Jahangeer Jahangeer, Joshi Pranjay, Kapoor Aditya, Tang Zhenghong
Community and Regional Planning Program, University of Nebraska-Lincoln, Lincoln, NE 68588 USA.
Civil Engineering for Mitigation of Risk from Natural Hazards, University of Pavia, Corso Strada Nuova, Pavia, 27100 Italy.
Discov Geosci. 2025;3(1):140. doi: 10.1007/s44288-025-00255-x. Epub 2025 Sep 25.
Geospatial technologies now allow routine observation of lakes and wetlands across large areas, but turning those observations into timely, actionable insight still requires scalable computing. Google Earth Engine (GEE) provides a web-based platform that brings multi-sensor Remote Sensing (RS) archives and parallel processing together in one environment. This review synthesizes how artificial intelligence (AI), machine learning (ML) and deep learning (DL) have been paired with GEE to map and monitor surface water quantity and quality. We summarize recent methods, compare model families commonly used on GEE, and discuss frequent processing pitfalls. To ground the review, we include a case study of three Nebraska lakes (2022-2023) that demonstrates month-to-month tracking of water extent and indicators of water quality. The results demonstrated the effectiveness of GEE in providing timely and accurate insights for surface water monitoring and assessment while also revealing current limitations and opportunities for improvement. Overall, we find that coupling AI methods with GEE can strengthen operational surface water assessment and inform decision-making under increasing environmental pressures.
The online version contains supplementary material available at 10.1007/s44288-025-00255-x.
地理空间技术现在允许对大面积的湖泊和湿地进行常规观测，但将这些观测结果转化为及时、可操作的见解仍然需要可扩展的计算。谷歌地球引擎（GEE）提供了一个基于网络的平台，该平台将多传感器遥感（RS）档案和并行处理整合在一个环境中。本综述综合了人工智能（AI）、机器学习（ML）和深度学习（DL）如何与GEE相结合，以绘制和监测地表水的数量和质量。我们总结了最近的方法，比较了GEE上常用的模型家族，并讨论了常见的处理陷阱。为了使综述更具说服力，我们纳入了内布拉斯加州三个湖泊（2022 - 2023年）的案例研究，该研究展示了逐月对水域范围和水质指标的跟踪。结果证明了GEE在为地表水监测和评估提供及时准确见解方面的有效性，同时也揭示了当前的局限性和改进机会。总体而言，我们发现将人工智能方法与GEE相结合可以加强地表水的业务评估，并在环境压力不断增加的情况下为决策提供信息。
在线版本包含可在10.1007/s44288-025-00255-x获取的补充材料。
