遥感
环境科学
地形
仰角(弹道)
云计算
地球观测
地表水
卫星
合成孔径雷达
比例(比率)
领域(数学)
数字高程模型
计算机科学
地质学
地理
地图学
数学
环境工程
操作系统
工程类
航空航天工程
几何学
纯数学
作者
Chang Huang,Yun Chen,Shiqiang Zhang,Jianping Wu
摘要
Abstract Observation of surface water is a functional requirement for studying ecological and hydrological processes. Recent advances in satellite‐based optical remote sensors have promoted the field of sensing surface water to a new era. This paper reviews the current status of detecting, extracting, and monitoring surface water using optical remote sensing, especially progress in the last decade. It also discusses the current status and challenges in this field, including spatio‐temporal scale issues, integration with in situ hydrological data and elevation data, obscuration caused by clouds and vegetation, and the growing need to map surface water at a global scale. Historically, sensors have exhibited a contradiction in resolutions. Techniques including pixel unmixing and reconstruction, and spatio‐temporal fusion have been developed to alleviate this contradiction. Spatio‐temporal dynamics of surface water have been modeled by combining remote sensing data with in situ river flow. Recent studies have also demonstrated that the river discharge can be estimated using only optical remote sensing imagery, providing valuable information for hydrological studies in ungauged areas. Another historical issue for optical sensors has been obscuration by clouds and vegetation. An effective approach of reducing this limitation is to combine with synthetic aperture radar data. Digital elevation model data have also been employed to eliminate cloud/terrain shadows. The development of big data and cloud computation techniques makes the increasing demand of monitoring global water dynamics at high resolutions easier to achieve. An integrated use of multisource data is the future direction for improved global and regional water monitoring.
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