季节性水体是地表水的重要组成单元，在调蓄区域洪水、维持生物多样性等方面具有重要作用。如何获取高精度水深信息，是有效支撑季节性水体水储量和碳通量估算的关键，对于深入理解区域水文过程及物质能量平衡等问题具有重要意义。针对季节性水体下垫面条件的复杂性及传统测深技术的局限性，本文基于GEE云平台，联合激光雷达和光学传感器数据，提出了一种面向季节性水体的湖盆地形定量估算方法，并以鄱阳湖典型碟形子湖为研究对象，开展了该方法的精度评价及适用性分析。研究结果表明，以ICESat-2/ATLAS剖面光子高程为基础，协同Sentinel-2获取的淹没频率信息，实现季节性水体湖盆地形“由点及面”的定量估算方法切实可行，估算值和实测值相关性较好，决定系数（R2）优于0.7，均方根误差（RMSE）控制在1m内；随着湖泊面积的增大，地形估算的精度总体呈上升趋势，同时受淹没频率范围及光子轨迹分布的影响，不同区域及下垫面条件上的子湖，其估算精度也存在着差异性。本文提出的方法可实现面向季节性水体大范围、低成本、长时序的水下地形定量估算，有望为全球范围内季节性水体水深数据的获取提供新思路。

Seasonal water bodies are an important component of global surface water, and play an indispensable role in regional flood mitigation and biodiversity maintenance. Acquiring high-precision bathymetric information is the key to effectively support the estimation of water storage and carbon flux in seasonal water bodies, which is of great significance for comprehensive understanding of regional hydrological processes and material-energy balance. In view of the complexity of the subsurface conditions of seasonal water bodies and the limitations of traditional bathymetric techniques, this paper proposes a quantitative estimation method of underwater topography for seasonal water bodies based on GEE cloud platform, combined with active LiDAR and passive optical sensor data, and then systematically evaluates the estimation accuracy and applicability of the method with Poyang Lake as the research object, which is a typical seasonal lake composed of a number of dished lakes. The results show that the quantitative estimation method is feasible based on the photon elevation of ICESat-2/ATLAS profiles and the inundation frequency information obtained from Sentinel-2 to achieve the “point-to-surface” topography of seasonal water bodies. The R2 values between the predicted and measured yield are greater than 0.7 and the root mean square errors (RMSEs) are controlled within 1 meter. Additionally, the simulation accuracy of dished lakes in different areas and subsurface conditions is also different due to the combined effects of various factors such as lake area, inundation frequency range and photon track distribution. The method proposed in this paper can realize the quantitative estimation of underwater topography for seasonal water bodies in a large scale, low cost and long time series, which is expected to provide ideas and directions for the development of bathymetric retrieving models for seasonal water bodies at the global scale.