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Validations of the HY-1C COCTS remote sensing reflectance products in coastal waters

  • role： First author第一作者
  • Affiliation：

    School of Oceanography, Shanghai Jiao Tong University, Shanghai 200030, China

    State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China

  • Email：xuyuzhuang007@163.com
  • Introduction：许玉壮，研究方向为水色卫星遥感。E-mail： xuyuzhuang007@163.com
XU Yuzhuang12,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Oceanography, Shanghai Jiao Tong University, Shanghai 200030, China

    State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China

  • Email：hexianqiang@sio.org.cn
  • Introduction：何贤强，研究方向为水色卫星遥感。E-mail： hexianqiang@sio.org.cn
HE Xianqiang12*,  
  • Affiliation：

    School of Oceanography, Shanghai Jiao Tong University, Shanghai 200030, China

    State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China

BAI Yan12,  
  • Affiliation：

    State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China

ZHU Qiankun2,  
  • Affiliation：

    State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China

GONG Fang2

résumé

A large number of global observation data was obtained by HY-1C COCTS since its launch in September 2018. The comprehensive evaluation of the HY-1C/COCTS products is important for further applications. In this study, we used the global in-situ data from AERONET-OC to evaluate the performance of the remote sensing reflectance (Rrs) products of the HY-1C COCTS. Firstly, the AERONET-OC dataset were divided into four optical water types (A, clean water; B, relatively clean water; C, slightly turbid water; D, turbid water) based on a spectral normalization method. Secondly, the AERONET-OC Rrs data and HY-1C/COCTS retrieved Rrs data were matched according to the defined spatial-temporal windows (5×5 box and 1 hour). Finally, the performances of the HY-1C/COCTS Rrs products were quantitatively evaluated in the four optical water types. As a result, good correlation between satellite and in-situ Rrs data was indicated as R values among four types water ranged from 0.680 to 0.879. In type A water, the relatively good consistency between satellite and in-situ Rrs data was observed as average percent difference (PD) at 6.79%, and the average absolute percent difference (APD) at 38.79%. In type B water, slight overestimation of satellite data occurred with PD at 18.73% and APD less than 45%. Underestimation of satellite data was reported in type C water, as negative remote sensing Rrs data in 412 nm and 443 nm bands were with PD at -14.38% and APD at 47.14%. Similarly, in type D water, negative Rrs in 412 nm and 443 nm bands were with PD at -32.35%, and APD at 47.14%, indicating significant underestimation from the satellite data. In addition, difference of accuracy performance of Rrs products in different bands of COCTS for four water types was also observed. Rrs presented good consistency between in situ and COCTS data in band 412 nm and 443 nm for type A water. Better consistency in band 520 nm and 565 nm was observed for type C, D water than type A water while significant underestimation of COCTS Rrs were reported in all four types of water compared to in situ data. Overall, COCTS and in situ Rrs data showed good consistency in clean water, but remained relatively inconsistent in turbid water. Our results also reported that the COCTS inversed Rrs products are slightly overestimated compared with the AERONET-OC in situ data in type A and B water. In contrast, COCTS products slightly underestimated Rrs for type C water but significantly underestimated for type D water. Attention should also be paid to enlarge the evaluated errors, as the AERONET-OC in-situ spectral data was linearly interpolated to match COCTS band. In the future, the hyperspectral in-situ Rrs data should be used to further evaluate the performance of COCTS.

mots-clés

HY-1C;COCTS;remote sensing reflectance;AERONET-OC;validation

References

  1. 1.
    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
  2. 2.
    Balasubramanian S V, Pahlevan N, Smith B, Binding C, Schalles J, Loisel H, Gurlin D, Greb S, Alikas K, Randla M, Bunkei M, Moses W, Nguyễn H, Lehmann M K, O'Donnell D, Ondrusek M, Han T H, Fichot C G, Moore T and Boss E. 2020. Robust algorithm for estimating total suspended solids (TSS) in inland and nearshore coastal waters. Remote Sensing of Environment, 246: 111768
  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.
    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
  5. 5.
    Fan W L，Huang X X and Fu Y T. 2022. Infrared information acquisition technology of Chinese ocean color and temperature scanner of HY-1 satellite. National Remote Sensing Bulletin， 26（8）：1589-1601
  6. 6.
    Harding L W, Magnuson A and Mallonee M E. 2005. SeaWiFS retrievals of chlorophyll in Chesapeake Bay and the mid-Atlantic bight. Estuarine, Coastal and Shelf Science, 62(1/2): 75-94
  7. 7.
    Hernández W J, Ortiz-Rosa S, Armstrong R A, Geiger E F, Eakin C M and Warner R A. 2020. Quantifying the effects of hurricanes Irma and Maria on coastal water quality in Puerto Rico using moderate resolution satellite sensors. Remote Sensing, 12(6): 964
  8. 8.
    Hlaing S, Harmel T, Gilerson A, Foster R, Weidemann A, Arnone R, Wang M H and Ahmed S. 2013. Evaluation of the VIIRS ocean color monitoring performance in coastal regions. Remote Sensing of Environment, 139: 398-414
  9. 9.
    Kyryliuk D and Kratzer S. 2019. Evaluation of Sentinel-3A OLCI products derived using the Case-2 regional coastcolour processor over the Baltic Sea. Sensors, 19(16): 3609
  10. 10.
    Ladner S D, Arnone R, Vandermeulen R, Martinolich P, Lawson A, Bowers J, Crouti R, Ondrusek M and Fargion G. 2014. Inter-satellite comparison and evaluation of Navy SNPP VIIRS and MODIS-Aqua ocean color properties//Proceedings Volume 9111, Ocean Sensing and Monitoring VI. Baltimore, Maryland: SPIE
  11. 11.
    Li T J. 2012. China offshore marine: marine optical properties and remote sensing. Beijing: China Ocean Press: 193-196
  12. 12.
    Liu H Z, He X Q, Li Q Q, Hu X J, Ishizaka J, Kratzer S, Yang C, Shi T Z, Hu S B, Zhou Q M and Wu G F. 2022. Evaluation of ocean color atmospheric correction methods for Sentinel-3 OLCI using global automatic in situ observations. IEEE Transactions on Geoscience and Remote Sensing, 60: 1-19
  13. 13.
    Liu M, Guan L, Liu J, Song Q, Ma C, Li N. 2021. First Assessment of HY-1C COCTS Thermal Infrared Calibration Using MetOp-B IASI. Remote Sensing. 13(4):635.
  14. 14.
    Shen Y F, Liu J Q, Ding J, Jiao J N, Sun S J and Lu Y C. 2020. HY-1C COCTS and CZI observation of marine oil spills in the South China Sea. Journal of Remote Sensing，24(8): 933-944
  15. 15.
    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
  16. 16.
    Thuillier G, Hersé M, Labs D, Foujols T, Peetermans W, Gillotay D, Simon P C and Mandel H. 2003. The solar spectral irradiance from 200 to 2400 nm as measured by the SOLSPEC spectrometer from the Atlas and Eureca Missions. Solar Physics, 214(1): 1-22
  17. 17.
    Wang M H, Liu X M, Jiang L D, Son S, Sun J Q, Shi W, Tan L Q, Naik P, Mikelsons K, Wang X L and Lance V. 2014. Evaluation of VIIRS ocean color products//Proceedings Volume 9261, Ocean Remote Sensing and Monitoring from Space. Beijing: SPIE
  18. 18.
    Wang S L, Li J S, Zhang B, Spyrakos E, Tyler A N, Shen Q, Zhang F F, Kuster T, Lehmann M K, Wu Y H and Peng D L. 2018. Trophic state assessment of global inland waters using a MODIS-derived Forel-Ule index. Remote Sensing of Environment, 217: 444-460
  19. 19.
    Wei J W, Lee Z and Shang S L. 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
  20. 20.
    Xu Y Zh , 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 [DOI : ]
  21. 21.
    Zhong S K，Lyu H，Yang Z Q，Li Y Y，Xu J F and Miao S. 2022. Remote sensing estimation method of organic suspended matter concentration in inland lakes based on Sentinel-3 OLCI data. National Remote Sensing Bulletin， 26（1）：155-167
  22. 22.
    Zibordi G, Holben B, Mélin F, D'Alimonte D, Berthon J F, Slutsker I and Giles D. 2010. AERONET-OC: an overview. Canadian Journal of Remote Sensing, 36(5): 488-497
  23. 23.
    Zibordi G, Mélin F and Berthon J F. 2018. A regional assessment of OLCI data products. IEEE Geoscience and Remote Sensing Letters, 15(10): 1490-1494
  24. 24.
    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
  25. 25.
    Zibordi G, Strömbeck N, Mélin F and Berthon J F. 2006. Tower-based radiometric observations at a coastal site in the Baltic Proper. Estuarine, Coastal and Shelf Science, 69(3/4): 649-654

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