Surface Water Mapping (SWM) is essential for studying hydrological and ecological phenomena. SWM holds significant importance in water resource management, environmental conservation, and disaster preparation. Recently, rapid urbanization, overutilization, and environmental degradation have seriously impacted surface water bodies. Rapid advancement in remote sensing data and technologies has promoted the SWM to a new era. Timely and precise SWM is crucial for water resource preservation and planning. This paper critically reviews the extraction of surface water bodies from optical sensors using Spectral Indices (SI), Machine Learning (ML), Deep Learning (DL), and Spectral unmixing with a comprehensive overview of satellite data, study areas, methodologies, results, advantages, and disadvantages, especially over the last decade. The extensive review of SWM reveals that DL outperforms ML and SI. DL outperforms other methods because it incorporates crucial elements in network design, like skip connections, dilation convolution, attention mechanisms, and residual blocks. The spectral unmixing addresses the mixed pixel misclassification problem. Some SI, ML, and DL methods are implemented, and the results are discussed. Integrating the DL technique with spectral unmixing, fusing multisource data (SAR and optical) and integrating it with ancillary data (DEM) is the future direction for improved SWM.

地表水测绘 (SWM) 对于研究水文和生态现象至关重要。 SWM 在水资源管理、环境保护和备灾方面具有重要意义。近年来，快速的城市化、过度利用和环境退化严重影响了地表水体。遥感数据和技术的快速进步推动SWM进入新时代。及时、准确的 SWM 对于水资源保护和规划至关重要。本文批判性地回顾了使用光谱指数 (SI)、机器学习 (ML)、深度学习 (DL) 和光谱分解从光学传感器中提取地表水体的方法，全面概述了卫星数据、研究领域、方法、结果、优点和缺点，特别是在过去的十年里。对 SWM 的广泛审查表明 DL 优于 ML 和 SI。深度学习优于其他方法，因为它结合了网络设计中的关键元素，例如跳跃连接、扩张卷积、注意力机制和残差块。光谱分解解决了混合像素错误分类问题。实现了一些 SI、ML 和 DL 方法，并讨论了结果。将深度学习技术与光谱分解相结合、融合多源数据（SAR 和光学）并将其与辅助数据 (DEM) 相集成是改进 SWM 的未来方向。