随着遥感卫星的快速发展，多尺度、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（Sea Surface Temperature, SST）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，云覆盖频繁导致云下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，并通过三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R2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。

Sea Surface Temperature (SST) is crucial for managing marine ecosystems and mitigating oceanic disasters. For over 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 (TIR) 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 multi-source 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 (L1) 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 the inversion results with each other and the Himawari-8 SST production. What’s more, 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°C and an R2 of 0.935. AHI/ATMS data effectively addresses 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 multi-source data on improving the accuracy and broadening the spatial coverage of SST inversions. The 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, 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.