سابِق|تال

Construction of ocean color remote sensing data processing system based on open source code： Taking HY-1C/D as an example

  • role： First author第一作者
  • Affiliation：

    College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China

  • Email：dswang@xmu.edu.cn
  • Introduction：王道生，研究方向为水色卫星地面处理系统集成、大气校正。E-mail：dswang@xmu.edu.cn
WANG Daosheng1,  
  • Affiliation：

    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

DU Keping2,  
  • Affiliation：

    College of Marine Technology, Ocean University of China, Qingdao 266100, China

    Sanya Oceanographic Institution, Ocean University of China, Sanya 572024, China

    National Satellite Ocean Application Service, Ministry of Natural Resources of the People’s Republic of China, Beijing 100081, China

CHEN Shuguo345,  
  • Affiliation：

    College of Marine Technology, Ocean University of China, Qingdao 266100, China

XUE Cheng3,  
  • Affiliation：

    National Satellite Ocean Application Service, Ministry of Natural Resources of the People’s Republic of China, Beijing 100081, China

YE Xiaomin5,  
  • Affiliation：

    School for the Environment, University of Massachusetts, Boston, MA 02125, United States

LEE Zhongping6

ملخص

China has specifically planned a series of ocean observation satellites in the Medium and Long-term Development Plan for National Civil Space Infrastructure (2015—2025) to establish a more complete three-dimensional monitoring system for the marine environment through a network from a single test satellite to a constellation. Satellite observations place high demands on the degree of quantification of ocean satellite ground data processing systems due to the influence of the atmosphere and the typically low contribution of ocean parameters at the top of the atmosphere. Since the launch of CZCS water color satellite in 1970 s, the United States has accumulated many years of experience in water color satellite data processing system. In this paper, we develop HY-1C/1D offline data processing system (OffLine-COCPS), which realizes the whole chain from L1B data (after geometric positioning and radiometric calibration) to the production of remote sensing reflection ratio of water bodies and various water color products for HY-1C/1D Chinese Ocean Color and Temperature Scanner (COCTS).Based on NASA's mature open-source SeaDAS Ocean Color Science SoftWare package and HY-1C/1D COCTS format, we develop and recompile the software package to support COCTS L1B data. Using a vector sea-air coupled radiative transfer model developed based on the successive scattering method, HY-1C/1D COCTS specific atmospheric related lookup table are generated.The results show that the independently established HY-1C/1D satellite COCTS sensor atmospheric correction related lookup table basically meets the quantitative application on a global scale. The global inversion chlorophyll products of HY-1C/1D COCTS are obtained based on statistical projection, indicating that the system can realize support for HY-1C/1D dual-satellite missions, and then the overall distribution trend is also consistent with MODIS when compared with the official MODIS chlorophyll product scatter plot released by NASA for the same period. Through this study, the extension of the domestic water color satellite sensor and the independent inversion algorithm has been successfully realized, and the atmospheric correction algorithm and other water color inversion algorithms can be extended subsequently, while more work is needed to evaluate the accuracy of atmospheric correction and the accuracy of water color inversion products.

مفهوم

ocean remote sensing;water color satellite;HY-1C/1D;processing system;SeaDAS

References

  1. 1.
    Ahmad Z, Franz B A, McClain C R, Kwiatkowska E J, Werdell J, Shettle E P and Holben B N. 2010. New aerosol models for the retrieval of aerosol optical thickness and normalized water-leaving radiances from the SeaWiFS and MODIS sensors over coastal regions and open oceans: publisher’s note. Applied Optics, 50(5): 626-626
  2. 2.
    Bailey S W, Franz B A and Werdell P J. 2010. Estimation of near-infrared water-leaving reflectance for satellite ocean color data processing. Optics Express, 18(7): 7521-7527
  3. 3.
    Bates D R. 1984. Rayleigh scattering by air. Planetary and Space Science, 32(6): 785-790
  4. 4.
    Bodhaine B A, Wood N B, Dutton E G and Slusser J R. 1999. On rayleigh optical depth calculations. Journal of Atmospheric and Oceanic Technology, 16(11): 1854-1861
  5. 5.
    Chen S G, Du K P, Lee Z, Liu J Q, Song Q J, Xue C, Wang D S, Lin M S, Tang J W and Ma C F. 2021. Performance of COCTS in global ocean color remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 59(2): 1634-1644
  6. 6.
    Cox C and Munk W. 1954. Measurement of the roughness of the sea surface from photographs of the sun’s glitter. Journal of the Optical Society of America, 44(11): 838-850
  7. 7.
    Franz B A, Bailey S W, Kuring N and Werdell P J. 2015. Ocean color measurements with the Operational Land Imager on Landsat-8: implementation and evaluation in SeaDAS. Journal of Applied Remote Sensing, 9(1): 096070
  8. 8.
    Fu, G , Baith, K S and McClain, C R. 1998. SeaDAS: the SeaWiFS data analysis system// Proceedings of the 4th Pacific Ocean Remote Sensing Conference. Qingdao, China: 73-79.
  9. 9.
    Gordon H R and Wang M H. 1994. Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: a preliminary algorithm. Applied Optics, 33(3): 443-452
  10. 10.
    Hu C M, 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): C01011
  11. 11.
    Jiang X W, Lin M S and Zhang Y G. 2016. Progress and prospect of Chinese ocean satellites. Journal of Remote Sensing, 20(5): 1185-1198
  12. 12.
    Lee Z P, Darecki M, Carder K L, Davis C O, Stramski S and Rhea W J. 2005. Diffuse attenuation coefficient of downwelling irradiance: an evaluation of remote sensing methods. Journal of Geophysical Research: Oceans, 110(C2): C02017
  13. 13.
    Lee Z P, Shang S L, Hu C M, Du K P, Weidemann A, Hou W L, Lin J F and Lin G. 2015. Secchi disk depth: a new theory and mechanistic model for underwater visibility. Remote Sensing of Environment, 169: 139-149
  14. 14.
    Lin M S, He X Q, Jia Y J, Bai Y, Ye X M and Gong F. 2019. Advances in marine satellite remote sensing technology in China. Haiyang Xuebao, 41(10): 99-112
  15. 15.
    Mishchenko M I and Travis L D. 1997. Satellite retrieval of aerosol properties over the ocean using polarization as well as intensity of reflected sunlight. Journal of Geophysical Research: Atmospheres, 102(D14): 16989-17013
  16. 16.
    Mishchenko M I, Travis L D and Lacis A A. 2002. Scattering, Absorption, and Emission of Light by Small Particles. 3rd ed. Cambridge, U. K.: Cambridge University Press
  17. 17.
    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: Oceans, 103(C11): 24937-24953
  18. 18.
    Vanhellemont Q and Ruddick K. 2015. Advantages of high quality SWIR bands for ocean colour processing: examples from Landsat-8. Remote Sensing of Environment, 161: 89-106
  19. 19.
    Wang M H and Shi W. 2007. The NIR-SWIR combined atmospheric correction approach for MODIS ocean color data processing. Optics Express, 15(24): 15722-15733
  20. 20.
    Wang M H, Shi W, Jiang L D and Voss K. 2016. NIR- and SWIR-based on-orbit vicarious calibrations for satellite ocean color sensors. Optics Express, 24(18): 20437-20453
  21. 21.
    Wang X L, Liu X M, Jiang L D, Wang M H and Sun J Q. 2014. VIIRS ocean color data visualization and processing with IDL-based NOAA-SeaDAS//Proceedings Volume 9261, Ocean Remote Sensing and Monitoring from Space. Beijing, China: SPIE, 92611H
  22. 22.
    Yang H Y and Gordon H R. 1997. Remote sensing of ocean color: assessment of water-leaving radiance bidirectional effects on atmospheric diffuse transmittance. Applied Optics, 36(30): 7887-7897

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