基于逐步订正的国产卫星遥感海表温度数据融合*

doi:10.11978/2025015

热带海洋学报 ›› 2025, Vol. 44 ›› Issue (6): 1-11.doi: 10.11978/2025015CSTR: 32234.14.2025015

所属专题：海洋大数据及应用

• 海洋水文学 • 下一篇

基于逐步订正的国产卫星遥感海表温度数据融合^*

牛浩然¹^,²(), 王喜冬¹^,²(), 高振博¹^,², 陈志强³

¹ 河海大学, 自然资源部海洋灾害预报技术重点实验室, 江苏南京 210024
² 河海大学海洋学院, 江苏南京 210024
³ 中国科学院南海海洋研究所, 广东广州 510301

收稿日期:2025-01-25 修回日期:2025-03-18 出版日期:2025-11-10 发布日期:2025-12-03
通讯作者: 王喜冬。email: xidong_wang@hhu.edu.cn
作者简介:
牛浩然(2000—), 男, 安徽省阜阳市人, 硕士研究生, 从事海气相互作用研究。email: 221311040013@hhu.edu.cn
基金资助:
国家自然科学基金(41776004)

Domestic satellite sea surface temperature data fusion based on successive corrections^*

NIU Haoran¹^,²(), WANG Xidong¹^,²(), GAO Zhenbo¹^,², CHEN Zhiqiang³

¹ Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing 210024, China
² College of Oceanography, Hohai University, Nanjing 210024, China
³ South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China

Received:2025-01-25 Revised:2025-03-18 Online:2025-11-10 Published:2025-12-03
Contact: WANG Xidong. email: xidong_wang@hhu.edu.cn
Supported by:
National Natural Science Foundation of China(41776004)

摘要/Abstract

摘要：

海表温度(sea surface temperature, SST)作为大气海洋圈层的重要环境变量之一, 对于海洋学的研究具有重要意义。本文基于国产海洋(HaiYang, HY)和风云(FengYun, FY)卫星遥感SST数据, 利用逐步订正数据融合方法, 研制了“海上丝绸之路”沿线海域自主化、高时空分辨率卫星SST融合产品。首先利用iQuam实测数据开展了海洋卫星和风云卫星的SST观测数据精度评估, 同时对HY-1C和HY-1D卫星进行了偏差校正; 其次, 利用HY-1C、HY-1D、HY-2B、FY-3D、FY-3E和FY-4B的SST数据以及多尺度逐步订正融合算法, 制作了2024年4月至6月“海上丝绸之路”沿线海域逐日0.1°×0.1°分辨率SST融合产品; 最后, 利用iQuam实测数据评估了融合产品精度。结果发现, 融合产品平均偏差为0.08℃, 均方根误差为0.67℃, 产品精度较好。不同SST融合产品的功率谱分析结果表明, 本文生成的SST融合产品较OISST (optimum interpolation sea surface temperature)产品在刻画100km以小尺度特征上更有优势, 与OSTIA产品在50~100km尺度上性能相当, 体现了融合产品良好的应用潜力。

关键词: 海表温度, 国产卫星, 逐步订正, 数据融合

Abstract:

As one of the important environmental variables in the atmosphere-ocean system, sea surface temperature (SST) is of significant importance for oceanographic research. This paper utilizes domestic HaiYang (HY) and FengYun (FY) satellites remote sensing SST data to develop an autonomous, high spatiotemporal resolution satellite SST fusion product for the “Maritime Silk Road” region using the successive corrections method. First, the accuracy of SST observation data from HY and FY satellites was evaluated using iQuam in-situ data, with bias correction performed on HY-1C and HY-1D. Next, SST data from HY-1C, HY-1D, HY-2B, FY-3D, FY-3E, and FY-4B, along with a multi-scale successive corrections fusion algorithm, were used to create a daily 0.1°×0.1° resolution SST fusion product for the “Maritime Silk Road” region from April to June 2024. Finally, the accuracy of the fusion product was assessed using iQuam in-situ data. The results showed that the fusion product had an average bias of 0.08°C and a root mean square error (RMSE) of 0.67°C, indicating good accuracy. Power spectral analysis of different SST fusion products demonstrated that the SST fusion product generated in this study outperforms the Optimum Interpolation Sea Surface Temperature (OISST) in capturing small-scale features at the 100 km scale and shows comparable performance to the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) at the 50-100 km scale, reflecting its good application potential.

Key words: sea surface temperature, domestic satellite, successive corrections, data fusion

中图分类号:

P714.1

牛浩然, 王喜冬, 高振博, 陈志强. 基于逐步订正的国产卫星遥感海表温度数据融合^*[J]. 热带海洋学报, 2025, 44(6): 1-11.

NIU Haoran, WANG Xidong, GAO Zhenbo, CHEN Zhiqiang. Domestic satellite sea surface temperature data fusion based on successive corrections^*[J]. Journal of Tropical Oceanography, 2025, 44(6): 1-11.

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卫星	匹配数	平均偏差/℃	绝对偏差/℃	均方根误差/℃	相关系数
HY-1C	9725	-1.45	1.65	2.08	0.75
HY-1D	9572	-1.23	1.52	1.98	0.70
HY-2B	2328	-1.25	1.38	1.73	0.81
FY-3D	3877	-0.36	0.72	0.94	0.88
FY-3E	7935	0.04	0.47	0.63	0.94
FY-4B	24712	-0.39	0.59	0.77	0.94

卫星	平均偏差/℃	均方根误差/℃	绝对偏差/℃
HY-1C	0.13	1.11	0.83
HY-1D	0.09	1.08	0.82

	融合产品	OISST
平均偏差/℃	0.08	-0.14
绝对偏差/℃	0.47	0.37
均方根误差/℃	0.67	0.52
相关系数	0.96	0.97

基于逐步订正的国产卫星遥感海表温度数据融合^*

Domestic satellite sea surface temperature data fusion based on successive corrections^*

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基于逐步订正的国产卫星遥感海表温度数据融合*

Domestic satellite sea surface temperature data fusion based on successive corrections*

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基于逐步订正的国产卫星遥感海表温度数据融合^*

Domestic satellite sea surface temperature data fusion based on successive corrections^*