Précédent|Prochain

Remote sensing prediction of global subsurface thermohaline and the impact of longitude and latitude based on LightGBM

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China

  • Email：905335312@qq.com
  • Introduction：张天一，1995年生，男，硕士研究生，研究方向为海洋环境遥感。E-mail：905335312@qq.com
ZHANG Tianyi1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China

  • Email：suhua@fzu.edu.cn
  • Introduction：苏华，1985年生，男，副研究员，研究方向为海洋环境遥感。E-mail：suhua@fzu.edu.cn
SU Hua1*,  
  • Affiliation：

    Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350108, China

YANG Xin1,  
  • Affiliation：

    Xiamen University / University of Delaware Joint Institute for Coastal Research and Management (Joint-CRM), Xiamen University, Xiamen 361005, China

YAN Xiaohai2

résumé

Satellite observation data are increasingly used to derive and predict thermohaline information within the ocean due to the development of satellite technology. However, the effective improvement of prediction accuracy in satellite remote sensing is still challenging. A model with strong spatiotemporal applicability and high robustness for subsurface thermohaline estimation is necessary due to the complex and highly dynamic processes in the ocean.In this study, a new LightGBM method combined with random forest algorithm is used to predict global subsurface temperature and salinity anomalies in the upper 1000 m depth based on remote sensing and Argo float data. The proposed method uses multisource sea surface parameters, including Sea Surface Height Anomaly (SSHA), Sea Ssurface Temperature Anomaly (SSTA), Sea Surface Salinity Anomaly (SSSA), and northward and eastward components of sea surface wind anomaly (USSWA and VSSWA), combined with longitude and latitude data (LON and LAT) as predictor variables and Argo gridded data as training and testing labels for model construction and prediction. This study creates five-parameter model (SSTA, SSHA, SSSA, USSWA, and VSSWA), six-parameter model with latitude (LAT, SSTA, SSHA, SSSA, USSWA, and VSSWA), six-parameter model with longitude (LON, SSTA, SSHA, SSSA, USSWA, and VSSWA), and seven-parameter model with longitude and latitude (LON, LAT, SSTA, SSHA, SSSA, USSWA, and VSSWA) to analyze and evaluate the role of LON + LAT in STA and SSA prediction using LightGBM and RF models.Using the monotemporal LightGBM model to predict STA, the average R2 of the seven-parameter model, six-parameter model with latitude, six-parameter model with longitude, five-parameter model is 0.980, 0.922, 0.937, 0.776 and the average RMSE is 0.072 ℃, 0.141 ℃, 0.127 ℃, 0.240 ℃. The average R2 in the SSA prediction is 0.963, 0.846, 0.872, 0.545, and the average RMSE is 0.012 psu, 0.025 psu, 0.022 psu, 0.042 psu. The average R2 of the seven-parameter model, six-parameter model with latitude, six-parameter model with longitude, five-parameter model when using time-series LightGBM model to predict STA is 0.703, 0.655, 0.585, 0.523, and the average RMSE is 0.298 ℃, 0.317 ℃, 0.356 ℃, 0.378 ℃. The average R2 in the SSA prediction is 0.426, 0.277, 0.197, 0.103, and the average RMSE is 0.050 psu, 0.057 psu, 0.059 psu, 0.064 psu. Hence, the seven-parameter model demonstrated the best performance. The maximum R2 and minimum RMSE in the seven-parameter LightGBM model are 0.992, 0.981 and 0.022 ℃, 0.004 psu in the monotemporal STA, SSA prediction. Meanwhile, the maximum R2 and minimum RMSE are 0.817, 0.574 and 0.092 ℃, 0.013 psu in the time-series STA, SSA prediction. The prediction accuracy of the model decreases gradually with increasing depth.This study suggested that LON + LAT significantly contribute to both STA and SSA prediction, but differently impact on respective STA and SSA prediction. The contribution of LON + LAT to the model increases with depth in the monotemporal and time-series STA prediction while maintaining a large contribution to the model at different depths in the monotemporal and time-series SSA prediction. LON makes a larger contribution than LAT in the monotemporal STA and SSA prediction, while LAT plays a more significant role than LON in the time-series STA and SSA prediction. Furthermore, LightGBM outperforms RF and is more robust in the subsurface thermohaline prediction.

mots-clés

subsurface ocean;thermohaline anomaly;LightGBM;remote sensing prediction;longitude and latitude

References

  1. 1.
    Ali M M, Swain D and Weller R A. 2004. Estimation of ocean subsurface thermal structure from surface parameters: a neural network approach. Geophysical Research Letters, 31(20): L20308
  2. 2.
    Balmaseda M A, Trenberth K E and Källén E. 2013. Distinctive climate signals in reanalysis of global ocean heat content. Geophysical Research Letters, 40(9): 1754-1759
  3. 3.
    Bao S L, Zhang R, Wang H Z, Yan H Q, Yu Y and Chen J. 2019. Salinity profile estimation in the Pacific Ocean from satellite Surface salinity observations. Journal of Atmospheric and Oceanic Technology, 36(1): 53-68
  4. 4.
    Breiman L. 2001. Random forests. Machine Learning, 45(1): 5-32
  5. 5.
    Chapman C and Charantonis A A. 2017. Reconstruction of subsurface velocities from satellite observations using iterative self-organizing maps. IEEE Geoscience and Remote Sensing Letters, 14(5): 617-620
  6. 6.
    Chen C, Yang K D, Ma Y L and Wang Y. 2018. Reconstructing the subsurface temperature field by using sea surface data through self-organizing map method. IEEE Geoscience and Remote Sensing Letters, 15(12): 1812-1816
  7. 7.
    Chen X Y and Tung K K. 2014. Varying planetary heat sink led to global-warming slowdown and acceleration. Science, 345(6199): 897-903
  8. 8.
    Cheng L J and Zhu J. 2014. Uncertainties of the ocean heat content estimation induced by insufficient vertical resolution of historical ocean subsurface observations. Journal of Atmospheric and Oceanic Technology, 31(6): 1383-1396
  9. 9.
    Cheng L J, Zhu J, Abraham J, Trenberth K E, Fasullo J T, Zhang B, Yu F J, Wan L Y, Chen X R and Song X Z. 2019. 2018 Continues record global ocean warming. Advances in Atmospheric Sciences, 36(3): 249-252
  10. 10.
    Ding Y H. 2016. Analysis of the process and mechanisms of genesis and development for 2014-2016 mega El Niño event. Transactions of Atmospheric Sciences, 39(6): 722-734
  11. 11.
    Fox D N, Teague W J, Barron C N, Carnes M R and Lee C M. 2002. The modular ocean data assimilation system (MODAS). Journal of Atmospheric and Oceanic Technology, 19(2): 240-252
  12. 12.
    Guo L K, Qi M, Finley T, Wang T F, Chen W, Ma W D, Ye Q W and Liu T Y. 2017. LightGBM: a highly efficient gradient boosting decision tree//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, CA, USA: ACM: 3149-3157
  13. 13.
    Klemas V and Yan X H. 2014. Subsurface and deeper ocean remote sensing from satellites: an overview and new results. Progress in Oceanography, 122: 1-9
  14. 14.
    Li W E, Su H, Wang X Q and Yan X H. 2017. Estimation of global subsurface temperature anomaly based on multisource satellite observations. Journal of Remote Sensing, 21(6): 881-891
  15. 15.
    Liu L. 2016. Ocean’s Interior Reconstruction from Satellite-derived Surface Information. Beijing: University of Chinese Academy of Sciences
  16. 16.
    Lu W F, Su H, Yang X and Yan X H. 2019. Subsurface temperature estimation from remote sensing data using a clustering-neural network method. Remote Sensing of Environment, 229: 213-222
  17. 17.
    Maes C, Behringer D, Reynolds R W and Ji M. 2000. Retrospective analysis of the salinity variability in the western tropical pacific ocean using an indirect minimization approach. Journal of Atmospheric and Oceanic Technology, 17(4): 512-524
  18. 18.
    Meehl G A, Arblaster J M, Fasullo J T, Hu A X and Trenberth K E. 2011. Model-based evidence of deep-ocean heat uptake during surface-temperature hiatus periods. Nature Climate Change, 1(7): 360-364
  19. 19.
    Roemmich D and Gilson J. 2009. The 2004-2008 mean and annual cycle of temperature, salinity, and steric height in the global ocean from the Argo Program. Progress in Oceanography, 82(2): 81-100
  20. 20.
    Shen R P, Guo J, Zhang J X and Li L X. 2017. Construction of a drought monitoring model using the random forest based remote sensing. Journal of Geo-Information Science, 19(1): 125-133
  21. 21.
    Su H, Li W and Yan X H. 2018. Retrieving temperature anomaly in the global subsurface and deeper ocean from satellite observations. Journal of Geophysical Research: Oceans, 123(1): 399-410
  22. 22.
    Su H, Wu X B, Lu W F, Zhang W W and Yan X H. 2017. Inconsistent subsurface and deeper ocean warming signals during recent global warming and hiatus. Journal of Geophysical Research: Oceans, 122(10): 8182-8195
  23. 23.
    Su H, Wu X B, Yan X H and Kidwell A. 2015. Estimation of subsurface temperature anomaly in the Indian Ocean during recent global surface warming hiatus from satellite measurements: a support vector machine approach. Remote Sensing of Environment, 160: 63-71
  24. 24.
    Su H, Yang X, Lu W F and Yan X H. 2019. Estimating subsurface thermohaline structure of the global ocean using surface remote sensing observations. Remote Sensing, 11(13): 1598
  25. 25.
    Wang G J, Cheng L J, Abraham J and Li C Y. 2017. Consensuses and discrepancies of basin-scale ocean heat content changes in different ocean analyses. Climate Dynamics, 50(7): 2471-2487
  26. 26.
    Wang J B, Flierl G R, LaCasce J H, McClean J L and Mahadevan A. 2013. Reconstructing the ocean’s interior from surface data. Journal of Physical Oceanography, 43(8): 1611-1626
  27. 27.
    Wu X B, Yan X H, Jo Y H and Liu W T. 2012. Estimation of subsurface temperature anomaly in the north Atlantic using a self-organizing map neural network. Journal of Atmospheric and Oceanic Technology, 29(11): 1675-1688
  28. 28.
    Yang X, Su H, Li W E, Huang L J, Wang X Q and Yan X H. 2019. Seasonal-spatial variations in satellite-derived global subsurface temperature anomalies. Journal of Remote Sensing, 23(5): 997-1010
  29. 29.
    Zhao S Y, An R and Zhu M R. 2019. Urban change detection by aerial remote sensing using combining features of pixel-depth-object. Acta Geodaetica et Cartographica Sinica, 48(11): 1452-1463

Lire l'article complet

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