渔业信息与战略2025，Vol.40Issue(4)：292-303,12.DOI:10.13233/j.cnki.fishis.2025.04.007
黑潮-亲潮海表温度反演数据缺失及填补方法浅析
Analysis of sea surface temperature data gaps and filling methods in the Kuroshio-Oyashio region
摘要
Abstract
Sea surface temperature(SST)is a core parameter for studying the global climate system and marine ecological processes.The integrity of its data is crucial for the analysis of the dynamics and ecological environment in the Kuroshio-Oyashio transition zone.Currently,SST observations mainly rely on satellite infrared radiometers and microwave radiometers.Among them,infrared data is preferred for research due to its high spatiotemporal resolution,but it is prone to large-scale data loss due to cloud cover.In contrast,microwave data can penetrate clouds but has a lower resolution,making it difficult to accurately depict the fine temperature gradients in this region.To address this contradiction,this paper systematically analyzes the causes of infrared SST data loss in the Kuroshio-Oyashio transition zone(cloud interference,equipment failure,and complex marine environment),and reviews the application potential of various data filling methods such as interpolation(optimal interpolation,kriging),extrapolation(spatiotemporal sequence),and machine learning(decision tree,support vector machine).The research indicates that the integration of spatial interpolation and machine learning techniques(such as the Kriging-random forest combined model)can significantly improve data accuracy and coverage,and effectively capture temperature fronts and vortex structures.This study provides methodological support for SST data reconstruction in complex marine environments and points out that in the future,it is necessary to combine multi-source data and real-time processing technologies to meet the high-precision monitoring requirements under the background of climate change.关键词
黑潮/亲潮/海表温度/机器学习/插值方法Key words
Kuroshio/Oyashio/sea surface temperature/machine learning/interpolation methods分类
农业科技引用本文复制引用
谢雨家,王斐,伍玉梅,吴祖立,戴阳,张胜茂..黑潮-亲潮海表温度反演数据缺失及填补方法浅析[J].渔业信息与战略,2025,40(4):292-303,12.基金项目
崂山实验室科技创新项目(LSKJ202201804) （LSKJ202201804）
