[논문]연안 혼탁 해수에 적합한 위성 클로로필-a 농도 산출 알고리즘 개관과 전망

박지은; 박경애; 이지현

doi:10.5467/jkess.2021.42.3.247

연안 혼탁 해수에 적합한 위성 클로로필-a 농도 산출 알고리즘 개관과 전망
Overview and Prospective of Satellite Chlorophyll-a Concentration Retrieval Algorithms Suitable for Coastal Turbid Sea Waters 원문보기

한국지구과학회지 = Journal of the Korean Earth Science Society, v.42 no.3, 2021년, pp.247 - 263

박지은 (극지연구소 원격탐사빙권정보센터) , 박경애 (서울대학교 지구과학교육과) , 이지현 (서울대학교 과학교육과)

초록
AI-Helper

최근의 기후변화는 연안에서 더욱 가속화되고 있어 연안에서의 해양 환경변화 감시의 중요성이 커지고 있다. 클로로필-a 농도는 해양 환경 변화의 중요한 지표 중 하나로 수십년 동안 여러 해색 위성을 통해 전구 해양 표층의 클로로필-a 농도가 산출되었으며 다양한 연구 분야에 활용되었다. 하지만 연안 해역의 탁한 해수는 외해의 맑은 해수와는 구별되는 구성 성분과 광학적 특성으로 인해 나타나는 심각한 오차 때문에 일반적으로 사용되는 전지구 대양을 위하여 만들어진 클로로필-a 농도 알고리즘은 연안 해역에 대입할 수 없다. 또한 연안 해역은 해역에 따라 성분과 특성이 크게 달라져 통일된 하나의 알고리즘을 제시하기 어렵다. 이러한 문제점을 극복하기 위하여 연안의 탁도가 높은 해역에서는 구성 성분과 광학적 변동 특성을 고려한 다양한 알고리즘들이 개발되어 사용되어 왔다. 클로로필-a 농도 산출 알고리즘은 크게 경험적 알고리즘, 반해석적 알고리즘, 기계학습을 활용한 알고리즘 등으로 나눌 수 있다. 해수의 반사 스펙트럼에 기반한 청색-녹색 밴드 비율이 기본적인 형태로 주로 사용된다. 반면 탁한 해수를 위해 개발된 알고리즘은 연안해역에 존재하는 용존 유기물과 부유물의 영향을 상쇄시키기 위한 방식으로 녹색-적색 밴드 비율, 적색-근적외 밴드 비율, 고유한 광학적 특성 등을 사용한다. 탁한 해수에서의 신뢰성 있는 위성 클로로필-a 농도 산출은 미래의 연안 해역을 관리하고 연안 생태 변화를 감시하는데 필수적이다. 따라서 본 연구는 탁도가 높은 Case 2 해수에서 활용되어온 알고리즘들을 요약하고, 한반도 주변해역의 모니터링과 연구에 대한 문제점을 제시한다. 또한 다분광 및 초분광 센서의 개발로 더욱 정확하고 다양한 해색 환경을 이해할 수 있는 미래의 해색 위성에 대한 발전 전망도 제시한다.

Abstract ▼ AI-Helper

Climate change has been accelerating in coastal waters recently; therefore, the importance of coastal environmental monitoring is also increasing. Chlorophyll-a concentration, an important marine variable, in the surface layer of the global ocean has been retrieved for decades through various ocean color satellites and utilized in various research fields. However, the commonly used chlorophyll-a concentration algorithm is only suitable for application in clear water and cannot be applied to turbid waters because significant errors are caused by differences in their distinct components and optical properties. In addition, designing a standard algorithm for coastal waters is difficult because of differences in various optical characteristics depending on the coastal area. To overcome this problem, various algorithms have been developed and used considering the components and the variations in the optical properties of coastal waters with high turbidity. Chlorophyll-a concentration retrieval algorithms can be categorized into empirical algorithms, semi-analytic algorithms, and machine learning algorithms. These algorithms mainly use the blue-green band ratio based on the reflective spectrum of sea water as the basic form. In constrast, algorithms developed for turbid water utilizes the green-red band ratio, the red-near-infrared band ratio, and the inherent optical properties to compensate for the effect of dissolved organisms and suspended sediments in coastal area. Reliable retrieval of satellite chlorophyll-a concentration from turbid waters is essential for monitoring the coastal environment and understanding changes in the marine ecosystem. Therefore, this study summarizes the pre-existing algorithms that have been utilized for monitoring turbid Case 2 water and presents the problems associated with the mornitoring and study of seas around the Korean Peninsula. We also summarize the prospective for future ocean color satellites, which can yield more accurate and diverse results regarding the ecological environment with the development of multi-spectral and hyperspectral sensors.

주제어

참고문헌 (100)

Abbas, M. M., Melesse, A. M., Scinto, L. J., and Rehage, J. S., 2019, Satellite Estimation of Chlorophyll-a Using Moderate Resolution Imaging Spectroradiometer (MODIS) Sensor in Shallow Coastal Water Bodies: Validation and Improvement. Water, 11(8), 1621.
Aiken, J., Moore, G. F., Trees, C. C., Hooker, S. B., and Clark, D. K., 1996, The SeaWiFS CZCS-type pigment algorithm. Oceanographic Literature Review, 3(43), 315-316.
Babin, M. and Stramski, D., 2004, Variations in the massspecific absorption coefficient of mineral particles suspended in water. Limnology and Oceanography, 49(3), 756-767.

상세보기
Bissett, W. P., Schofield, O., Glenn, S., Cullen, J. J., Miller, W. L., Plueddemann, A. J., and Mobley, C. D., 2001, Resolving the impacts and feedbacks of ocean optics on upper ocean ecology. Oceanography, 14(3), 30-53.
Blondeau-Patissier, D., Gower, J. F., Dekker, A. G., Phinn, S. R., and Brando, V. E., 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.

상세보기
Bowers, D. G., Harker, G. E. L., and Stephan, B., 1996, Absorption spectra of inorganic particles in the Irish Sea and their relevance to remote sensing of chlorophyll. International Journal of Remote Sensing, 17(12), 2449-2460.

상세보기
Boyce, D. G., Lewis, M. R., and Worm, B., 2010, Global phytoplankton decline over the past century. Nature, 466(7306), 591-596.

상세보기
Bukata, R. P., Jerome, J. H., Kondratyev, A. S., and Pozdnyakov, D. V., 2018, Optical properties and remote sensing of inland and coastal waters. CRC Press, Boca Raton, USA, 384 p.
Cao, Z., Ma, R., Duan, H., Pahlevan, N., Melack, J., Shen, M., and Xue, K., 2020, A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes. Remote Sensing of Environment, 248, 111974.

상세보기
Carder, K. L., Chen, F. R., Lee, Z. P., Hawes, S. K., and Kamykowski, D., 1999, Semianalytic Moderate-Resolution Imaging Spectrometer algorithms for chlorophyll a and absorption with bio-optical domains based on nitrate-depletion temperatures. Journal of Geophysical Research: Oceans, 104(C3), 5403-5421.
Carder, K. L., Chen, F. R., Cannizzaro, J. P., Campbell, J. W., and Mitchell, B. G., 2004, Performance of the MODIS semi-analytical ocean color algorithm for chlorophyll-a. Advances in Space Research, 33(7), 1152-1159.

상세보기
Claustre, H., Babin, M., Merien, D., Ras, J., Prieur, L., Dallot, S., Prasil, O., Dousova, H., and Moutin, T., 2005, Toward a taxon-specific parameterization of biooptical models of primary production: A case study in the North Atlantic. Journal of Geophysical Research: Oceans, 110(C7).
Cui, T., Zhang, J., Groom, S., Sun, L., Smyth, T., Sathyendranath, S., 2010, Validation of MERIS oceancolor products in the Bohai Sea: A case study for turbid coastal waters. Remote Sensing Environment, 114, 2326-2336.
Cullen, J. J., 1982, The deep chlorophyll maximum: comparing vertical profiles of chlorophyll a. Canadian Journal of Fisheries and Aquatic Sciences, 39(5), 791-803.

상세보기
Dierssen, H. M., 2010, Perspectives on empirical approaches for ocean color remote sensing of chlorophyll in a changing climate. Proceedings of the National Academy of Sciences of the United States of America, 107(40), 17073-17078.

상세보기
Doerffer, R. and Schiller, H., 2007, The MERIS Case 2 water algorithm. International Journal of Remote Sensing, 28(3-4), 517-535.
Donlon, C., Berruti, B., Buongiorno, A., Ferreira, M. H., Femenias, P., Frerick, J., Goryl, P., Klein, U., Laur, H., Mavrocordatos, C., Nieke, J., Rebhan, H., Seitz, B., Stroede, J., and Sciarra, R., 2012, The global monitoring for environment and security (GMES) sentinel-3 mission. Remote Sensing of Environment, 120, 37-57.

상세보기
Falkowski, P. and Kiefer, D. A., 1985, Chlorophyll a fluorescence in phytoplankton: relationship to photosynthesis and biomass. Journal of Plankton Research, 7(5), 715-731.

상세보기
Franz, B. A., Kwiatowska, E. J., Meister, G., and McClain, C. R., 2008, Moderate Resolution Imaging Spectroradiometer on Terra: limitations for ocean color applications. Journal of Applied Remote Sensing, 2(1), 023525.

상세보기
Garver, S. A. and Siegel, D. A., 1997, Inherent optical property inversion of ocean color spectra and its biogeochemical interpretation: 1. Time series from the Sargasso Sea. Journal of Geophysical Research: Oceans, 102(C8), 18607-18625.
Gitelson, A. A., Schalles, J. F., Rundquist, D. C., Schiebe, F. R., and Yacobi, Y. Z., 1999, Comparative reflectance properties of algal cultures with manipulated densities. Journal of Applied Phycology, 11(4), 345-354.
Gordon, H. and Morel, A., 1983, Lecture notes on coastal and estuarine studies. In Remote assessment of ocean color for interpretation of satellite visible imagery: A review Vol. 4. Springer-Verlag, NY, USA, 114 p.
Gower, J., 2000, Productivity and plankton blooms observed with SeaWiFS. In. Proc. 5th Pacific Ocean Remote Sensing Conference, PORSEC, 23-27.
Gower, J., Brown, L., and Borstad, G., 2004, Observation of chlorophyll fluorescence in west coast waters of Canada using the MODIS satellite sensor. Canadian Journal of Remote Sensing, 30(1), 17-25.
Gower, J., King, S., Borstad, G., and Brown, L., 2005. Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer. International Journal of Remote Sensing, 26, 2005-2012.

상세보기
Groom, S., Sathyendranath, S., Ban, Y., Bernard, S., Brewin, R., Brotas, V., Brockmann, C., Chauhan, P., Choi, J., Chuprin, A., Ciavatta, S., Cipollini, P., Donlon, C., Franz, B., He, X., Hirata, T., Jackson, T., Kampel, M., Krasemann, H., Lavender, S., PardoMartinez, S., Melin, F., Platt, T., Santoleri, R., Skakala, J., Schaeffer, B., Smith, M., Steinmetz, F., Valente, A., and Wang, M., 2019, Satellite ocean colour: current status and future perspective. Frontiers in Marine Science, 6, 485.

상세보기
Gross, L., Thiria, S., Frouin, R., and Mitchell, B. G., 2000, Artificial neural networks for modelling the transfer function between marine reflectance and phytoplankton pigment concentration. Journal of Geophysical Research, 105, 3483-3495.
Gurlin, D., Gitelson, A. A., and Moses, W. J., 2011, Remote estimation of chl-a concentration in turbid productive waters-Return to a simple two-band NIR-red model?. Remote Sensing of Environment, 115(12), 3479-3490.

상세보기
Hastie, T. and Tibshirani, R., 1990, Exploring the nature of covariate effects in the proportional hazards model. Biometrics, 1005-1016.
Hattab, T., Jamet, C., Sammari, C., and Lahbib, S., 2013, Validation of chlorophyll-α concentration maps from Aqua MODIS over the Gulf of Gabes (Tunisia): Comparison between MedOC3 and OC3M bio-optical algorithms. International Journal of Remote Sensing, 34(20), 7163-7177.
He, M. X., Liu, Z. S., Du, K. P., Li, L. P., Chen, R., Carder, K. L., and Lee, Z. P., 2000, Retrieval of chlorophyll from remote-sensing reflectance in the China seas. Applied Optics, 39(15), 2467-2474.
Hieronymi, M., Muller, D., and Doerffer, R., 2017, The OLCI Neural Network Swarm (ONNS): a bio-geooptical algorithm for open ocean and coastal waters. Frontiers in Marine Science, 4, 140.
Hojerslev, N. K., 1980, Water color and its relation to primary production. Boundary-Layer Meteorology, 18(2), 203-220.

상세보기
Hooker, S. B., Firestone, E. R., Esaias, W. E., Feldman, G. C., Gregg, W. W., and Mcclain, C. R., 1992, An overview of SeaWiFS and ocean color. In Hooker, S. B. and Firestone, E. R. (eds.), SeaWiFS technical report series Vol. 1. NASA Goddard Space Flight Center, Maryland, USA, 24 p.
Hovis, W. A., 1981, The Nimbus-7 coastal zone color scanner (CZCS) program. In Oceanography from space. Springer, MA, USA, 213-225.
Hu, C., Muller-Karger, F. E., Taylor, C. J., Carder, K. L., Kelble, C., Johns, E., and Heil, C. A., 2005, Red tide detection and tracing using MODIS fluorescence data: A regional example in SW Florida coastal waters. Remote Sensing of Environment, 97(3), 311-321.

상세보기
Hu, C., Lee, Z., and Franz, B., 2012, Chlorophyll a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference. Journal of Geophysical Research: Oceans, 117(C1).
Hu, C., Feng, L., Lee, Z. P., Franz, B. A., Bailey, S. W., Werdell, P. J., and Proctor, C. W., 2019, Improving satellite global chlorophyll a data products through algorithm refinement and data recovery. Journal of Geophysical Research-Oceans, 124(3), 1524-1543.
Ioannou I., Gilerson, A., Gross, B., Moshary, F., and Ahmed, S., 2011, Neural network approach to retrieve the inherent optical properties of the ocean from observations of MODIS, Applied Optics, 50(19), 3168-3186.

상세보기
IOCCG, 2000, Remote Sensing of Ocean Colour in Coastal, and Other Optically-Complex, Waters, In Sathyendranath, S. (ed.), Reports of the International Ocean-Colour Coordinating Group No. 3. Dartmouth, NS, Canada, 140 p.
IOCCG, 2006, Remote Sensing of Inherent Optical Properties: Fundamentals, Tests of Algorithms, and Applications, In Lee, Z-P. (ed), Reports of the International Ocean-Colour Coordinating Group No. 5. Dartmouth, NS, Canada, 126 p.
IOCCG, 2012a, Ocean-colour observations from a geostationary orbit. In Antoine, D. (ed.), Reports of the International Ocean Colour Coordinating Group No. 7. Dartmouth, Canada, 103 p.
IOCCG, 2012b, Mission requirements for future oceancolour sensors. In McClain, C. and Meister, G. (ed.), Reports of the International Ocean Colour Coordinating Group. NASA Goddard Space Flight Center, Greenbelt (MD, USA), 106 p.
Irwin, A. J. and Finkel, Z. V., 2008, Mining a sea of data: Deducing the environmental controls of ocean chlorophyll. PloS One, 3(11), e3836.

상세보기
Jamet, C., Loisel, H., and Dessailly, D., 2012, Retrieval of the spectral diffuse attenuation coefficient Kd (λ) in open and coastal ocean waters using a neural network inversion. Journal of Geophysical Research: Oceans, 117(C10).
Joint, I. I. and Groom, S. B., 2000, Estimation of phytoplankton production from space: current status and future potential of satellite remote sensing. Journal of Experimental Marine Biology and Ecology, 250, 233-255.

상세보기
Kajiyama, T., D'Alimonte, D., and Zibordi, G., 2018, Algorithms Merging for the Determination of Chlorophyll-a Concentration in the Black Sea. IEEE Geoscience and Remote Sensing Letters, 16(5), 677-681.
Kim, W., Moon, J. E., Park, Y. -J., and Ishizaka, J., 2016, Evaluation of chlorophyll retrievals from Geostationary Ocean Color Imager (GOCI) for the North-East Asian region. Remote Sensing of Environment, 184, 482-495.

상세보기
Kwiatkowska, E. J. and Fargion, G. S., 2003, Application of machine learning techniques towards the creation of a consistent and calibrated global chlorophyll concentration baseline dataset using remotely sensed ocean color data. IEEE Transactions on Geoscience and Remote Sensing, 41, 2844-2860.
Le, C., Hu, C., Cannizzaro, J., and Duan, H., 2013a, Longterm distribution patterns of remotely sensed water quality parameters in Chesapeake Bay. Estuarine, Coastal and Shelf Science, 128, 93-103.

상세보기
Le, C., Hu, C., Cannizzaro, J., English, D., Muller-Karger, F., and Lee, Z., 2013b, Evaluation of chlorophyll-a remote sensing algorithms for an optically complex estuary. Remote Sensing of Environment, 129, 75-89.

상세보기
Lee, Z., Carder, K. L., and Arnone, R. A., 2002, Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters. Applied Optics, 41(27), 5755-5772.
Lim, H., Choi, M., Kim, J., Kasai, Y., and Chan, P., 2018, AHI/Himawari-8 Yonsei Aerosol Retrieval (YAER): Algorithm, validation and merged products. Remote Sensing, 10(5), 699.

상세보기
Maritorena, S., Siegel, D. A., and Peterson, A. R., 2002, Optimization of a semi analytical ocean color model for global-scale applications. Applied Optics, 41, 2705-2714.
Martin, A. P., 2003, Phytoplankton patchiness: the role of lateral stirring and mixing. Progress in Oceanography, 57(2), 125-174.

상세보기
McClain, C. R., 2009, A decade of satellite ocean color observations. Annual Review of Marine Science, 1, 19-42.

상세보기
McKinna, L. I., Fearns, P. R., Weeks, S. J., Werdell, P. J., Reichstetter, M., Franz, B. A., Shea, D. M., and Feldman, G. C., 2015, A semianalytical ocean color inversion algorithm with explicit water column depth and substrate reflectance parameterization. Journal of Geophysical Research: Oceans, 120(3), 1741-1770.
Mobley, C. D. and Stramski, D., 1994, Influences of microbial particles on oceanic optics. In Ocean Optics XII (Vol. 2258, pp. 184-193). International Society for Optics and Photonics.
Mobley, C. D., Stramski, D., Paul Bissett, W., and Boss, E., 2004, Optical modeling of ocean waters: Is the case 1-case 2 classification still useful?. Oceanography, 17(2), 60-67.

상세보기
Moon, J. E., Ahn, Y. H., Ryu, J. H., and Shanmugam, P., 2010, Development of ocean environmental algorithms for Geostationary Ocean Color Imager (GOCI). Korean Journal of Remote Sensing, 26(2), 189-207.
Moore, T. S., Campbell, J. W., and Dowell, M. D., 2009, A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product. Remote Sensing of Environment, 113(11), 2424-2430.

상세보기
Morel, A. and Prieur, L., 1977, Analysis of variations in ocean color 1. Limnology and Oceanography, 22(4), 709-722.
Morel, A., 1988, Optical modeling of the upper ocean in relation to its biogenous matter content (case I waters). Journal of Geophysical Research: Oceans, 93(C9), 10749-10768.
Moses, W. J., Gitelson, A. A., Berdnikov, S., Saprygin, V., and Povazhnyi, V., 2012, Operational MERIS-based NIR-red algorithms for estimating chlorophyll-a concentrations in coastal waters-The Azov Sea case study. Remote Sensing of Environment, 121, 118-124.

상세보기
Murakami, H., 2016, Ocean color estimation by Himawari8/AHI. In Proceedings of SPIE Asia-Pacific Remote Sensing. International Society for Optics and Photonics, 2016, New Delhi, India, 987810.
Neville, R. A. and Gower, J. F. R., 1977, Passive remote sensing of phytoplankton via chlorophyll α fluorescence. Journal of Geophysical Research, 82(24), 3487-3493.
Odermatt, D., Gitelson, A., Brando, V. E., and Schaepman, M., 2012, Review of constituent retrieval in optically deep and complex waters from satellite imagery. Remote sensing of environment, 118, 116-126.

상세보기
O'Reilly, J. E., Maritorena, S., Mitchell, B. G., Siegel, D. A., Carder, K. L., Garver, S. A., Kahru, M., and McClain, C., 1998, Ocean color chlorophyll algorithms for SeaWiFS. Journal of Geophysical Research, 103, 24937-24953.
O'Reilly, J. E. and Werdell, P. J., 2019, Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6. Remote Sensing of Environment, 229, 32-47.

상세보기
Pahlevan, N., Smith, B., Schalles, J., Binding, C., Cao, Z., Ma, R., Alikas, K., Kangro, K., Gurlin, D., Ha, N., Matsushita, B., Moses, W., Greb, S., Lehmann, M. K., Ondrusek, M., Oppelt, N., and Stumpf, R., 2020, Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. Remote Sensing of Environment, 240, 111604.

상세보기
Park, J. -E., Park, K. -A., Ullman, D. Cornillon, P., and Park, Y. -J., 2016, Observation of diurnal variations in mesoscale eddy sea-surface currents using GOCI data. Remote Sensing Letters, 7(12), 1131-1140.

상세보기
Park, K. -A., Lee, M. -S., Park, J. -E., Ullman, D., Cornillon, P., and Park, Y. -J., 2018, Surface currents from hourly variations of suspended particulate matter from Geostationary Ocean Color Imager data. International Journal of Remote Sensing, 39(6), 1929-1949.

상세보기
Pozdnyakov, D., Lyaskovsky, A., Grassl, H., and Pettersson, L., 2002, Numerical modelling of transspectral processes in natural waters: implications for remote sensing. International Journal of Remote Sensing, 23(8), 1581-1607.

상세보기
Pradhan, Y., Thomaskutty, A. V., Rajawat, A. S., and Nayak, S., 2005, Improved regional algorithm to retrieve total suspended particulate matter using IRS-P4 ocean colour monitor data. Journal of Optics A: Pure and Applied Optics, 7(7), 343.

상세보기
Raitsos, D. E., Korres, G., Triantafyllou, G., Petihakis, G., Pantazi, M., Tsiaras, K., and Pollani, A., 2012, Assessing chlorophyll variability in relation to the environmental regime in Pagasitikos Gulf, Greece. Journal of Marine Systems, 94, S16-S22.
Rast, M., Bezy, J. L., and Bruzzi, S., 1999, The ESA Medium Resolution Imaging Spectrometer MERIS a review of the instrument and its mission. International Journal of Remote Sensing, 20(9), 1681-1702.

상세보기
Robinson, I. S., 2004, Measuring the oceans from space: The principles and methods of satellite oceanography. Springer Science & Business Media, Chichester, UK, 670 p.
Roesler, C. S. and Perry, M. J., 1995, In situ phytoplankton absorption, fluorescence emission, and particulate backscattering spectra determined from reflectance. Journal of Geophysical Research: Oceans, 100(C7), 13279-13294.
Ruddick, K. G., Gons, H. J., Rijkeboer, M., and Tilstone, G., 2001, Optical remote sensing of chlorophyll a in case 2 waters by use of an adaptive two-band algorithm with optimal error properties. Applied Optics, 40(21), 3575-3585.
Schalles, J. F., 2006, Optical remote sensing techniques to estimate phytoplankton chlorophyll a concentrations in coastal. In Remote sensing of aquatic coastal ecosystem processes, Springer, Dordrecht, Netherlands, 27-79.
Schiller, H. and Doerffer, R., 2005, Improved determination of coastal water constituent concentrations from MERIS data. IEEE Transactions on Geoscience and Remote Sensing, 43(7), 1585-1591.

상세보기
Sen Gupta, A., McNeil, B., 2012. Variability and change in the ocean. In: Henderson-Sellers, A., McGuffie, K. (Eds.), The Future of the World's Climate, second ed. Elsevier, Boston, 141-165.
Shang, S., Dong, Q., Lee, Z., Li, Y., Xie, Y., and Behrenfeld, M., 2011, MODIS observed phytoplankton dynamics in the Taiwan Strait: an absorption-based analysis. Biogeosciences, 8(4), 841-850.

상세보기
Shanmugam, P., 2011. A new bio-optical algorithm for the remote sensing of algal blooms in complex ocean waters. Journal of Geophysical Research: Oceans, 116, 12.
Shin, J., Kim, K., and Ryu, J. -H., 2020, Comparative Study on Hyperspectral and Satellite Image for the Estimation of Chlorophyll a Concentration on Coastal Areas. Korean Journal of Remote Sensing, 36(2-2), 309-323.
Siegel, H., Ohde, T., Gerth, M., Lavik, G., and Leipe, T., 2007, Identification of coccolithophore blooms in the SE Atlantic Ocean off Namibia by satellites and in-situ methods. Continental Shelf Research, 27(2), 258-274.

상세보기
Siswanto, E., Tang, J., Yamaguchi, H., Ahn, Y. H., Ishizaka, J., Yoo, S., Kim, S. W., Kiyomoto, Y., Yamada, K., Chiang, C., and Kawamura, H., 2011, Empirical ocean-color algorithms to retrieve chlorophylla, total suspended matter, and colored dissolved organic matter absorption coefficient in the Yellow and East China Seas. Journal of Oceanography, 67(5), 627-650.

상세보기
Smith, B., Pahlevan, N., Schalles, J., Ruberg, S., Errera, R., Ma, R., Giardino C., Bresciani M., Barbosa C., Moore T., Fernandez V., Alikas K., and Kangro K., 2021, A chlorophyll-a algorithm for Landsat-8 based on mixture density networks. Frontiers in Remote Sensing, 1, 5.
Stock, A., 2015, Satellite mapping of Baltic Sea Secchi depth with multiple regression models. International Journal of Applied Earth Observation and Geoinformation, 40, 55-64.

상세보기
Strickland, J. D. and Parsons, T. R., 1972, A practical handbook of seawater analysis. Fisheries Research Board of Canada 167, Ottawa, Canada, 310 p.
Tassan, S., 1994, Local algorithms using SeaWiFS data for the retrieval of phytoplankton, pigments, suspended sediment, and yellow substance in coastal waters. Applied Optics, 33(12), 2369-2378.
Tilstone, G. H., Lotliker, A. A., Miller, P. I., Ashraf, P. M., Kumar, T. S., Suresh, T., Ragavan, B. R., and Menon, H. B., 2013, Assessment of MODIS-Aqua chlorophyll-a algorithms in coastal and shelf waters of the eastern Arabian Sea. Continental Shelf Research, 65, 14-26.

상세보기
Tzortziou, M., Subramaniam, A., Herman, J. R., Gallegos, C. L., Neale, P. J., and Harding Jr, L. W., 2007, Remote sensing reflectance and inherent optical properties in the mid Chesapeake Bay. Estuarine, Coastal and Shelf Science, 72(1-2), 16-32.
Wang, Y., Liu, D., and Tang, D., 2017, Application of a generalized additive model (GAM) for estimating chlorophyll-a concentration from MODIS data in the Bohai and Yellow Seas, China. International Journal of Remote Sensing, 38(3), 639-661.

상세보기
Wei, J., Lee, Z., and Shang, S., 2016, A system to measure the data quality of spectral remote-sensing reflectance of aquatic environments. Journal of Geophysical Research: Oceans, 121(11), 8189-8207.
Werdell, P. J., McKinna, L. I., Boss, E., Ackleson, S. G., Craig, S. E., Gregg, W. W., Lee, Z., Maritorena, S., Roesler, C. S., Rousseaux, C. S., Stramski, D., Sullivan, J. M., Twardowskik, M. S., Tzortziou, M., and Zhang, X., 2018, An overview of approaches and challenges for retrieving marine inherent optical properties from ocean color remote sensing. Progress in oceanography, 160, 186-212.

상세보기
Yang, M. M., Ishizaka, J., Goes, J. I., Gomes, H. D. R., Maure, E. D. R., Hayashi, M., Katano, T., Fujii, N., Saitoh, K., Mine, T., Yamashita, H., Fujii, N., and Mizuno, A., 2018, Improved MODIS-Aqua chlorophyll-a retrievals in the turbid semi-enclosed Ariake Bay, Japan. Remote Sensing, 10(9), 1335.

상세보기
Yentsch, C. S. and Menzel, D. W., 1963, A method for the determination of phytoplankton chlorophyll and phaeophytin by fluorescence. Deep Sea Research and Oceanographic Abstracts, 10(3), 221-231.
Yoder, J. A. and Kennelly, M. A., 2003, Seasonal and ENSO variability in global ocean phytoplankton chlorophyll derived from 4 years of SeaWiFS measurements. Global Biogeochemical Cycles, 17(4), 1112.
Zhan, H., Shi, P., and Chen, C., 2003, Retrieval of oceanic chlorophyll concentration using support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 41(12), 2947-2951.

상세보기

저자의 다른 논문 :

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증