Reconstruction of Subsurface Salinity Structure in the South China Sea Using Satellite Observations: A LightGBM-Based Deep Forest Method

doi:10.3390/rs14143494

IOCAS-IR > 海洋环流与波动重点实验室

	Reconstruction of Subsurface Salinity Structure in the South China Sea Using Satellite Observations: A LightGBM-Based Deep Forest Method
	Dong, Lin 1; Qi, Jifeng 2,3,4; Yin, Baoshu 2,3,4; Zhi, Hai 5; Li, Delei 2,3,4; Yang, Shuguo 1; Wang, Wenwu 6; Cai, Hong 1; Xie, Bowen 1
	2022-07-01
发表期刊	REMOTE SENSING
卷号	14 期号:14 页码:19
通讯作者	Qi, Jifeng([email protected])
摘要	Accurately estimating the ocean's interior structures using sea surface data is of vital importance for understanding the complexities of dynamic ocean processes. In this study, we proposed an advanced machine-learning method, the Light Gradient Boosting Machine (LightGBM)-based Deep Forest (LGB-DF) method, to estimate the ocean subsurface salinity structure (OSSS) in the South China Sea (SCS) by using sea surface data from multiple satellite observations. We selected sea surface salinity (SSS), sea surface temperature (SST), sea surface height (SSH), sea surface wind (SSW, decomposed into eastward wind speed (USSW) and northward wind speed (VSSW) components), and the geographical information (including longitude and latitude) as input data to estimate OSSS in the SCS. Argo data were used to train and validate the LGB-DF model. The model performance was evaluated using root mean square error (RMSE), normalized root mean square error (NRMSE), and determination coefficient (R-2). The results showed that the LGB-DF model had a good performance and outperformed the traditional LightGBM model in the estimation of OSSS. The proposed LGB-DF model using sea surface data by SSS/SST/SSH and SSS/SST/SSH/SSW performed less satisfactorily than when considering the contribution of the wind speed and geographical information, indicating that these are important parameters for accurately estimating OSSS. The performance of the LGB-DF model was found to vary with season and water depth. Better estimation accuracy was obtained in winter and autumn, which was due to weaker stratification. This method provided important technical support for estimating the OSSS from satellite-derived sea surface data, which offers a novel insight into oceanic observations.
关键词	machine learning ocean subsurface salinity structure South China Sea satellite remote sensing data
DOI	10.3390/rs14143494
收录类别	SCI
语种	英语
资助项目	Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao)[2022QNLM010301-3]; National Natural Science Foundation of China[42176010]; National Natural Science Foundation of China[42076022]; Natural Science Foundation of Shandong Province, China[ZR2021MD022]; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB42000000]
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000833240900001
出版者	MDPI
引用统计	被引频次：12[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/179818
专题	海洋环流与波动重点实验室
通讯作者	Qi, Jifeng
作者单位	1.Qingdao Univ Sci & Technol, Sch Math & Phys, Qingdao 266061, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 3.Pilot Natl Lab Marine Sci & Technol Qingdao, Qingdao 266237, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Nanjing Univ Informat Sci & Technol, Coll Atmospher Sci, Nanjing 210044, Peoples R China 6.Univ Surrey, Dept Elect & Elect Engn, Guildford GU2 7XH, Surrey, England
通讯作者单位	中国科学院海洋研究所
推荐引用方式 GB/T 7714	Dong, Lin,Qi, Jifeng,Yin, Baoshu,et al. Reconstruction of Subsurface Salinity Structure in the South China Sea Using Satellite Observations: A LightGBM-Based Deep Forest Method[J]. REMOTE SENSING,2022,14(14):19.
APA	Dong, Lin.,Qi, Jifeng.,Yin, Baoshu.,Zhi, Hai.,Li, Delei.,...&Xie, Bowen.(2022).Reconstruction of Subsurface Salinity Structure in the South China Sea Using Satellite Observations: A LightGBM-Based Deep Forest Method.REMOTE SENSING,14(14),19.
MLA	Dong, Lin,et al."Reconstruction of Subsurface Salinity Structure in the South China Sea Using Satellite Observations: A LightGBM-Based Deep Forest Method".REMOTE SENSING 14.14(2022):19.