Korean Journal of Remote Sensing, Vol.26, No.2, 2010, pp.189~207 Development of Ocean Environmental Algorithms for Geostationary Ocean Color Imager (GOCI) Jeong-Eon Moon*, **, Yu-Hwan Ahn*, Joo-Hyung Ryu*, and Palanisamy Shanmugam*** *Korea Ocean Satellite Center, KORDI, **Department of Oceanography, Inha University ***Department of Ocean Engineering, IIT Madras, India Abstract : Several ocean color algorithms have been developed for GOCI (Geostationary Ocean Color Imager) using in-situ bio-optical data sets. These data sets collected around the Korean Peninsula between 1998 and 2009 include chlorophyll-a concentration (Chl-a), suspended sediment concentration (SS), absorption coefficient of dissolved organic matter (a dom ), and remote sensing reflectance (R rs ) obtained from 1348 points. The GOCI Chl-a algorithm was developed using a 4-band remote sensing reflectance ratio that account for the influence of suspended sediment and dissolved organic matter. The GOCI Chl-a algorithm reproduced in-situ chlorophyll concentration better than the other algorithms. In the SeaWiFS images, this algorithm reduced an average error of 46 % in chlorophyll concentration retrieved by standard chlorophyll algorithms of SeaWiFS. For the GOCI SS algorithm, a single band was used (Ahn et al., 2001) instead of a band ratio that is commonly used in chlorophyll algorithms. The GOCI a dom algorithm was derived from the relationship between remote sensing reflectance band ratio (R rs (412)/R rs (555)) and a dom (l). The GOCI Chl-a fluorescence and GOCI red tide algorithms were developed by Ahn and Shanmugam (2007) and Ahn and Shanmugam (2006), respectively. If the launch of GOCI in June 2010 is successful, then the developed algorithms will be analyzed in the GOCI CAL/VAL processes, and improved by incorporating more data sets of the ocean optical properties data that will be obtained from waters around the Korean Peninsula. Key Words : GOCI, Ocean Color, Chlorophyll, SS, DOM. a dom R rs yhahn@kordi.re.kr 189
Korean Journal of Remote Sensing, Vol.26, No.2, 2010 a dom R rs R rs a dom l 190
Development of Ocean Environmental Algorithms for Geostationary Ocean Color Imager (GOCI) dinoflagellate Ptychodiscus brevis Karenia brevis 191
Korean Journal of Remote Sensing, Vol.26, No.2, 2010 a dom Chl-a (mg/m 3 ) = 1.8528R _ 3.263 (1) Fig. 1. The observation map. 192
Development of Ocean Environmental Algorithms for Geostationary Ocean Color Imager (GOCI) Table 1. The observation schedule during 1998-2009 no. Sampling Platform Time Period Sampling Location Number of Stations Visited 193
Korean Journal of Remote Sensing, Vol.26, No.2, 2010 Fig. 2. The relationships between in-situ chlorophyll-a concentration (Chl-a) and 2-bands remote sensing reflectance (R rs ) ratios. (a) Chl-a and R rs (443)/R rs (555), (b) Chl-a and R rs (490)/R rs (555). Fig. 3. The relationship between in-situ chlorophyll-a concentration (Chl-a) and 4-bands remote sensing reflectance ratio. {R rs (443) + R rs (490)} _ R rs (412) where, R = R rs (555) Table 2. The comparisons for chlorophyll-a algorithms Name Equation Reference RMSE OC2v2 10 (0.2974_ 2.2429R+0.8358R 2_ 0.0077R 3 ) _ 0.0929 R = log 10( R rs 490 R rs 555 ) O Reilly et al. (1998) 0.28 OC4v4 (SeaWiFS standard) YOC Chl-a GOCI Chl-a 10 (0.366_ 3.067R+1.930R 2 +0.649R 3_ 1.532R 4 ) R = log 10( Max(R rs 443, R rs 490, R rs 510) R rs 555 ) 10 (0.25484_ 3.12684R+0.14715R 2 ) R = log 10[( R rs 443 R )( rs 412 R rs 555 R rs 490 1.8528R _ 3.263 ) _ 0.8 R = (R rs443 + R rs 490) _ R rs 412 R rs 555 ] O Reilly et al. (1998) 0.30 Siswanto et al. (2010) 0.23 In this Study 0.19 194
Development of Ocean Environmental Algorithms for Geostationary Ocean Color Imager (GOCI) Fig. 4. The relationships between in-situ chlorophyll-a concentration (Chl-a) and Chl-a obtained from algorithms. (a) GOCI Chl-a algorithm, (b) YOC Chl-a algorithm, (c) OC4v4 algorithm, (d) OC2v2 algorithm. Fig. 5. The comparison for chlorophyll distribution images obtained from GOCI Chl-a, YOC Chl-a, OC4v4 and OC2v2 algorithms using SeaWiFS data in March 19, 2006. 195 Table 3. The comparisons for chlorophyll-a concentration obtained from red box area of Fig. 5 GOCI Chl-a YOC Chl-a OC4v4 OC2v2 Average 1.18 mg/m 3 1.29 mg/m 3 2.48 mg/m 3 1.96 mg/m 3 vs. OC4v4 53 % 48 % decrease decrease 40 % 35 % vs. OC2v2 decrease decrease Selection Area: Lat. 33 _ 34, Long. 124 _ 125 (Red Box of Fig. 5)
Korean Journal of Remote Sensing, Vol.26, No.2, 2010 Fig. 7. The relationship between in-situ suspended sediment concentration (SS) and remote sensing reflectance at 555 nm (R rs (555)). Fig. 6. The relationships between in-situ suspended sediment concentration (SS) and 2-bands remote sensing reflectance (R rs ) ratios. (a) SS and R rs (412)/R rs (555), (b) SS and R rs (443)/R rs (555), (c) SS and R rs (490)/R rs (555). 196
Development of Ocean Environmental Algorithms for Geostationary Ocean Color Imager (GOCI) SS(g/m 3 ) = 945.07(R rs (555)) 1.137 (2) Table 4. The comparisons for suspended sediment algorithms Name Equation Reference RMSE SS = 10 0.51897_ 2.24106R+1.20113R 2_ 4.35315R 3 +9.07162R 4_ 5.10552R Clark 5 TSM R = log 10( nl w (412) + nl w (443) MODIS ATBD (1997) 0.61 (include SeaDAS) nl w (510) ) YOC TSM SS = 10 0.73789+22.7885R1_ 0.57437R2 R1 = R rs (555) + R rs (670) R rs (490) R2 = R rs(555) Siswanto et al. (2010) 0.33 GOCI SS SS = 945.07R 1.137 R = R rs (555) In this Study 0.28 Fig. 8. The relationships between in-situ suspended sediment concentration (SS) and SS obtained from algorithms. (a) Clark TSM algorithm, (b) YOC TSM algorithm, (c) GOCI SS algorithm. 197
Korean Journal of Remote Sensing, Vol.26, No.2, 2010 a dom a dom a dom a dom a dom a dom a dom a dom a dom a dom (400)[m _1 ] = 0.2355R _ 1.3423 (3) a dom (412)[m _1 ] = 0.2047R _ 1.3351 (4) Fig. 9. The relationships between in-situ absorption coefficients of dissolved organic matter (a dom ) with wavelengths and 2-bands remote sensing reflectance (R rs ) ratios. (a) a dom (400) and R rs (412)/Rrs(555), (b) a dom (400) and R rs (443)/R rs (555), (c) a dom (400) and R rs (490)/R rs (555), (d) a dom (412) and R rs (412)/R rs (555), (e) a dom (412) and R rs (443)/R rs (555), (f) a dom (412) and R rs (490)/R rs (555). 198
Development of Ocean Environmental Algorithms for Geostationary Ocean Color Imager (GOCI) R rs (412) where, R = R rs (555) 1 a dom (l) S = ( ) ln ( ) (5) l _ l 0 a dom (l 0 ) 1 a dom (412) S = ( ) ln ( ) (6) 12 a dom (400) a dom l a dom a dom Table 5. The comparisons for absorption coefficient of dissolved organic matter algorithms Name Equation Reference RMSE a dom (400) = 0.2355R _ 1.3423 GOCI a R rs (412) dom (400) In this Study 0.18 R = R rs(555) GOCI a dom (412) YOC a dom (440) a dom (412) = 0.2047R _ 1.3351 R rs (412) R = R rs(555) a dom (440) = 10 _ 1.11529 _ 1.38942R+0.51803R 2 R = log 10[( R rs (490) R )(R rs(443)) ] 0.1 rs(555) In this Study 0.18 Siswanto et al. (2010) 0.24 Fig. 10. The relationships between in-situ absorption coefficients of dissolved organic matter (a dom ) and a dom obtained from algorithms. (a) developed GOCI a dom (400) algorithm in this study, (b) developed GOCI a dom (412) algorithm in this study, (c) YOC a dom (440) algorithm. 199
Korean Journal of Remote Sensing, Vol.26, No.2, 2010 a dom Fig. 11. Schematic representation of the fluorescence line height (DFlu) estimation using the remote sensing reflectance spectrum (from Ahn and Shanmugam, 2007). D D DFlu = XY = CX _ YC (7) DFlu = XY = CX _ [( R L (l _ (F) l (S) )) _ R S l _ (L) l (S) + R S] (8) l l l D 730 DFlu (Area) = DFlu(l)dl (9) 600 D Fig. 12. Relationships between in-situ chlorophyll concentrations and (a) DFlu(681) and (b) DFlu (area). The solid line is the best-fit regression to our bio-optical dataset (N=118) (from Ahn and Shanmugam, 2007). 200
Development of Ocean Environmental Algorithms for Geostationary Ocean Color Imager (GOCI) Table 6. The regression equations and squared correlation coefficients obtained for the fluorescence algorithms (from Ahn and Shanmugam, 2007) Algorithms Correlation coefficient (r (r 2 ) ) Chl-a = 605908[DFlu(681)] 1.48 0.88 Chl-a = 4142.3[DFlu(area)] 1.46 0.90 D D D D D D Fig. 14. The diagram for relationship among redtide index, chlorophyll-a concentration and suspended solid particle concentration. Fig. 15. (a) Scatterplot of RI calculated using Eq.(10) versus L w at 443 nm for data N=25. (b) Scatterplot of RI calculated using Eq.(10) versus L w at 443 nm for data N=375. Note that RI progressively increased with the decrease of L w at 443 nm (from Ahn and Shanmugam, 2006). Fig. 13. Comparison between the measured and predicted chlorophyll-a concentrations (from Ahn and Shanmugam, 2007). 201
Korean Journal of Remote Sensing, Vol.26, No.2, 2010 [L w (510)/L w (555) _ L w (443)] RI = (10) [L w (510)/L w (555) + L w (443)] RI (D1) = 10 (_ 2.4394 X 3 +5.2587 X 2_ 4.117 X+0.8782) (11) RI (D1) = 10 (_ 0.1069 X 3 +0.6259 X 2_ 1.3936 X+0.919) (12) Fig. 16. Comparison between OC4-Chl-a (a) and RI (b) from SeaWiFS image of 19 September 2000 in the Korea South Sea. The (c) image is red tide information obtained from NFRDI in same date (from Ahn and Shanmugam, 2006). 202
Development of Ocean Environmental Algorithms for Geostationary Ocean Color Imager (GOCI) a dom l a dom a dom a dom a dom a dom a dom 203
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