предыдущий|следующий

Influence of temporal and spatial variation of sea surface temperature on validation errors

  • role： First author第一作者
  • Affiliation：

    Ocean college, Zhejiang University, Zhoushan 316021, China

    Institute of Remote Sensing and Earth Science, Hangzhou Normal University, Hangzhou 311121, China

  • Email：gangzg@163.com
  • Introduction：蒋锦刚，1981年生，男，助理研究员，研究方向为海洋水色遥感及不确定性研究。E-mail：gangzg@163.com
Jingang JIANG14,  
  • Affiliation：

    Institute of Remote Sensing and Earth Science, Hangzhou Normal University, Hangzhou 311121, China

Yao XU4,  
  • Affiliation：

    The Second Surveying and Mapping Institute of Zhejiang Province, Hangzhou 310012, China

Chenhui NIE3,  
  • Affiliation：

    The Second Surveying and Mapping Institute of Zhejiang Province, Hangzhou 310012, China

Xiaojun PAN3,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Satellite Ocean Environment Dynamics, Hangzhou 310012, China

    Institute of Remote Sensing and Earth Science, Hangzhou Normal University, Hangzhou 311121, China

  • Email：hznu_bzhou@126.com
  • Introduction：周斌，1972年生，男，教授，研究方向为海洋水色遥感理论和应用研究。E-mail：hznu_bzhou@126.com
Bin ZHOU24*

реферат

Ocean remote sensing is the only effective method for monitoring the marine ecosystem on a global scale in the long term. This method has played a crucial role in the research of carbon cycle, global changing, and ocean acidification and its biological impacts. The validation of ocean color products is quite important for the improvement of the algorithm and the reliability of further application. Given the complex and highly dynamic optical marine characteristics, especially for case Ⅱ water, the evaluation of accuracy and the expression of remote sensing products have become difficult, and considerable research has focused in this field. This study aims to create a scientific evaluation method of the representativeness of ground stations and the heterogeneity of remote sensing pixels of sea surface temperature (SST) verification. The temporal and spatial variation parameters, including the statistical standard deviation of time series, the spatial variation index, and the frontal index, are designed. The analytic hierarchy process for determining the weight of different indices and the method for regionalizing the interval of a spatiotemporal variation. Verification datasets are classified using a distribution map of the spatiotemporal variation of space level division. Result of the experimental data shows that spatiotemporal variation is a direct cause of errors. Considering the strong spatiotemporal variation, numerous verification errors are introduced in the verification process, which uses data from different variation grade divisions, and the relative error of the verification result can reach more than 13.08%. The spatial distribution of spatiotemporal variation is large in the Yellow and Bohai Sea regions and small in the East and South China Seas. The seasonal variation is strong in winter and spring and weak in summer and autumn. In this study, we propose models and methods to reasonably select representative validation datasets and verify the scientific nature of the method from the experimental data. In the region where the spatiotemporal variability is intense, the verification errors are large. These errors are not completely remote sensing product errors. The results of verification are not representative and cannot really reflect the error characteristics of remote sensing products. For SST ocean remote sensing product verification, spatiotemporal variability and its contribution to the validation error must be considered. Moreover, a reasonable selection of evaluation data set, scientific method, and representative test area must be verified.

ключеви́че слова́

ocean remote sensing;sea surface temperature;spatial-temporal variance;accuracy and uncertainty;level division;validation

References

  1. 1.
    Alvera-Azcárate A, Barth A, Rixen M and Beckers J M. 2005. Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions: application to the Adriatic Sea surface temperature. Ocean Modelling, 9(4): 325–346
  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.
    Beckers J M and Rixen M. 2003. EOF calculations and data filling from incomplete oceanographic datasets. Journal of Atmospheric and Oceanic Technology, 20(12): 1839–1856
  4. 4.
    Canadell J G and Dickinson R. 2006. Global carbon project. Science framework and implementation. Global Carbon Project Science Framework and Implementation Earth System Science Partnership: 80
  5. 5.
    Chen L Q, Yang X L, Zhang Y H, Li W, Lin Q, Lin H M, Xu S Q, Zhan J Q. 2008. Observation Technology of CO2 fluxes in the ocean and atmosphere. Ocean Technology, 27(4): 9–12 (
  6. 6.
    Chen L Q, Xu S Q, Gao Z Y, Chen H Y, Zhang Y H, Zhan J Q and Li W. 2011. Estimation of monthly air-sea CO2 flux in the southern Atlantic and Indian Ocean using in-situ and remotely sensed data. Remote Sensing of Environment, 115(8): 1935–1941
  7. 7.
    Chen Q L, Tang J W and Wang X N. 1998. Review on the calibration and validation for ocean color sensing. Ocean Technology, 17(3): 13–26 (
  8. 8.
    Ding Y Z, Wang Z H, Mao Z H and Pan D L. 2009. Reconstruction of incomplete Satellite SST data sets based on EOF method. Acta Oceanologica Sinica, 28(2): 36–44
  9. 9.
    Else B G T, Yackel J J and Papakyriakou T N. 2008. Application of satellite remote sensing techniques for estimating air–sea CO2 fluxes in Hudson Bay, Canada during the ice-free season. Remote Sensing of Environment, 112(9): 3550–3562
  10. 10.
    Feely R A, Sabine C L, Lee K, Berelson W, Kleypas J, Fabry V J and Millero F J. 2004. Impact of anthropogenic CO2 on the CaCO3 system in the oceans. Science, 305(5682): 362–366
  11. 11.
    Joos F, Plattner G K, Stocker T F, Marchal O and Schmittner A. 1999. Global warming and marine carbon cycle feedbacks on future atmospheric CO2. Science, 284(5413): 464–467
  12. 12.
    Li S H, Wang H and Xu W D. 2000. Research and progress in satellite ocean color remote sensing. Advance in Earth Sciences, 15(2): 190–196 (
  13. 13.
    Li Y M, Wang Q, Huang J Z, Lv H and Wei Y C. 2010. Water Optical Characteristics and Remote Sensing of Taihu Lake. Beijing: Science Press
  14. 14.
    Li Z L, Duan S B, Tang B H, Wu H, Ren H Z, Yan G J, Tang R L and Leng P. 2016. Review of methods for land surface temperature derived from thermal infrared remotely sensed data. Journal of Remote Sensing, 20(5): 899–920 (
  15. 15.
    Ma J, Zhou J, Liu S M and Wang Y J. 2017. Review on validation of remotely sensed land surface temperature. Advances in Earth Science, 32(6): 615–629 (
  16. 16.
    Mao Z H, Zhu Q K and Pan D L. 2003. A temperature error control technology for an operational satellite application system. Acta Oceanologica Sinica, 25(5): 49–57 (
  17. 17.
    Olsen A, Triñanes J A and Wanninkhof R. 2004. Sea–air flux of CO2 in the Caribbean Sea estimated using in situ and remote sensing data. Remote Sensing of Environment, 89(3): 309–325
  18. 18.
    Pan D L and Gong F. 2011. Progress in application technology of satellite ocean remote sensing in China. Journal of Hangzhou Normal University (Natural Science), 10(1): 1–10 (
  19. 19.
    Reynolds R W and Smith T M. 1994. Improved global sea surface temperature analyses using optimum interpolation. Journal of Climate, 7(6): 929–948
  20. 20.
    Sun F Q, Zhang C Y, Shang S P and Shang S L. 2011. Primary validation of AVHRR/MODIS/TMI SST for part of the northwest pacific. Journal of Xiamen University (Natural Science), 46(S1): 1–5 (
  21. 21.
    Sun L, Wang X M, Guo M H and Tang J W. 2009. MODIS ocean color product validation around the Yellow Sea and East China Sea. Journal of Lake Science, 21(2): 298–306 (
  22. 22.
    Wang H J. 2011. Applicability inspection of remote-sensing single-parameter algorithm for air-sea CO2 flux in open ocean. Journal of Oceanography in Taiwan Strait, 30(2): 286–291 (
  23. 23.
    Xue L. 2011. Surface CO2 in the Yellow Sea and Buoy CO2 in the South Atlantic Bight. Qingdao: Ocean University of China
  24. 24.
    Zhai S Ｋ, Meng W and Yu Z G. 2008. The Environment of the Yangtze Estuary After the First Phase of the Three Gorges Project. Beijing: Science Press
  25. 25.
    Zhou G H, Tang J W, Tian G L, Li J and Liu Q H. 2009. Uncertainty analysis of inland water quality remote sensing: a review. Advances in Earth Science, 24(2): 150–158 (
  26. 26.
    Zhou X, Yang X F, Cheng L, Li Z W. 2012. Sensitivity analysis and validation of the single channel physical method for retrieving sea surface temperature. Journal of Infrared and Millimeter Waves, 31(1): 91–96 (
  27. 27.
    Zhu Y, Shang S L, Zhai W D and Dai M H. 2009. Satellite-derived surface water pCO2 and air–sea CO2 fluxes in the northern South China Sea in summer. Progress in Natural Science, 19(6): 775–779

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