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Increasing atmospheric warming is enhancing surface melt across Antarctica, leading to the widespread formation of supraglacial lakes (SGLs) on ice shelves. These lakes play a critical role in ice-shelf stability by promoting hydrofracturing, increasing flexural stresses, and potentially triggering ice-shelf collapse, thereby accelerating grounded ice discharge and sea-level rise. Accurate estimation of SGL depth and volume is therefore essential for understanding Antarctic ice dynamics.To date, most large-scale SGL depth estimates have relied on optical remote sensing and radiative transfer methods (RTMs), which infer lake depth from spectral attenuation in satellite imagery. While effective, RTMs are sensitive to surface conditions, cloud cover, and water optical properties. The launch of ICESat-2 in 2018 provides a complementary approach, as its photon-counting lidar can detect returns from both lake surfaces and beds, enabling direct depth estimation. Several algorithms have been developed to extract SGL depths from ICESat-2 data, but applications remain spatially limited and none have yet focused on the George VI Ice Shelf (GVIIS).Here, we present the first retrieval of supraglacial lake depths on the northern GVIIS using ICESat-2 ATL03 and ATL06 data. We apply the Watta algorithm (Datta and Wouters, 2021) to derive along-track SGL depth profiles and compare these results with independent depth estimates obtained using an RTM applied to Sentinel-2 and Landsat 8 imagery. This comparison is used to evaluate the strengths and limitations of ICESat-2–based depth retrievals relative to established optical methods.Our study provides new constraints on supraglacial lake characteristics on the northern GVIIS and demonstrates the value of integrating active and passive remote sensing approaches to improve assessments of meltwater processes that influence Antarctic ice-shelf stability.