| Summary: | As an approach with great potential, the interpretation of space‐borne synthetic aperture radar (SAR) images has been applied for monitoring the dynamic evolution of the glacial lakes in recent years. Considering unfavorable factors, such as inherent topography‐induced effects and speckle noise in SAR images, it is challenging to accurately map and track the dynamic evolution of the glacial lakes by using multi‐temporal SAR images. This paper presents an improved neighborhood‐based ratio method utilizing a time series of SAR images to identify the boundaries of the glacial lakes and detect their spatiotemporal changes. The proposed method was applied to monitor the dynamic evolution of the two glacial lakes with periodic water discharge at the terminus of the Gongba Glacier in the southeastern Tibetan Plateau by utilizing 144 Sentinel‐1A<br>SAR images collected between October of 2014 and November of 2020. We first generated the reference intensity image (RII) by averaging all the SAR images collected when the water in the glacial lakes was wholly discharged, then calculated the neighborhood‐based ratio between RII and each SAR intensity image, and finally identified the boundaries of the glacial lakes by a ratio<br>threshold determined statistically. The time series of areas of the glacial lakes were estimated in this way, and the dates for water recharging and discharging were accordingly determined. The testing results showed that the water of the two glacial lakes began to be recharged in April and reached their peak in August and then remained stable dynamically until they began to shrink in October and were discharged entirely in February of the following year. We observed the expansion process with annual growth rates of 3.19% and 12.63% for these two glacial lakes, respectively, and monitored a glacial lake outburst flood event in July 2018. The validation by comparing with the results derived from Sentinel‐2A/B optical images indicates that the accuracy for identifying the<br>boundaries of the glacial lakes with Sentinel‐1A SAR images can reach up to 96.49%. Generally, this contribution demonstrates the reliability and precision of SAR images to provide regular updates for the dynamic monitoring of glacial lakes.
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