卫星
环境科学
遥感
卫星图像
地表水
水资源
云计算
计算机科学
地球观测
时间序列
气象学
地质学
地理
机器学习
操作系统
航空航天工程
工程类
环境工程
生物
生态学
作者
Omid Elmi,Amirhossein Ahrari
标识
DOI:10.1109/lgrs.2025.3528323
摘要
Lakes and reservoirs are essential freshwater sources, yet global monitoring of surface water storage faces challenges due to sparse gauge observations and limitations in remote sensing techniques. The lack of detailed knowledge about spatiotemporal dynamics of inland surface water storage impairs accurate global weather forecasting, Earth system modeling, and water management planning. To address this, we present Water area Tracking from Satellite imagery (WaTSat), a Google Earth Engine-based tool that generates long-term water area time series for global lakes and reservoirs from satellite imagery. The tool requires minimal user input, low storage demand, and is computationally efficient. WaTSat automates multiple tasks to modify the lake shoreline search area, identify and remove cloud-contaminated images, delineate the lake extent, detect and remove the outliers, and generate the water area time series. The initial version of the WaTSat tool utilizes the MODIS MOD09Q1 product to generate water area time series from February 2000 to date. Validation against altimetric water level time series for 40 global lakes of varying sizes and regions demonstrates an average correlation of 0.89, highlighting WaTSat's capability to accurately estimate surface water area and capture long-term trends and annual fluctuations. The tool's outputs can make a significant contribution to both scientific studies and operational applications in water resource management, hydrological modeling, and climate studies.
科研通智能强力驱动
Strongly Powered by AbleSci AI