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http://dx.doi.org/10.7780/kjrs.2021.37.5.2.7

GOCI-II Capability of Improving the Accuracy of Ocean Color Products through Fusion with GK-2A/AMI  

Lee, Kyeong-Sang (Korea Institute of Ocean Science & Technology)
Ahn, Jae-Hyun (Korea Institute of Ocean Science & Technology)
Park, Myung-Sook (Korea Institute of Ocean Science & Technology)
Publication Information
Korean Journal of Remote Sensing / v.37, no.5_2, 2021 , pp. 1295-1305 More about this Journal
Abstract
Satellite-derived ocean color products are required to effectively monitor clear open ocean and coastal water regions for various research fields. For this purpose, accurate correction of atmospheric effect is essential. Currently, the Geostationary Ocean Color Imager (GOCI)-II ground segment uses the reanalysis of meteorological fields such as European Centre for Medium-Range Weather Forecasts (ECMWF) or National Centers for Environmental Prediction (NCEP) to correct gas absorption by water vapor and ozone. In this process, uncertainties may occur due to the low spatiotemporal resolution of the meteorological data. In this study, we develop water vapor absorption correction model for the GK-2 combined GOCI-II atmospheric correction using Advanced Meteorological Imager (AMI) total precipitable water (TPW) information through radiative transfer model simulations. Also, we investigate the impact of the developed model on GOCI products. Overall, the errors with and without water vapor absorption correction in the top-of-atmosphere (TOA) reflectance at 620 nm and 680 nm are only 1.3% and 0.27%, indicating that there is no significant effect by the water vapor absorption model. However, the GK-2A combined water vapor absorption model has the large impacts at the 709 nm channel, as revealing error of 6 to 15% depending on the solar zenith angle and the TPW. We also found more significant impacts of the GK-2 combined water vapor absorption model on Rayleigh-corrected reflectance at all GOCI-II spectral bands. The errors generated from the TOA reflectance is greatly amplified, showing a large error of 1.46~4.98, 7.53~19.53, 0.25~0.64, 14.74~40.5, 8.2~18.56, 5.7~11.9% for from 620 nm to 865 nm, repectively, depending on the SZA. This study emphasizes the water vapor correction model can affect the accuracy and stability of ocean color products, and implies that the accuracy of GOCI-II ocean color products can be improved through fusion with GK-2A/AMI.
Keywords
GOCI-II; GK-2A/AMI; ocean color; fusion; water vapor correction;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 He, X., Y. Bai, D. Pan, J. Tang, and D. Wang, 2012. Atmospheric correction of satellite ocean color imagery using the ultraviolet wavelength for highly turbid waters, Optics Express, 20(18): 20754-20770.   DOI
2 Ahmad, Z., C.R. McClain, J.R. Herman, B.A. Franz, E.J. Kwiatkowska, W.D. Robinson, E.J. Bucsela, and M. Tzortziou, 2007. Atmospheric correction for NO 2 absorption in retrieving water-leaving reflectances from the SeaWiFS and MODIS measurements, Applied Optics, 46(26): 6504-6512.   DOI
3 Ahn, J.H., Y.J. Park, J.H. Ryu, B. Lee, and I.S. Oh, 2012. Development of atmospheric correction algorithm for Geostationary Ocean Color Imager (GOCI), Ocean Science Journal, 47(3): 247-259.   DOI
4 Blondeau-Patissier, D., J.F. Gower, A.G. Dekker, S.R. Phinn, and V.E. Brando, 2014. A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans, Progress in Oceanography, 123: 123-144.   DOI
5 Han, H.-J., H. Yang, J.-M. Heo, and Y.-J. Park, 2016. Systemic Ground-Segment Development for the Geostationary Ocean Color Image II, GOCI-II, KIIS Transactions on Computing Practices, 23(3): 171-176
6 Choi, J.-K., J.-H. Ahn, Y.B. Son, D.-J. Hwang, and S.-J. Lee, 2020, Application of GOCI to the Estimates of Primary Productivity in the Coastal Waters of the East Sea, Korean Journal of Remote Sensing, 36(2-2), 237-247 (in Korean with English abstract).   DOI
7 Choi, Y.S. and C.H. Ho, 2015. Earth and environmental remote sensing community in South Korea: A review, Remote Sensing Applications: Society and Environment, 2: 66-76.   DOI
8 Fan, Y., W. Li, N. Chen, J.H. Ahn, Y.J. Park, S. Kratzer, T. Schroeder, J. Ishizaka, R. Chang, and K. Stamnes, 2021. OC-SMART: A machine learning based data analysis platform for satellite ocean color sensors, Remote Sensing of Environment, 253: 112236.   DOI
9 Lee, K.-S., 2019. Atmospheric Correction Issues of Optical Imagery in Land Remote Sensing, Korean Journal of Remote Sensing, 35(6-3), 1299-1312 (in Korean with English abstract).   DOI
10 NMSC (National Meteorological Satellite Center), 2019. GK-2A Algorithm Theoretical Basis Document AMI Atmospheric Profile, available at https://nmsc.kma.go.kr/homepage/html/base/cmm/selectPage.do?page=static.edu.atbdGk2a, Accessed on Apr. 15, 2019.
11 Orcutt, J., 2013. Earth System Monitoring, Introduction, Earth system monitoring, Springer, New York, NY, USA.
12 Pereira-Sandoval, M., A. Ruescas, P. Urrego, A. Ruiz-Verdu, J. Delegido, C. Tenjo, X. Soria-Perpinya, E. Vincente, J. Soria, and J. Moreno, 2019. Evaluation of atmospheric correction algorithms over Spanish inland waters for sentinel-2 multi spectral imagery data, Remote Sensing, 11(12): 1469.   DOI
13 Tzortziou, M., J.R. Herman, Z. Ahmad, C.P. Loughner, N. Abuhassan, and A. Cede, 2014. Atmospheric NO2 dynamics and impact on ocean color retrievals in urban nearshore regions, Journal of Geophysical Research: Oceans, 119(6): 3834-3854.   DOI
14 Li, H., X. He, Y. Bai, P. Shanmugam, Y.J. Park, J. Liu, Q. Zhu, F. Gong, D. Wang, and H. Huang, 2020. Atmospheric correction of geostationary satellite ocean color data under high solar zenith angles in open oceans, Remote Sensing of Environment, 249: 112022.   DOI
15 Werdell, P.J. and C.R. McClain, 2019. Satellite Remote Sensing: Ocean Color, Encyclopedia of Ocean Sciences (Third Edition), Academic Press, Sandigo CA, USA.
16 Lee, S.J., M.H. Ahn, and S.R. Chung, 2017. Atmospheric profile retrieval algorithm for next generation geostationary satellite of Korea and its application to the advanced Himawari Imager, Remote Sensing, 9(12): 1294.   DOI
17 Ahn, Y.H., 2000, Development of Remote Sensing Reflectance and Water Leaving Radiance Models for Ocean Color Remote Sensing Technique, Korea Journal of Remote Sensing, 16(3): 243-260 (in Korean with English abstract).   DOI
18 Choi, W.J., K.J. Moon, J. Yoon, A. Cho, S.K. Kim, S. Lee, D.H. Ko, J. Kim, M.H. Ahn, D.-R. Kim, S.-M. Kim J.-Y. Kim, D. Nicks, and J.S. Kim, 2018. Introducing the geostationary environment monitoring spectrometer, Journal of Applied Remote Sensing, 12(4): 044005.
19 Ferreira, A., V. Brotas, C. Palma, C. Borges, and A.C. Brito, 2021. Assessing Phytoplankton Bloom Phenology in Upwelling-Influenced Regions Using Ocean Color Remote Sensing, Remote Sensing, 13(4): 675.   DOI
20 Kim, D., M. Gu, T.H. Oh, E.K. Kim, and H.J. Yang, 2021. Introduction of the Advanced Meteorological Imager of Geo-Kompsat-2a: In-Orbit Tests and Performance Validation, Remote Sensing, 13(7): 1303.   DOI
21 Pahlevan, N., S. Sarkar, and B.A. Franz, 2016. Uncertainties in coastal ocean color products: Impacts of spatial sampling, Remote Sensing of Environment, 181: 14-26.   DOI
22 Wang, M. and L. Jiang, 2018. Atmospheric correction using the information from the short blue band, IEEE Transactions on Geoscience and Remote Sensing, 56(10), 6224-6237.   DOI