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基于随机森林方法反演墨西哥湾海表盐度

吴芳芳 傅智一 胡林舒 张丰 杜震洪 刘仁义

吴芳芳,傅智一,胡林舒,等. 基于随机森林方法反演墨西哥湾海表盐度[J]. 海洋学报,2021,43(9):126–136 doi: 10.12284/hyxb2021146
引用本文: 吴芳芳,傅智一,胡林舒,等. 基于随机森林方法反演墨西哥湾海表盐度[J]. 海洋学报,2021,43(9):126–136 doi: 10.12284/hyxb2021146
Wu Fangfang,Fu Zhiyi,Hu Linshu, et al. Retrieval of sea surface salinity in the Gulf of Mexico based on random forest method[J]. Haiyang Xuebao,2021, 43(9):126–136 doi: 10.12284/hyxb2021146
Citation: Wu Fangfang,Fu Zhiyi,Hu Linshu, et al. Retrieval of sea surface salinity in the Gulf of Mexico based on random forest method[J]. Haiyang Xuebao,2021, 43(9):126–136 doi: 10.12284/hyxb2021146

基于随机森林方法反演墨西哥湾海表盐度

doi: 10.12284/hyxb2021146
基金项目: 国家重点研发计划重点专项(2018YFB0505000);国家自然科学基金(41671391,41922043,41871287)
详细信息
    作者简介:

    吴芳芳(1994-),女,贵州省长顺县人,从事海洋GIS研究。E-mail:wyl19952021@126.com

    通讯作者:

    张丰(1977-),女,副教授,从事海洋GIS研究。E-mail: zfcarnation@zju.edu.cn

  • 中图分类号: P731.12

Retrieval of sea surface salinity in the Gulf of Mexico based on random forest method

  • 摘要: 盐度是表征物理和生物地球化学过程的重要参数之一,光学遥感可满足较高分辨率的监测需要并避免射频干扰问题,为沿海水域的海表盐度研究提供可行的途径。本文基于MODIS-Aqua的412 nm、443 nm、488 nm、555 nm和667 nm波段的遥感反射率(Rrs412、Rrs443、Rrs488、Rrs555、Rrs667)、海表温度以及实测的海表盐度数据构建随机森林模型,基于模型结果分析墨西哥湾海表盐度时空异质性及海表盐度与影响因子(海表温度和遥感反射率)之间的相关关系。研究结果表明:(1)随机森林模型能较准确地反演墨西哥湾海表盐度,其均方根误差为0.335,决定系数为0.931;(2)湾区海表盐度空间分布呈近岸−河口低、离岸高,环状向内增值的态势,其变化受河流流量、风力以及环流的影响;(3)海表温度与海表盐度存在较强的相关性,海表温度对海表盐度的反演影响显著;(4)海表温度、遥感反射率与海表盐度的相关性呈现空间异质性。
  • 图  1  研究区概况

    黑色曲线代表100 m等深线;水深大于100 m区域用浅蓝色表示;黑色带箭头曲线代表环流;圆环代表环流扩张时引起的涡流

    Fig.  1  Overview of the study area

    The black curve is for the 100 m isobath; areas with water depths greater than 100 m are shaded in light blue; the curve with black arrow is for circulation; the ring is for the eddy current caused by circulation expansion

    图  2  实测海表盐度数据分布

    Fig.  2  Spatial distribution of field sea surface salinity

    图  3  基于随机森林算法的海表盐度反演模型构建流程图

    Fig.  3  Flow chart of sea surface salinity retrieval model based on random forest algorithm

    图  4  随机森林模型性能对比

    Fig.  4  Performance comparison of random forest model

    图  5  随机森林模型在河口区域与环流区域的性能对比

    Fig.  5  Random forest model performance comparison in estuary region and circulation region

    图  6  4种模型反演的海表盐度验证

    Fig.  6  Validation of sea surface salinity retrieved by four models

    图  7  随机森林模型生成的2018年墨西哥湾年平均海表盐度反演图

    灰色代表陆地

    Fig.  7  Annual mean sea surface salinity generated by the random forest model in the Gulf of Mexio in 2018

    Land are shaded in grey

    图  8  随机森林模型生成的2018年墨西哥湾月平均海表盐度反演图

    Fig.  8  Monthly mean sea surface salinity generated by the random forest model in the Gulf of Mexio in 2018

    图  9  影响因子重要性排序

    Fig.  9  Importance ranking of influence factors

    图  10  不同地理分区海表盐度影响因子的贡献度

    Fig.  10  Contribution of each factor to sea surface salinity in different geographical regions

    表  1  实测海表盐度数据来源航次信息

    Tab.  1  The source and voyage information of field sea surface salinity

    航次名称船名时间范围观测点个数
    EQ17M/V Celebrity Equinox2018年1月1−6日2 179
    AS17M/V Allure of the Seas2018年1月4−7日1 198
    GU1801_Leg1R/V Gordon Gunter2018年1月14−22日4 178
    GU1801_Leg2R/V Gordon Gunter2018年1月26日至2月9日7 421
    GU1801_Leg3R/V Gordon Gunter2018年2月12−27日5 428
    GU1801_Leg4R/V Gordon Gunter2018年3月1−16日7 941
    GU1802R/V Gordon Gunter2018年6月24日至7月9日7 609
    GU1803−transitR/V Gordon Gunter2018年7月11−14日1 340
    GU1803_Leg1R/V Gordon Gunter2018年7月20日至8月3日7 196
    GU1803_Leg2R/V Gordon Gunter2018年8月6−19日4 727
    GU1804R/V Gordon Gunter2018年8月23−31日4 445
    GU1805_Leg1R/V Gordon Gunter2018年9月2−9日3 563
    GU1805_Leg2R/V Gordon Gunter2018年9月11−30日9 656
    EQ18M/V Celebrity Equinox2018年6月1日至12月22日872
    GU1806R/V Gordon Gunter2018年11月10日至1月4日10 127
    观测点总数77 883
    用于模型的开发与验证的点数7 963
    下载: 导出CSV

    表  2  实测数据与卫星数据统计信息

    Tab.  2  Statistics of measured data and satellite data

    变量实测盐度海表温度/℃Rrs412/sr−1Rrs443/sr−1Rrs488/sr−1Rrs555/sr−1Rrs667/sr−1
    最大值36.58732.5250.030 180.036 440.045 420.031 410.010 12
    最小值22.32818.270−0.001 74−0.000 420.000 85−0.000 11−0.000 65
    中位数36.14726.4750.008 000.007 000.005 410.001 460.000 15
    平均值35.55426.5140.007 510.006 710.005 350.001 680.000 21
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-02-03
  • 修回日期:  2021-06-16
  • 网络出版日期:  2021-07-22
  • 刊出日期:  2021-09-06

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