring the fluorescence peak in the region of 685 nm and the contaminating presence of an oxygen absorption band at 687 nm. Following Gower et. al.2004, 5 FLH = nlw678 κ nlw667 ( 1 κ ) nlw746 (1) Where κ = ( ) / ( ) = for MODIS. The FLH algorithm in fact almost always underestimates the actual fluorescence contribution to the elastic (without Fluorescence) nlw. As depicted in figure 1, the linear baseline is always higher than the actual nlw at the MODIS fluorescence band. The difference is due to the increased scattering, and a consequent peak in the baseline, with increasing [Chl]. 6,7 The resulting FLH underestimation will naturally decrease as a function of the quantum yield (φ f ; defined as the ratio of emitted to absorbed photons). For low quantum yields the underestimation is rather significant, but as φ f increases the FLH performance improves. This is due to the fact that the florescence signal is large compared to the underestimation error. The applicability of FLH in coastal regions is limited, since with φ f ~ 0.5%, it becomes difficult to resolve the relatively small fluorescence signal in locations where the [Chl] and its elastic scattering have become relatively large, with consequent overestimation of the baseline compared to the fluorescence signal to be detected (see graph on the left in figure 1). Figure 1. A schematic of the FLH algorithm, with blue dash/dot lines representing the normalized transmittance of the MODIS sensor. The solid black line shows the spectral distribution of upwelling radiance (L w ) above the surface of the ocean for [Chl]= 8 (left figure) and 24 mg m -3 (right figure). The black dash/dot lines superimposed on top of the reflectance spectrum indicate the fluorescence contribution for φ f = 0.5%. The linear baseline is shown in red and is connecting the 667 and the 746nm bands. As shown the elastic nlw in both figures is underestimated by the linear baseline, with a stronger underestimation at the higher [Chl] figure on the right. Thus, because of these factors, the use of the MODIS FLH algorithm for the retrieval of [Chl] in the coastal regions introduces large uncertainties, in addition to those related to φ f and the effect of CDOM absorption in the excitation Proc. of SPIE Vol

3 wavelengths. 7 If retrievals are to be effective, we must first reduce the uncertainty in the retrieval of fluorescence in coastal waters. 3. ALGORITHM DEVELOPMENT In order to obtain a better estimate of elastic scattering component at 678nm, we explore the relationship between the ratio of the elastic reflectance components at 667 and 678nm Re (678) ε =, ( Re denotes elastic reflectance components), Re (667) to that of the reflectance at and nm R. Instead of radiance units we chose to pursue retrievals in reflectance units. This approach is followed primarily to avoid any difference in the downwelling radiance between the two fluorescence bands, since the elastic radiation is stronger than that for case 1 waters and can cause errors in the fluorescence bands if the water leaving radiance is not normalized (to down-welling radiance). The signals can later be easily transferred back to radiance units by multiplying by the downwelling radiance at 678nm (denoted F 0 in MODIS). The R decreases when phytoplankton absorption increases, due to the fact that the nm is close to the 440nm peak, and the nm is close to a minimum in the phytoplankton absorption. This is the basis of the blue green ratio algorithms consistently used to retrieve [Chl] (e.g., see O'Reilly et al., 1998) in case 1 waters. The second peak of phytoplankton absorption centered around 675nm also increases with the increase in phytoplankton absorption, but due to the strong water absorption in that region, it becomes pronounced or even dominant when [Chl] increases to values that are usually observed in coastal regions. Since the nm (here we use actual MODIS bands) phytoplankton absorption is lower than the 675nm absorption and the nm, is closer to the 675 nm peak, we expect that ε should follow R. With a relationship that connects the elastic components in the chlorophyll fluorescence region and the blue green band ratio, a usual proxy of the chlorophyll concentration, the variation of elastic scattering signal due to increasing chlorophyll concentration can be taken into account. The goal of this approach is to retrieve the fluorescence signal, based on the empirical relationship between R andε. The relationship between these band ratios will next be derived and tested in simulations using synthetic data bases, as well as field data and satellite imagery. 3.1 Main features of the synthetic dataset A total of 2000 reflectance spectra with 1nm resolution were generated using HYDROLIGHT with and without the chlorophyll fluorescence for conditions typical of coastal waters. It is based on the four-component bio-optical models: pure water, non-algal particulates (mineral and detritus, NAP), chlorophyll contained particles (algae cells) and colored dissolved organic matter (CDOM). Instead of relating all the other water constituents to chlorophyll concentration [Chl] all these water constituents are allowed to vary independently with [Chl] varying from 0.5 to 100 mg/m 3, CDOM absorption at 400 nm in the range of 0 5 m -1 and concentrations of non-algal particles (NAP) changing from 0 to 100 g/m 3. Furthermore the relation between the inherent optical values and the concentrations of individual components is not fixed; instead it is allowed to vary randomly within a certain range which is consistent with the previous field observations. All details and assumptions used for the simulation of the water parameters are given in Gilerson et al They are similar to the assumptions used in the construction of the Lee datasets. 8 Proc. of SPIE Vol

4 Figure 2. Specific chlorophyll absorption spectra used in simulations and the derivation of the algorithm are based on Ciotti et.al. 9 To model the specific absorption of phytoplankton we used the Ciotti et al approach which uses a combination of Micro and Pico plankton weighted by the size factor [S f ] to model the specific absorption, * * * a ( λ) = S a ( λ) + (1 S ) a ( λ) (2) Where S f varies between 0.1 and 0.5 (see Figure 2); 0.5 means equal contribution of Micro and Pico-plankton. The chlorophyll absorption was considered proportional to [Chl] and given by, a chl ( λ) = [ Chl ] a The solar input was simulated using the Gregg and Carder model with a cloud-free sky. 9 The effective quantum yield [φ f ] of fluorescence was assumed to be 0.5%. All reflectance s were simulated for the sun zenith angle θ i = 30 and nadir viewing. 3.2 Empirical relationship chl f pico The simulated datasets have a spectral resolution of 1nm, hence to properly average the channels we used a normalized Gaussian function with FWHM of 10nm centered at the MODIS reference wavelengths 486.9, 546.8, 665.5, 746.4, and with FWHM of 11.3nm for the 676.8nm in order to accurately represent the measurements taken by the sensor. The empirical relationship observable between these two ratios is described by: * chl f ( λ). micro (3) 3 2 ε = 0.46 ( R ) ( R ) + 1 ( R ) (4) and can be seen in figure 3. To obtain this relation we limit the CDOM absorption at 400nm below 2 m -1, which can address most cases in case II waters. Due to the higher [Chl] and CDOM absorption of coastal water compared to the open ocean the reflectance at nm is usually less than nm in coastal area, leading to less than 1. The lower limit of R is determined by the maximum [Chl] and CDOM absorption of our datasets while its upper limit should converge to a constant which is mainly determined by the water absorption. We also limit our discussion to those waters with Rrs667 < 0.3% to flag out strongly scattering cases that badly contaminate fluorescence retrieval. R Proc. of SPIE Vol

5 DI rs19rs847 Figure 3. (R 678 ε ) as a function of R the solid line is Eq. (4), when Rrs 667 is less than 0.3% and 0.1< R < 1 and CDOM<2 m Fluorescence Retrieval Unlike FLH algorithm which connects a line between 667nm and 746nm to estimate the elastic signal at 678nm our estimation relies on the empirical ratio relationship between theε to R, which has been developed in previous section, here we called it Ratio Fluorescence Height (RFH). In this case we estimate the elastic reflectance components at 678nm by the product of the reflectance signal at 667nm, Rrs(667) and ε, a function of R and subtracting it from the total reflectance signal at 678nm Rrs(678), RFH = R rs ( 678) Rrs(667) ε (5) This process assumes that there is no fluorescence influence at the 667nm channel, a statement which is not true especially for high chlorophyll concentrations, since Fluorescence has a well known Gaussian spectral shape centered at 685nm with a full width half maximum of 25nm. With the shape of fluorescence known, we can easily see that the tail of the emitted fluorescence expands up to 650 nm, which gives a significant fluorescence emission at 667nm, when comparing it to the signal at 678nm. This shortcoming is dealt with below. Following Gower et al. 2004, the 667nm channel will be effectively responding to 20% of the fluorescence signal, and the 678 nm channel to 74% of the signal, where it is assumed that the fluorescence( fl ) signal can be modeled with a Gaussian shape centered at 685nm with FWHM of 25nm. Gower then calculates the reduction factor ([ ρ ]; defined as the ratio of the detected signal to the peak signal at 685nm), to be 0.57 for the MODIS configuration when using the FLH algorithm. This reduction factor for RFH is calculated as follows. The total reflectance at 678nm can be decomposed into the elastic part due to scattering as well as the inelastic part due to fluorescence, or analytically, R ( 678) = R (678) fl(678) rs e + (6) and similarly the reflectance at 667nm can be decomposed to, R ( 667) = R (667) fl(667) rs e + (7) Substituting equations (6) and (7) into equation (5) we obtain, Proc. of SPIE Vol

6 RFH = R ( 678) + fl(678) ( R(667) + fl(667)) ε e (8) Rearranging the terms in equation (8) we have, RFH = R ( 678) R (667) ε + fl(678) fl(678) ε e e (9) Since fl ( 678) = 0.74 fl(685) and fl ( 667) = 0.2 fl(685), and if the spread (uncertainty in the empirical relationship of figure 3) is neglected, the elastic terms cancel out, and substitution into Eq. (9) reduces it to: RFH fl(685) ρ = ε = MODIS (10) This ρ correction is used in our retrieval in this study for both simulated, field and satellite data. Instead of a fixed MODIS reduction factor (0.57) of traditional FLH, ρ is a function of R, as it more accurately reflects how the elastic scattering signal changes with other water constituents especially [Chl]. Since our fluorescence retrieval is performed in the Rrs units (sr -1 ) we next need to transform it to the radiance units (L w ; W m -2 μm -1 sr -1 ) that we use in this paper, by multiplying with the downwelling irradiance (E d ; W m -2 μm -1 ) to properly compare it with the MODIS FLH product. Since in MODIS the retrieval of FLH is done at the top of the atmosphere we also use the average extraterrestrial radiance used in the MODIS sensor (148 Wm -2 μm -1 ). 4. RESULTS The channels of,, 667 and 678nm are available for routine measurements performed by the MODIS sensor, thus we naturally chose them in the construction of this algorithm. In order to observe the performance efficacy of this algorithm we compare the retrieved results with the known fluorescence in our simulated datasets, and then we applied to the MODIS satellite data. Its efficacy is examined by comparing the retrieved fluorescence signal to that obtained through traditional FLH. 4.1 Performance with Simulated Datasets To carry out this comparison, we begin by applying the algorithm to our simulated datasets with φ f =0.5% in an attempt to quantify how accurately SICF can be retrieved for conditions typical of coastal waters. The retrieved fluorescence at 685nm using both our algorithms (dots) and FLH (circles) were plotted against the known fluorescence signal and the chlorophyll concentrations in figure 4a and 4b respectively. As shown in figure 4a, better estimation of fluorescence signal were obtained by RFH for fluorescence amplitude up to 0.1 W m -2 μm -1 sr -1 with correlation r 2 = between retrieved and known signals. The correlation becomes r 2 = when the φ f =1% (not shown). The estimation error of FLH becomes larger with increasing fluorescence amplitude. Also as observed in figure 4b, there is a close agreement between the two methods for low [Chl] (less than about 5mg). However as [Chl] increases the FLH algorithm underestimates the fluorescence signals significantly while the RFH performs better since the modulation of the elastic scattering signal in fluorescence region by chlorophyll absorption has been taken into account at least partially by connecting the elastic reflectance part in red channels to the blue-green ratio, a traditional proxy of [Chl]. Proc. of SPIE Vol

7 Figure 4: The retrieved fluorescence signal at 685nm (W m -2 μm -1 sr -1 ) using both our algorithm and FLH against a) known fluorescence b) chlorophyll concentrations. 4.2 Performance with satellite imagery We also applied the algorithm to a satellite image of MODIS of the Chesapeake Bay (May 3, 2005) that illustrates typical case 2 waters. The proposed retrieval is plotted against MODIS FLH in figure 6a and the percent difference defined as fl (685) (685) % 100 RFH fl diff = FLH (11) ( flrfh (685) + flflh (685)) / 2 between them is displayed as a function of MODIS product Chl-a in figure 6b. As we can see the two algorithms have a close agreement when it comes to small fluorescence intensities and a larger disagreement for moderate to large intensities, a trend similar to that of synthetic datasets. Their percent difference is an increasing function of [Chl]. The corresponding satellite images of FLH and retrieved fluorescence were shown side by side with the same color scale. The RFH is higher than that of FLH, especially in coastal region, where higher [Chl] is expected. This can be seen more clearly from the image of their percent difference in Fig. 7 (bottom left). The percent difference in the coastal regions varies from about 5% and in certain cases even exceeds 30%. Figure 6 (a) Proposed fluorescence vs. MODIS FLH in W m -2 μm -1 sr -1 projected at 685nm (b) The percent difference vs. MODIS Chl-a Proc. of SPIE Vol

8 Figure 7: Top left: FLH retrieval; Top right: RFH retrieval; bottom left: percent difference (unit: W m -2 μm -1 sr -1 ) All pixels with Rrs 667 >0.3% and R >1 are have been eliminated. 4. SUMMARY The elastic reflectance spectrum in the chlorophyll fluorescence band is often modulated by the chlorophyll absorption and its spectral shape is further away from a line with increasing chlorophyll concentration, leading to large errors of FLH algorithms. Taking advantage of an empirical correlation between the band ratio of and nm and that of elastic reflectance at 667 and 678nm we introduced a novel approach in the retrieval of Sun-Induced chlorophyll Fluorescence: RFH that gives a better estimation of the elastic radiance by incorporating the [Chl] dependency in the algorithm. When compared to more turbid waters with higher [Chl] the MODIS FLH doesn t give a signal similar to the proposed one mainly due to the fact that, it overestimates the true baseline. In these waters FLH can be used to indicate chlorophyll presence, but not to quantify it. Its performance in the quantification of the signal is a strong function of both [Chl] and φ f. In contrast, the performance of our proposed algorithm is a function mostly of the φ f and less of a function of [Chl]. ACKNOWLEDGMENTS This research was supported partially by grants from NASA, NOAA and ONR. Proc. of SPIE Vol

9 REFERENCES [1] Lorenzen, C. J. A method for the continuous measurement of the in vivo chlorophyll concentration, Deep-Sea Res., 13, (1966). [2] Morel, A. and Prieur, L. Analysis of variations in ocean color, Limnol. Oceanogr. 22, (1977). [3] O'Reilly, J. E., Maritorena, S., Mitchell, B. G., Siegel, D. A., Carder,K. L., Garver, S. A., Kahru, M. and McClain, C. Ocean color chlorophyll algorithms for SeaWiFS, J. Geophys. Res., 24, (1998). [4]Letelier, R.M. and Abbott, M. R. An Analysis of Chlorophyll Fluorescence Algorithms for the Moderate Resolution Imaging Spectrometer (MODIS), Remote Sens. Environ., 58, (1996). [5] Gower, J. F. R., Brown, L. and Borstad, G. A. Observation of chlorophyll fluorescence in west coast waters of Canada using the MODIS satellite sensor, Can. J. Rem. Sens., 30(1), (2004). [6] Huot, Y., Brown, C. A. and Cullen, J. J. New algorithms for MODIS sun-induced chlorophyll fluorescence and a comparison with present data products, Limnol. Oceanogr.: Methods, 3, (2005). [7]Gilerson, A., Zhou, J., Hlaing, S., Ioannou, I., Schalles, J., Gross, B., Moshary, F. and Ahmed, S., Fluorescence component in the reflectance spectra from coastal waters. Dependence on water composition, Opt. Express, 15 (24), (2007). [8] Lee, Z. P., [9]Gregg, W. and Carder, K. L. A simple spectral solar irradiance model for cloudless maritime atmospheres, Limnol. Oceanogr. 35, (1990). [10]Ciotti, A. M., Lewis, M. R., and Cullen, J. J., Assessment of the relationships between dominant cell size in natural phytoplankton communities and the spectral shape of the absorption coefficient, Limnol. Oceanogr., 47(2), (2002). [11]Gordon, H. R., Brown, O. B., Evans, R. H., Brown, J. W., Smith, R. C., Baker, K. S. and Clark, D. K., A semianalytic radiance model of ocean color, J. Geophys. Res., 93, (1988). Proc. of SPIE Vol