融合AHI/ATMS数据的全天候海表温度反演

胡文翰; 范冬林; 何宏昌; 付波霖

doi:10.11834/jrs.20264152

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专辑

融合AHI/ATMS数据的全天候海表温度反演
All-Weather sea surface temperature retrieval based on AHI/ATMS data fusion
2026年30卷第3期页码：635-646
收稿：2024-04-25，

纸质出版：2026-03-07
DOI： 10.11834/jrs.20264152
稿件说明：

移动端阅览

胡文翰，范冬林，何宏昌，付波霖.2026.融合AHI/ATMS数据的全天候海表温度反演.遥感学报，30（3）： 635-646 DOI： 10.11834/jrs.20264152.

Hu W H，Fan D L，He H C and Fu B L. 2026. All-Weather sea surface temperature retrieval based on AHI/ATMS data fusion. National Remote Sensing Bulletin， 30（3）：635-646 DOI： 10.11834/jrs.20264152.

摘要

随着遥感卫星的快速发展，具有多尺度和广覆盖特征的海表温度遥感产品数据正逐步取代传统的海表温度SST（Sea Surface Temperature）采集方法。热红外卫星传感器凭借高频率、广覆盖反演SST数据的能力，在科学应用中展现出巨大潜力。然而，云覆盖常常导致云下SST预估异常，部分云覆盖区间甚至出现SST数据缺失现象。为克服上述问题缺失，本文以原位海温数据为基准，采用时空匹配方法将AHI（Advanced Himawari Imager）近红外影像与ATMS（Advanced Technology Microwave Sounder）微波辐射计波段数据融合，建立匹配数据集，并通过3种机器学习方法评估融合数据的SST反演精度。同时，分别对昼夜条件下SST反演精度进行分析。研究结果表明，AHI/ATMS联合亮温数据进行SST反演显著提高全天候SST数据准确性，与仅利用AHI数据进行反演的结果相比，融合AHI/ATMS数据方法使得云下区域的

提高7.7%，RMSE降低0.896 ℃。此外，相比热红外反演方法，融合ATMS数据方法能够有效反演云层覆盖区域的SST，为全天候条件下SST反演提供重要的技术参考。

Abstract

Sea Surface Temperature (SST) is crucial for managing marine ecosystems and mitigating oceanic disasters. For more than three decades

the scientific community has been dedicated to improving the precision of SST products through satellite remote sensing techniques. Despite the potential of thermal infrared sensors to yield SST estimations with high spatial and temporal precision

cloud cover undermines the accuracy of SST acquisition.

This study presents a novel approach to improving SST retrieval accuracy by integrating multisource remote sensing data. The approach addresses the challenge of cloud cover by using the cloud-penetrating capabilities of microwave sensors in conjunction with infrared sensor data. This methodology involves generating level 1 sample datasets through spatiotemporal matching with in-situ SST data. After extensive pre-processing

the dataset is categorized into clear skies and cloud cover conditions. This paper employs three advanced machine learning algorithms—XGBoost

SVR

and RF—to conduct SST inversion with synergistic data from AHI/ATMS sensors. The performance of these algorithms is rigorously assessed through a comparative analysis of their inversion results and the Himawari-8 SST production. Moreover

the analysis meticulously examines SST inversion accuracy across diurnal and nocturnal conditions

effectively exploring between daytime and nighttime inversion accuracies.

These findings demonstrate that integrating ATMS microwave data markedly improves the accuracy of SST inversion

particularly in cloudy conditions. The XGBoost algorithm exhibits exceptional performance

with an RMSE of 1.707 ℃ and an

² of 0.935. AHI/ATMS data effectively address data inconsistencies and cloud cover issues and highlights the importance of multiple sources of data to obtain a comprehensive and accurate SST dataset.

This paper confirms the significant impact of multisource data on improving the accuracy and broadening the spatial coverage of SST inversions. The proposed approach effectively diminishes cloud interference

providing a compelling argument for the adoption of ATMS microwave sensing to overcome the challenges posed by cloud cover. Additionally

this study emphasizes the potential of machine learning algorithms to improve the resolution and accuracy of SST estimates

generating high-precision

wide-coverage SST distribution maps that provide important data for the effective management of marine ecosystems and proactive prevention of marine disasters.

关键词

Keywords

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