使用卫星红外数据识别火山热点可以实现安全且低成本的监测全球火山活动。本文综述了卫星红外数据在火山热点识别中的算法研究进展，特别强调了算法的分类和发展历史。这些算法主要基于火山活动时热点所在像元中红外通道亮温升高的原理，根据考虑火山及其周围地物的空间和时间特性来识别火山热异常，算法大致分为四种主要类型：空间特征算法、时间特征算法、综合特征算法和人工智能算法。从算法分类、特性、适用范围、局限性方面，厘清了当前国内外利用遥感的方式进行火山热点识别的现状，为理解和改进火山热点检测技术提供了全面的分类和评估，对火山热遥感前沿理论和技术发展具有重要意义。

Volcano monitoring is essential for predicting volcanic eruptions and taking early warning measures. Traditional ground-based monitoring methods cannot fully cover all volcanoes. Satellite remote sensing technology, with its advantages of global coverage and high temporal and spatial resolution, is an important complement for near-real-time monitoring of volcanic activities, especially the detection of lava flows and volcanic thermal anomalies. This study introduces the current status of typical sensors for infrared remote sensing of volcanic hotspots and summarizes the methodology for detecting volcanic hotspots using satellite infrared data. Firstly, the history of thermal infrared satellite data monitoring and satellite system development is summarized, and various types of algorithms and satellite systems have been applied to make the monitoring of volcanic activities on a global scale more efficient and accurate. Secondly, the development of volcanic hotspot identification algorithms is analyzed, and the existing volcanic hotspot identification algorithms are classified into four categories according to the different characteristics of the volcano used and its surrounding features (spatial/temporal): spatial feature algorithms, temporal feature algorithms, comprehensive feature algorithms and artificial intelligence algorithms. The spatial feature algorithms are categorized into fixed threshold method and dynamic threshold method based on different methods of threshold selection (fixed threshold/dynamic threshold). Based on the above classification, we describe the current status of each type of volcanic hotspot identification algorithms and summarize their data, scope of application, and application limitations, which provides a comprehensive classification and assessment for understanding and improving volcano hotspot detection technology, and is of great significance for the development of future volcano thermal remote sensing theories and technologies. Subsequent research should improve the adaptability of the algorithms in different volcanic environments, combine the advantages of traditional algorithms and artificial intelligence, and utilize historical data and time-series analyses to identify volcanic hotspots more accurately. In addition, fusion of high-resolution and multispectral satellite data will improve the spatial and spectral resolution of volcanic activity monitoring, thus capturing the micro features of volcanoes more accurately. These improvements will enhance the comprehensiveness and accuracy of volcanic hotspot monitoring and provide more reliable support for monitoring, early warning and prevention of geologic hazards.