MERIS PERFORMANCE IN THE EAST CHINA SEAS: EVALUATION OF ATMOSPHERIC CORRECTION AND OPTICAL INVERSION ALGORITHMS

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MERIS PERFORMANCE IN THE EAST CHINA SEAS: EVALUATION OF ATMOSPHERIC CORRECTION AND OPTICAL INVERSION ALGORITHMS Ming-Xia HE 1, Shuangyan He 1, Lianbo Hu 1, Yunfei Wang 1, Qian Yang 1, Tinglu Zhang 1

Weltraumwissenschaften, Berlin, Germany 3 Naval Research Laboratory, Code 7300,Stennis Space Center, MS 39529, U.S.A. 4 College of Marine Science, University of South Florida, St.

1 MERIS PERFORMANCE IN THE EAST CHINA SEAS: EVALUATION OF ATMOSPHERIC CORRECTION AND OPTICAL INVERSION ALGORITHMS Ming-Xia HE 1, Shuangyan He 1, Lianbo Hu 1, Yunfei Wang 1, Qian Yang 1, Tinglu Zhang 1 Wenzhong Chen 1, Jüergen Fischer 2, Zhongping Lee 3, Chuanmin Hu 1,4 1 Ocean Remote Sensing Institute, Ocean University of China, Qingdao , China 2 Freie Universitaet Berlin, Institut fuer Weltraumwissenschaften, Berlin, Germany 3 Naval Research Laboratory, Code 7300,Stennis Space Center, MS 39529, U.S.A. 4 College of Marine Science, University of South Florida, St. Petersburg, FL 33701, U.S.A. Abstract Spectral remote sensing reflectance in the visible (Rrs(λ), sr -1 ) was derived from ENVISAT MERIS data over the East China Seas, and compared with those determined from concurrent in situ measurements between 2003 and The results showed that both the FUB plug-in module and the standard MERIS Level-2 data generally overestimated Rrs(λ), particularly in the blue, while FUB plug-in shows a significant improvement in accuracy compared to the standard MERIS L2 data. The MERIS and in situ Rrs(λ) were respectively used as input to a quasi-analytical algorithm to derive absorption coefficients of the various water constituents (phytoplankton, colored organic matter and detritus), and compared with those determined in situ. There was slight improvement in the FUB-derived absorption as compared with that from the MERIS L2 data. The derived absorption from in situ Rrs(λ) were apparently improved compared with those derived from MERIS Rrs(λ). The Rrs(λ)-based algorithm for the retrieval of diffuse attenuation coefficient, K490 (m -1 ), was adjusted to include a term Rrs(670), which showed the better results. derive inherent optical properties (IOP) besides the Rrs and bio-optical parameters. The 2006 MAVT Validation workshop evaluated MERIS atmospheric correction and bio-optical inversion algorithms, which need to be improved in both Case 1 and Case 2 waters compared with in situ measurements. Compared to the MERIS Level 2 standard reflectance product, the FUB plug-in shows a significant improvement in accuracy, especially in the blue [7]. Recently studies also show that Rrs derived from the standard MERIS Level-2 data, agreed reasonably well with in situ measurements for coastal waters around the United States [4]. However, MERIS data performance for Chinese marginal waters, where the atmosphere is rather turbid and the optical properties of the ocean are rather complex (Fig. 1), is unknown. Bohai Sea Yellow Sea 1. INTRODUCTION Since ENVISAT was launched in March 2002 by ESA, MERIS has shown its unique potentials in algal blooms, floating vegetation, Case 2 waters optical properties and primary production [1,2,3,4]. This is because of the spectral bands at 709 nm and 620 nm, which are not available on other ocean color sensors, for example MODIS or SeaWiFS. In particular MERIS aboard ENVISAT with AATSR, ASAR and RA-2 provides a unique opportunity on oceanography applications. Operational MERIS level 2 products focus on global application, and FUB, GKSS developed BEAM plug-in modules in order to improve the retrieval results in Case 2 waters [5,6,7,8]. FUB plug-in is based on inverse modelling of radiative transfer caculations using artificial neural network techniques. The GKSS plug-in can also East Sea Figure 1.MERIS RGB composite image on 15 February 2004 showing sunglint (lower right corner) and complex features in both the atmosphere and East China Seas. Here, using data collected from several cruises conducted between 2003 and 2007 in the East China Seas, we evaluate the performance of the MERIS atmospheric correction algorithms and an optical inversion algorithm. Proc. Envisat Symposium 2007, Montreux, Switzerland April 2007 (ESA SP-636, July 2007)

2 Ocean optical in situ experiments show that QAA performs well in the East China Seas. So MERIS Rrs(λ) is used as input to quasi-analytical algorithm (QAA) to derive the absorption coefficients of the various water constituents, and then compare with in situ measurements[9]. The Rrs(λ)-based algorithm for the retrieval of diffuse attenuation coefficient, K490 (m -1 ), was presented for Case 2 waters, which includes a new term Rrs(670) compared with Muller s algorithm [10]. This algorithm shows better results in the East China Seas. Bio-geochemical parameters, such as chlorophyll concentration, suspended particulate matter concentration and so on, are being processed, therefore these parameters are not evaluated in this paper. These results are preliminary from this extensive dataset, and more indepth analysis and results will be published in the future. 2. DATA and METHOD Five field experiments were conducted between 2003 and 2007 in the East China Seas, which include the Bohai Sea, the Yellow Sea, and the East Sea (Fig. 1). During each cruise, Rrs(λ) was measured using Hyper TSRB (Satlantic Co., Canada). The profiles of Lu(λ) and Ed(λ) were measured using hyperspectral instruments Trios (Trios Inc., Germany). Total absorption and attenuation coefficients were measured with the AC-S instrument (Wetlabs, USA). Absorption coefficients of phytoplankton, detrital particles, and colored dissolved organic matter (CDOM, also called Gelbstoff) were measured with standard filtration techniques. Aerosol optical thickness was measured by Skyradiometer POM- 01MKⅡ (PREDE Co., Ltd Japan). Both Level-1b and Level-2 reduced-resolution (RR) MERIS data over the study region were obtained through the ESA Earth Observation Missions. The Level-2 data contained spectral Rrs(λ) in the visible, a product from the default atmospheric correction [11]. The Level-1b data were processed using the FUB plug-in module [5,6,7] to correct for the atmosphere to derive the spectral Rrs(λ). The data were then mapped to a standard cylindrical projection to facilitate visualization and comparison with in situ data. MERIS Rrs of 26 match-up stations was compared with in-situ measurements, and the MERIS observations are mean Rrs computed for 3*3 pixel regions centered on in situ stations. The time differences are mostly less than 3 hours. 3. EVALUATION OF MERIS ATMOSPHERIC CORRECTION Fig. 3 shows that the accuracy of the MERIS Rrs(λ) varied substantially from location to location. Although further diagnose is required to pin point the reasons of the discrepancies, Fig. 4 shows the statistics of the Rrs(λ) P8-1 Rrs Comparison MER_FUB G40 Rrs Comparison MER_FUB C804 Rrs Comparison MER_FUB WFJ202 Rrs Comparison 0.05 MER_FUB 0.04 Figure 2. Cruise track and station locations of the SOLAS-China ocean survey between and in the Yellow Sea. Four other cruise surveys between 2003 and 2007 were conducted in the Yellow Sea, and the East Sea (Cruise maps are omitted) Figure 3. Comparison of Rrs(λ) from in situ measurement (blue), MERIS data from the FUB-plug-in (pink), and MERIS standard L2 data (red) in the East Sea clear water (top left), Yellow Sea intermediate water (top right), Dalian coastal water (lower left), and Jiangsu nearshore water (lower right) from various cruises. comparison for all 26 matchup stations. Clearly, both methods (FUB-plug-in and MERIS default atmospheric correction) overestimated Rrs(λ), particularly in the high

3 range. However, FUB generally performed better than the default correction, with average relative errors (AvRE) of 46% versus 71%. The results shown here suggest that the atmosphere and water are more complex, and substantial work is required to improve atmospheric correction in these regions. to the combined effect of atmospheric correction and optical inversion. Figure 4. Comparison of Rrs(λ) from in situ TSRB measurements, MERIS data from the FUB-plug-in module (top), and MERIS standard L2 data for 26 stations. 4. EVALUATION OF MERIS OPTICAL PARA- METERS BY QAA Because that MERIS Rrs(λ) from the FUB-plug-in was generally better than the default MERIS L2 Rrs(λ), we only used the former as the input of the quasi-analytical algorithm (QAA) to retrieve the various absorption coefficients. Fig. 5 shows the comparison between the retrieved a_t (total absorption), a_dg (absorption by detrital particles and Gelbstoff), a_ph (absorption by phytoplankton pigments) at 7 visible wavelengths for the 16 stations where both Rrs(λ) and absorption were measured. Average relative errors are 27%, 83% and 1325%, respectively. Most of the errors in a_ph were due Figure 5. Comparison between MERIS-derived and in situ measured total absorption coefficient (a_t, top), absorption coefficient of detrital particles and Gelbstoff (a_dg, middle), and absorption coefficient of phytoplankton pigments (a_ph, bottom).

To evaluate how much error was from the inversion algorithm alone, in situ Rrs(λ) data were used as the input of the QAA algorithm, and similar comparison was obtained (Fig. 6 and Tab. 1).

4 To evaluate how much error was from the inversion algorithm alone, in situ Rrs(λ) data were used as the input of the QAA algorithm, and similar comparison was obtained (Fig. 6 and Tab. 1). Clearly, QAA was robust in retrieving a_t for a dynamic range over one order, and the a_dg retrieval was also successful except for a few outliers where the reasons need to be identified in the future. Errors in the retrieved a_dg were smaller in the blue-green wavelengths ( 560 nm) than in the red wavelengths; this is possibly due to the fact that a_dg decrease exponentially with increasing wavelengths, resulting in larger relative errors. In contrast, much larger, wavelength-independent uncertainties existed in the retrieved a_ph. Compared with Fig. 5, the results in Fig. 6 suggest that most of the errors in the MERIS-derived absorption coefficients, particularly a_t and a_dg, must have resulted from the MERIS atmospheric correction, as opposed to the optical inversion. Further examination of the mean relative contribution of the various water constituents to the total absorption (Fig. 7) suggests that phytoplankton absorption contributed only <10% of the total absorption except in the red. This appears to be the reason why the retrieval of a_ph is inherently difficult and thus less satisfactory (Fig. 6, bottom). N=66 AvRE=27.7% R 2 =0.94 QAA_derived a dg (440)(m -1 ) Relative Contribution CDOM Detritus Phytoplankton Measured a dg (440)(m -1 ) QAA_derived a ph (440)(m -1 ) N=66 AvRE=56.7% R 2 =0.51 Measured a ph (440)(m -1 ) Figure 6. Same as Fig. 5, but in situ Rrs(λ) instead of MERIS Rrs(λ) was used as the input of the QAA inversion. For clarity a_dg and a_ph at 440 nm only are shown in the last two panels. Figure 7. Mean relative contribution of the absorption coefficients of detrital particles, CDOM, and phytoplankton to total absorption coefficient in the East China Seas (N=197), based on in situ measurements. Fig. 7 clearly shows that the optical properties of the East China Seas are dominated by non-living detrital particles (organic and inorganic) rather than by CDOM or phytoplankton pigments. This is in contrast to the deep, clear ocean where a_d (440) is only 10-20% of a_g (440) [14]. This is also different from other major river plumes such as the Orinoco River plume [15], where CDOM absorption (a_g) dominates. Therefore, estimating of phytoplankton absorption and the corresponding chlorophyll concentration is inherently difficult with traditional methods (i.e., methods based on absorption,

5 Table 1. Average relative errors in the QAA retrieved absorption coefficients for the MERIS wavelengths, based on in situ Rrs(λ) at 16 stations. The second number shows the correlation coefficient between the retrieved and measured parameters. a_t (m -1 ) a_dg (m -1 ) a_ph (m -1 ) 412nm 442nm 490nm 510nm 560nm 620nm 665nm 14.2% 21.4% 25.3% 26.1% 22.6% 10.7% 4.9% % 39.8% 39.9% 39.6% 42.1% 74.3% 60.5% % 165.5% % 179.0% 272.2% 513.4% 411.3% usually from the blue-green bands) or with the recently proposed band-ratio method in the near-ir. An alternative approach using solar stimulated chlorophyll fluorescence [12] might be more feasible, and therefore deserves more research. 5. K490 RETRIEVAL The diffuse attenuation coefficient at 490 nm, K490 (m -1 ), is a measure of how fast light disappears with increasing depths and therefore an index for water clarity. Traditionally, it is estimated using blue-green band ratios [10,13], based on the assumption that optical properties are dominated by phytoplankton and its degradation products (i.e., Case-I waters). Fig. 7 shows that the waters in the study region are not Case-I. Therefore, the band-ratio algorithm may be invalid for high K490 waters as demonstrated in Fig. 8 (top). In contrast, there was a tight correlation between K490 and Rrs(670) (Fig. 8, bottom), suggesting that the water was scattering dominated. This is in consistent with the results shown in Fig. 7. Based on Fig. 8, a modification to the Muller algorithm was applied by adding a new term Rrs(670): m1 R (490) rs K ( 490) = Kw(490) + m0 m2 Rrs (670) R (555) + rs where the coefficients were determined by non-linear regression. Fig. 9 and Tab. 2 show that the new algorithm, Derived K(490) (m -1 ) (b) Measured K(490) (m -1 ) Figure 9. Comparison between measured and Rrsderived K490 using several algorithms. Muller Morel Lee This Study AvRE 51.3% 59.4% 16.6% 13.7% R 2 (N=75) Figure 8. Top: Relationship between K490 and Rrs band ratio; Bottom: Relationship between K490 and Rrs(670). Table 2. Statistics of the K490 retrieval algorithms, as shown in Fig. 9.

6 based on regional tuning, resulted in much improved K490 retrieval than any other existing algorithms, although the performance of the Lee et al.(2002) QAA algorithm was similar. 6. SUMMARY AND CONCLUSION Although MERIS performance has been reported generally satisfactory in deep, clear waters as well as in coastal waters, uncertainties existed in the data products for the East China Seas. Generally, the FUB-plug-in performed better than the MERIS default atmospheric correction in estimating the spectral Rrs. This study shows that the performance of applying QAA algorithm to derive the absorption coefficients of the various water constituents is satisfactory in the East China Seas, especially robust in retrieving the total absorption coefficients. From the preliminary results shown above, it is clear that there are two primary reasons that led to the uncertainties in the retrieved data products: 1) inaccurate atmospheric correction, which led to errors in the retrieved Rrs(λ); 2) optical properties are not dominated by phytoplankton or CDOM absorption, but rather by absorption and scattering by detrital particles (organic and inorganic). Although the former can be potentially improved by taking into account of more aerosol types (e.g., absorbing aerosols) and aerosol vertical distributions, the latter creates inherent difficulty in estimating chlorophyll concentration using traditional techniques. However, this problem might be circumvented by using solar stimulated chlorophyll fluorescence, which is available on the MERIS and MODIS sensors. A K490 new algorithm based on Rrs resulted in much improvement than any other existing algorithms in the East China Seas, which considers turbid waters dominated by scattering. The results shown here are preliminary. More in-depth analysis on the exact reasons that led to the errors in the retrieved Rrs and outliers in the optical data products is required in future work. 7. ACKNOWLEDGEMENT This work was supported by NSFC Key Project on Ocean Optics and ESA-MOST DRAGON Program (Ocean Project ID2566), and the DRAGON Program provides MERIS data. Thank SOLAS-China Project, 908-China Ocean Survey Project, MOST 973 East China Sea Physical Oceanography Project, NSFC Red Tide Project for providing the cruises for in situ experiments. 8. REFERENCES 1.Gower, J., C. Hu, G. Borstad & S. King (2006). Ocean color satellites show extensive lines of floating Sargassum in the Gulf of Mexico. IEEE Trans. Geosci. Remote Sens. 44: Gower, Jim, King, Stephanie & Goncalves, Pedro (this volume). A Global Survey of Intense Surface Plankton Blooms using MERIS MCI. 3.Park, Young Je, Ruddick, Kevin (this volume). Detecting Algae Blooms in European Waters. 4.Lee, Z. P., C. Hu, D. Gray, B. Casey, R. Arnone, A. Weidemann, R. Ray & W. Goode (this volume). MERISderived bio-optical properties of the US coastal waters. 5.Fischer J. & Grassl H. (1984). Radiative transfer in an atmosphere-ocean system: an azimuthally dependent matrixoperator approach, Applied Optics, 23: Fell F. & Fischer J. (2001). Numerical simulation of the light field in the atmosphere-ocean system using the matrixoperator method, Journal of Quantitative Spectroscopy & Radiative Transfer, 69: Th. Schroeder, I. Behnert, M. Schaale, J. Fischer a, R. Doerffer (2007). Atmospheric correction algorithm for MERIS above case-2 waters. International Journal of Remote Sensing, Volume 28, Issue 7, pages R. Doerffer and H. Schiller (2007). The MERIS Case 2 water algorithm. International Journal of Remote Sensing, Volume 28, Issue 3 & 4, pages Lee, Z.P., K.L. Carder & R. Arnone (2002). Deriving inherent optical properties from water color: A multi-band quasianalytical algorithm for optically deep waters. Applied Optics, 41: p Mueller, J. L. (2000). SeaWiFS algorithm for the diffuse attenuation coefficient, K(490), using water-leaving radiances at 490 and 555 nm, in SeaWiFS Postlaunch Calibration and Validation Analyses, part 3, edited by S. B. Hooker, pp , NASA Goddard Space Flight Cent., Greenbelt, Md. 11.Antoine, D., and A. Morel (2005). MERIS ATBD 2.7. Atmospheric Correction of the MERIS observations Over Ocean Case 1 waters. Issue 5, revision pdf 12.Hu, C., F. E. Muller-Karger, C. Taylor, K. L. Carder, C. Kelble, E. Johns, and C. Heil (2005). Red tide detection and tracing using MODIS fluorescence data: A regional example in SW Florida coastal waters. Remote Sens. Environ., 97: Austin, R. W. & T. J. Petzold (1981). The determination of the diffuse attenuation coefficient of sea water using the coastal zone color scanner, in Oceanography From Space, edited by J. F. R. Gower, pp , Springer, New York. 14.Nelson, N. B., D. A. Siegel & A. F. Michaels (1998). Seasonal dynamics of colored dissolved material in the Sargasso Sea. Deep Sea Res. I. 45: Odriozola, A. L., R. Varela, C. Hu, Y. Astor, L. Lorenzoni, and F. E. Muller-Karger (2007). On the absorption of light in the Orinoco River plume. Cont. Shelf Res. In press.

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