DOI: 10.1029/2018JC014246
Scopus记录号: 2-s2.0-85052403369
论文题名: Retrieving Ocean Subsurface Temperature Using a Satellite-Based Geographically Weighted Regression Model
作者: Su H. ; Huang L. ; Li W. ; Yang X. ; Yan X.-H.
刊名: Journal of Geophysical Research: Oceans
ISSN: 21699275
出版年: 2018
卷: 123, 期: 8 起始页码: 5180
结束页码: 5193
语种: 英语
英文关键词: geographically weighted regression
; ocean subsurface temperature
; satellite altimetry
; sea surface observations
; the Indian Ocean
英文摘要: Accurately retrieving and describing subsurface temperature on a large scale can provide valuable information that can be used for subsurface dynamic and variability studies. This study develops a new satellite-based geographically weighted regression (GWR) model to estimate a subsurface temperature anomaly (STA) in the upper 2,000 m of the Indian Ocean by combining satellite observations (sea surface height, sea surface temperature, sea surface salinity, and sea surface wind) and Argo in situ data (STA). This model improves the estimation accuracy by considering the significant spatial nonstationarity feature between the surface and subsurface parameters in the ocean. The performance of the GWR model is measured by using Akaike Information Criterion combined with root-mean-square error and R2. The results showed that the proposed GWR model can easily retrieve the STA and outperform the ordinary least squares model. The GWR model can also explain the contribution from each variable via a local regression coefficient distribution. The sea surface height from altimetry is the most significant variable for GWR estimation. This study demonstrates the great potential and advantage of the GWR model for large-scale subsurface modeling and information retrieving. Thus, we have developed a novel approach for investigating subsurface thermal anomaly and variability from satellite observations. ©2018. American Geophysical Union. All Rights Reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/113445
Appears in Collections: 气候减缓与适应
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作者单位: Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National and Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou, China; Joint Center for Remote Sensing, University of Delaware and Xiamen University, Newark, DE, United States
Recommended Citation:
Su H.,Huang L.,Li W.,et al. Retrieving Ocean Subsurface Temperature Using a Satellite-Based Geographically Weighted Regression Model[J]. Journal of Geophysical Research: Oceans,2018-01-01,123(8)