收稿：2018-10-09，
录用：2018-12-13，
纸质出版：2019-09
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卫星遥感反演海洋内部多时相、大尺度热力结构信息对于了解海洋内部复杂、多维的动力过程有重要意义。本文采用随机森林回归模型，利用卫星遥感观测的海表参量(海表高度异常(SSHA)、海表温度异常(SSTA)、海表盐度异常(SSSA)和海表风场异常(SSWA))，反演不同季节、不同深度层位(1000 m深度以上)的海洋次表层温度异常(STA)，并用Argo实测数据作精度验证，采用均方根误差(RMSE)、归一化均方根误差(NRMSE)以及决定系数(
R
2
)评价模型在全球及洋盆尺度上的反演精度。结果显示，全球海洋16个深度层位的平均
R
2
在春、夏、秋、冬四季分别为0.53、0.60、0.54、0.66，NRMSE分别为0.051、0.031、0.043、0.044。 随着季节的变化，模型反演性能有所波动。模型在印度洋的反演效果最佳，不同季节、不同深度层位上的平均
R
2
和RMSE分别为0.71和0.18 ℃，而大西洋的反演精度最低，平均
R
2
和RMSE分别为0.46和0.25 ℃。研究表明随机森林模型适用于全球不同季节的STA遥感反演，且在不同洋盆上均有较好的反演效果；不同季节上，上层STA有明显变化信号，空间异质性显著，但300 m以深，STA信号较弱且基本不随季节变化。本研究可为长时序、大尺度海洋内部参量信息遥感反演与重建提供依据，有助于进一步发展深海遥感方法。
Improving ocean interior observation resolution via satellite remote sensing is essential because of the limitation and sparsity of ocean interior observation. Retrieving the multi-temporal and large-scale thermal structure information of the subsurface ocean on the basis of satellite remote sensing is of great importance in understanding the complex and multi-dimensional dynamic processes within the ocean. This task requires a robust model with strong spatiotemporal applicability to provide technical support based on satellite observations. This study adopts a random forest regression model
namely
an advanced machine learning algorithm
to predict global Subsurface Temperature Anomaly (STA) at different depth levels (upper 1
000 m) in different seasons in 2010 from multisource sea surface parameters (sea surface height anomaly
SSHA; sea surface temperature anomaly
SSTA; sea surface salinity anomaly
SSSA; sea surface wind anomaly
SSWA) based on satellite observations. We use the in-situ Argo data for performance measurement and accuracy validation by combined use of the root mean square error (RMSE)
normalized root-mean-square error (NRMSE) and coefficient of determination (
R
2
) at global and ocean basin scales. For model accuracy
the results show that the average
R
2
and NRMSE of 16 depth levels are 0.53/0.60/0.54/0.66 and 0.051/0.031/0.043/0.044 for global ocean in spring/summer/autumn/winter. With the evolution of seasons
the model performance promotes first
declines
and then promotes
a trend that may be caused by the El Niño and La Niña phenomena and the transformation between them. The best performance of the model occurs in the Indian Ocean with the average
R
2
and RMSE of 0.71 and 0.18 °C
respectively
whereas accuracy in the Atlantic is the lowest
with average
R
2
and RMSE of 0.46 and 0.25 °C at different depth levels in different seasons. This study suggests that the random forest model is suitable for retrieving ocean subsurface temperature anomalies in different seasons and can achieve good performance in different ocean basins. STA has distinctive variation signal in the upper ocean (above 300 m) and spatial heterogeneity is considerable in different seasons. However
in the subsurface and deeper layers (below 300 m)
STA variation signal is weak over different seasons. This study can provide a basis for remote sensing estimation of STA and further promote the reconstruction of long-term and large-scale ocean internal parameter information (such as thermohaline structure). It can also help develop the subsurface and deeper ocean remote sensing technique.
Akbari E, Alavipanah S K, Jeihouni M, Hajeb M, Haase D and Alavipanah S. 2017. A review of ocean/sea subsurface water temperature studies from remote sensing and non-remote sensing methods. Water, 9(12): 936
Atlas R, Hoffman R N, Ardizzone J, Leidner S M, Jusem J C, Smith D K and Gombos D. 2011. A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications. Bulletin of the American Meteorological Society, 92(2): 157–174
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
Belgiu M and Drăguţ L. 2016. Random forest in remote sensing: a review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114: 24–31
Breiman L. 2001. Random forests. Machine Learning, 45(1): 5–32
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
Chen X Y and Tung K K. 2014. Varying planetary heat sink led to global-warming slowdown and acceleration. Science, 345(6199): 897–903
Drijfhout S S, Blaker A T, Josey S A, Nurser A J G, Sinha B and Balmaseda M A. 2014. Surface warming hiatus caused by increased heat uptake across multiple ocean basins. Geophysical Research Letters, 41(22): 7868–7874
Feng Q L, Liu J T and Gong J H. 2015. UAV remote sensing for urban vegetation mapping using random forest and texture analysis. Remote Sensing, 7(1): 1074–1094
Kim B, Kang S, Eom J and Lee S. 2018. Evaluation of respiratory-correlated 4D digital tomosynthesis imaging technique for image-guided radiation therapy. Optik, 171: 247–252
李崇银, 穆明权. 2001. 赤道印度洋海温偶极子型振荡及其气候影响. 大气科学, 25(4): 433–443
Li C Y and Mu M Q. 2001. The dipole in the equatorial Indian Ocean and its impacts on climate. Chinese Journal of Atmospheric Sciences, 25(4): 433–443
黎文娥, 苏华, 汪小钦, 严晓海. 2017. 多源卫星观测的全球海洋次表层温度异常信息提取. 遥感学报, 21(6): 881–891
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
刘磊. 2016. 利用卫星遥感海表信息对海洋内部状态的反演研究. 北京: 中国科学院大学
Liu L. 2016. Inversion of the Internal State of the Ocean Using Satellite Remote Sensing Sea Surface Information. Beijing: University of Chinese Academy of Sciences
Liu L, Peng S Q, Huang R X. 2017. Reconstruction of ocean's interior from observed sea surface information. Journal of Geophysical Research: Oceans, 122(2): 1042–1056
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
中国气象局国家气候中心. ENSO监测简报(第41期)[EB/OL]. (2010-07-28). http://www.doc88.com/p-9117178739656.html
National Climate Center. ENSO Report (No. 41)[EB/OL]. (2010-07-28). http://www.doc88.com/p-9117178739656.html
Oke P R, Schiller A, Griffin D A and Brassington G B. 2005. Ensemble data assimilation for an eddy‐resolving ocean model of the Australian region. The Quarterly Journal of the Royal Meteorological Society, 131(613): 3301–3311
齐雁冰, 王茵茵, 陈洋, 刘姣姣, 张亮亮. 2017. 基于遥感与随机森林算法的陕西省土壤有机质空间预测. 自然资源学报, 32(6): 1074–1086
Qi Y B, Wang Y Y, Chen Y, Liu J J and Zhang L L. 2017. Soil organic matter prediction based on remote sensing data and random forest model in Shaanxi province. Journal of Natural Resources, 32(6): 1074–1086
沈润平, 郭佳, 张婧娴, 李洛晞. 2017. 基于随机森林的遥感干旱监测模型的构建. 地球信息科学学报, 19(1): 125–133
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
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
Su H, Huang L J, Li W E, Yang X and Yan X H. 2018a. Retrieving ocean subsurface temperature using a satellite-based geographically weighted regression model. Journal of Geographical Research: Oceans, 123(8): 5180–5193
Su H, Li W E and Yan X H. 2018b. Retrieving temperature anomaly in the global subsurface and deeper ocean from satellite observations. Journal of Geophysical Research: Oceans, 123(1): 399–410
Takano A, Yamazaki H, Nagai T and Honda O. 2009. A method to estimate three-dimensional thermal structure from satellite altimetry data. Journal of Atmospheric and Oceanic Technology, 26(12): 2655–2664
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
Wang L A, Zhou X D, Zhu X K, Dong Z D and Guo W S. 2016. Estimation of biomass in wheat using random forest regression algorithm and remote sensing data. The Crop Journal, 4(3): 212–219
Wu X B, Yan X H, Jo Y H, 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
詹思玙, 齐琳琳, 卢伟. 2017. 基于CCMP资料和现场观测资料的西北太平洋海面风场特征分析. 海洋预报, 34(2): 10–20
Zhan S Y, Qi L L and Lu W. 2017. Analysis of sea surface wind in Northwest Pacific based on CCMP satellite data. Marine Forecasts, 34(2): 10–20
周志华. 2016. 机器学习. 北京: 清华大学出版社: 178-188
Zhou Z H. 2016. Machine Learning. Beijing: Tsinghua University Press: 178-188
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