Preliminary performance of the COCTS onboard HY-1D satellite in the global ocean

  • role: First author第一作者
  • Affiliation:

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

  • Email:shixinhao@stu.ouc.edu.cn
  • Introduction:E-mail shixinhao@stu.ouc.edu.cn
SHI Xinhao1,  
  • role: Corresponding author通信作者
  • Affiliation:

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

    Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China

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

    National Satellite Ocean Application Service, Beijing 100081, China

  • Email:chenshuguo@ouc.edu.cn
  • Introduction:E-mailchenshuguo@ouc.edu.cn
CHEN Shuguo1234*,  
  • Affiliation:

    National Satellite Ocean Application Service, Beijing 100081, China

LIN Mingsen4,  
  • Affiliation:

    National Satellite Ocean Application Service, Beijing 100081, China

LIU Jianqiang4,  
  • Affiliation:

    National Satellite Ocean Application Service, Beijing 100081, China

MA Chaofei4,  
  • Affiliation:

    National Satellite Ocean Application Service, Beijing 100081, China

SONG Qingjun4,  
  • Affiliation:

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

XUE Cheng1,  
  • Affiliation:

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

    Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China

HU Lianbo12

реферат

The Chinese Ocean Color and Temperature Scanner (COCTS) onboard HY-1D satellite (COCTS HY-1D) was launched on June 11, 2020. However, the performance of COCTS HY-1D has not yet been completely evaluated. In this study, the performance of COCTS HY-1D was first evaluated by comparing satellite derived remote sensing reflectance (Rrs) with in situ measurements collected at four AERONET-OC sites and two Chinese long-term platforms.Initially, the in situ data at four AERONET-OC sites were acquired to evaluate the performance of COCTS HY-1D in the global coastal waters. AERONET-OC is an ocean color component of the AERONET and provides long-term high-quality in situ normalized water leaving radiance (Lwn) measured by an autonomous radiometer system on an offshore fixed platform to support the calibration and validation of satellite ocean color sensors in coastal waters. Muping and Dong’ou sites were constructed by the China National Satellite Ocean Administration Service (NSOAS), and the data were processed following the same procedure as that of the AERONET-OC data processing scheme. The COCTS HY-1D Level 1B data covering AERONET-OC sites and two long-term platforms between 1 August 1 2020 and 31 January 31 2021 in cloud-free days were acquired from NSOAS and processed to Level 2 Rrs and Chl-a concentration products. Furthermore, Rrs and Chl-a concentration comparison with two well-calibrated ocean color sensors (i.e., MODIS Aqua and VIIRS-SNPP) were made to evaluate the performance of COCTS HY-1D on the global scale. Additionally, the COCTS HY-1D Level 1B daily global dataset between December 7 and 14, 2020 were also required from NSOAS, processed to Level 2, and binned to Level 3 daily and 8-day 9-km data products by using the spatial-temporal binning algorithms developed by NASA. MODIS Aqua and VIIRS-SNPP Level 3 global binned daily and 8-day 9-km Rrs and Chl-a concentration data collected between December 7 and 14, 2020 were acquired from NASA GSFC. The statistics used in this study included correlation coefficient (r), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and mean bias (mBias).Results demonstrated that COCTS HY-1D-derived Rrs agreed well with the in situ data at all wavelengths with the correlation coefficient r of visible bands between 0.91 and 0.98 and up to 0.98 and Mean Absolute Percentage Error (MAPE) of 22.9%. The product’s accuracy is comparable to the average MAPE of 20.5% between MODIS Aqua and in situ data. At the global scale, the COCTS HY-1D-derived Rrs and chlorophyll concentration were consistent with MODIS Aqua products with a mean correlation coefficient ranging from 0.84 and to 0.95. The correlation coefficient of Chl-a is 0.85,which is higher than 0.76 between MODIS Aqua and VIIRS-SNPP. Nevertheless, the satisfactory Rrs was derived from COCTS HY-1D at the global scale compared with the in situ measurements or well-calibrated MODIS Aqua and VIIRS-SNPP products.COCTS HY-1D can provide high quality ocean color products comparable with the international mainstream ocean color satellite sensors, and therefore can carry out stable and accurate ocean color remote sensing observation.

ключеви́че слова́

in situ observation;ocean color;HY-1D;COCTS;remote sensing refcectance

References

  1. 1.
    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
  2. 2.
    Bailey S W and Werdell P J. 2006. A multi-sensor approach for the on-orbit validation of ocean color satellite data products. Remote Sensing of Environment, 102(1/2): 12-23
  3. 3.
    Barnes B B, Cannizzaro J P, English D C and Hu C M. 2019. Validation of VIIRS and MODIS reflectance data in coastal and oceanic waters: an assessment of methods. Remote Sensing of Environment, 220: 110-123
  4. 4.
    Barnes B B, Hu C M, Bailey S W and Franz B A. 2021. Sensitivity of satellite ocean color data to system vicarious calibration of the long near infrared band. IEEE Transactions on Geoscience and Remote Sensing, 59(3): 2562-2578
  5. 5.
    Behrenfeld M J, Gaube P, Della Penna A, O'Malley R T, Burt W J, Hu Y X, Bontempi P S, Steinberg D K, Boss E S, Siegel D A, Hostetler C A, Tortell P D and Doney S C. 2019. Global satellite-observed daily vertical migrations of ocean animals. Nature, 576(7786): 257-261
  6. 6.
    Bojinski S, Verstraete M, Peterson T C, Richter C, Simmons A and Zemp M. 2014. The concept of essential climate variables in support of climate research, applications, and policy. Bulletin of the American Meteorological Society, 95(9): 1431-1443
  7. 7.
    Cao C Y, Xiong J, Blonski S, Liu Q H, Uprety S, Shao X, Bai Y and Weng F Z. 2013. Suomi NPP VIIRS sensor data record verification, validation, and long-term performance monitoring. Journal of Geophysical Research: Atmospheres, 118(20): 11664-11678
  8. 8.
    Chami M, Lafrance B, Fougnie B, Chowdhary J, Harmel T and Waquet F. 2015. OSOAA: a vector radiative transfer model of coupled atmosphere-ocean system for a rough sea surface application to the estimates of the directional variations of the water leaving reflectance to better process multi-angular satellite sensors data over the ocean. Optics Express, 23(21): 27829-27852
  9. 9.
    Chen S G, Du K P, Lee Z P, 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
  10. 10.
    Clark D K. 1981. Phytoplankton pigment algorithms for the nimbus-7 CZCS//Gower J F R, ed. Oceanography from Space. New York: Springer: 227-237
  11. 11.
    Dutkiewicz S, Hickman A E, Jahn O, Henson S, Beaulieu C and Monier E. 2019. Ocean colour signature of climate change. Nature Communications, 10: 578
  12. 12.
    Giles D M, Sinyuk A, Sorokin M G, Schafer J S, Smirnov A, Slutsker I, Eck T F, Holben B N, Lewis J R, Campbell J R, Welton E J, Korkin S V and Lyapustin A I. 2019. Advancements in the aerosol robotic network (AERONET) version 3 database-automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements. Atmospheric Measurement Techniques, 12: 169-209
  13. 13.
    Gordon I E, Rothman L S, Hill C, Kochanov R V, Tan Y, Bernath P F, Birk M, Boudon V, Campargue A, Chance K V, Drouin B J, Flaud J M, Gamache R R, Hodges J T, Jacquemart D, Perevalov V I, Perrin A, Shine K P, Smith M A H, Tennyson J, Toon G C, Tran H, Tyuterev V G, Barbe A, Császár A G, Devi V M, Furtenbacher T, Harrison J J, Hartmann J M, Jolly A, Johnson T J, Karman T, Kleiner I, Kyuberis A A, Loos J, Lyulin O M, Massie S T, Mikhailenko S N, Moazzen-Ahmadi N, Müller H S P, Naumenko O V, Nikitin A V, Polyansky O L, Rey M, Rotger M, Sharpe S W, Sung K, Starikova E, Tashkun S A, Auwera J V, Wagner G, Wilzewski J, Wcisło P, Yu S and Zak E J. 2017. The HITRAN2016 molecular spectroscopic database. Journal of Quantitative Spectroscopy and Radiative Transfer, 203: 3-69
  14. 14.
    Goyens C, Jamet C and Schroeder T. 2013. Evaluation of four atmospheric correction algorithms for MODIS Aqua images over contrasted coastal waters. Remote Sensing of Environment, 131: 63-75 [DOI ]
  15. 15.
    Hu C M, Lee Z P 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
  16. 16.
    Jamet C, Loisel H, Kuchinke C P, Ruddick K, Zibordi G and Feng H. 2011. Comparison of three SeaWiFS atmospheric correction algorithms for turbid waters using AERONET-OC measurements. Remote Sensing of Environment, 115(8): 1955-1965
  17. 17.
    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
  18. 18.
    Laber C P, Hunter J E, Carvalho F, Collins J R, Hunter E J, Schieler B M, Boss E, More K, Frada M, Thamatrakoln K, Brown C M, Haramaty L, Ossolinski J, Fredricks H, Nissimov J I, Vandzura R, Sheyn U, Yoav L, Chant R J, Martins A M, Coolen M J L, Vardi A, Ditullio G R, Van Mooy B A S and Bidle K D. 2018. Coccolithovirus facilitation of carbon export in the North Atlantic. Nature Microbiology, 3(5): 537-547
  19. 19.
    Mason P J, Manton M, Harrison D E, Belward A, Thomas A R, Dawson A, Allali A, Church J, Clarke R A, Eyre J, Folland C K, Gould W J, Haeberli W, Harrison S, Karl T R, Maurer T, Parker D, Proffitt M, Quegan S, Simmons A, Trenberth K and Verstraete M M. 2003. The second report on the adequacy of the global observing systems for climate in support of the UNFCCC. World Meteorological Organization
  20. 20.
    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
  21. 21.
    Qi L, Hu C M, Barnes B B and Lee Z P. 2017. VIIRS captures phytoplankton vertical migration in the Ne Gulf of Mexico. Harmful Algae, 66: 40-46
  22. 22.
    Signorini S R, Franz B A and Mcclain C R. 2015. Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Frontiers in Marine Science, 2: 1
  23. 23.
    Song Q J, Chen S G, Xue C, Lin M S, Du K P, Li S C, Ma C F, Tang J W, Liu J Q, Zhang T L and Huang X X. 2019. Vicarious calibration of COCTS-HY1C at visible and near-infrared bands for ocean color application. Optics Express, 27(20): A1615-A1626
  24. 24.
    Wang M H and Jiang L D. 2018. Atmospheric correction using the information from the short blue band. IEEE Transactions on Geoscience and Remote Sensing, 56(10): 6224-6237
  25. 25.
    Wang M Q, Hu C M, Barnes B B, Mitchum G, Lapointe B and Montoya J P. 2019. The great Atlantic Sargassum belt. Science, 365(6448): 83-87
  26. 26.
    Xu Y Z, He X Q, Bai Y, Wang D F, Zhu Q K and Ding X S. 2021. Evaluation of remote-sensing reflectance products from multiple ocean color missions in highly turbid water (Hangzhou bay). Remote Sensing, 13(21): 4267
  27. 27.
    Zhang Y J, Hu C M, Liu Y G, Weisberg R H and Kourafalou V H. 2019. Submesoscale and mesoscale eddies in the florida straits: observations from satellite ocean color measurements. Geophysical Research Letters, 46(22): 13262-13270
  28. 28.
    Zibordi G, Mélin F, Berthon J F, Holben B, Slutsker I, Giles D, D’Alimonte D, Vandemark D, Feng H, Schuster G, Fabbri B E, Kaitala S and Seppälä J. 2009. AERONET-OC: a network for the validation of ocean color primary products. Journal of Atmospheric and Oceanic Technology, 26(8): 1634-1651

Читать полностью

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website