Detecting, Extracting, and Monitoring Surface Water From Space Using Optical Sensors: A Review

  • Chang Huang
    Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity Northwest University Xi'an China
  • Yun Chen
    Commonwealth Scientific and Industrial Research Organisation Land and Water Canberra ACT Australia
  • Shiqiang Zhang
    Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity Northwest University Xi'an China
  • Jianping Wu
    Key Laboratory of Geographic Information Science, Ministry of Education East China Normal University Shanghai China

書誌事項

公開日
2018-06
権利情報
  • http://creativecommons.org/licenses/by-nc-nd/4.0/
DOI
  • 10.1029/2018rg000598
公開者
American Geophysical Union (AGU)

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説明

<jats:title>Abstract</jats:title><jats:p>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.</jats:p>

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