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Review of global studies on the remote sensing of wetlands from 1975 to 2020

  • role： First author第一作者
  • Affiliation：

    Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

  • Email：maodehua@iga.ac.cn
  • Introduction：毛德华，研究方向为湿地遥感。E-mail：maodehua@iga.ac.cn
MAO Dehua1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

  • Email：zongmingwang@iga.ac.cn
  • Introduction：王宗明，研究方向为湿地遥感。E-mail：zongmingwang@iga.ac.cn
WANG Zongming1*,  
  • Affiliation：

    Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

JIA Mingming1,  
  • Affiliation：

    Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

LUO Ling1,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

NIU Zhenguo2,  
  • Affiliation：

    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

JIANG Weiguo3,  
  • Affiliation：

    Department of Geography and Spatial Information Technology, Ningbo University, Ningbo 315201, China

SUN Weiwei4

Resümee

Sustainable ecosystem management requires considerable wetland spatial information given the evident climate change impacts and human disturbances on wetlands. Remote sensing of wetlands, as an important interdiscipline, has increasing publications. Here, we searched for published papers in the past 50 years from the “Web of Science Core Collection” database. We summarized the changes in the number of publications and citations and the development process and trend in remote sensing of wetlands. We divided the development history into three research periods including potential exploration phase, framework emerging phase, and rapid growth phase. Based on the development history over the past 50 years and facing the background of wetland ecosystem protection demand in the era of big data, studies on remote sensing of wetlands have developed in the direction of fast, multisource, and fine, such as wetland intelligent classification, remote sensing inversion of large-scale wetland vegetation ecological parameters, and wetland ecosystem health assessment. However, the spectral and backscattering characteristics of wetlands are complex due to the interaction of water, vegetation, and soil, and their annual/inter-annual variation characteristics are notable, aggravating the difficulty of remote sensing detection of wetlands. This condition is a key issue that requires resolution at present. Thus, the multimodal remote sensing experiments of wetlands should be strengthened. We also concluded the main research topics and data sources in different phases and analyzed the hotspots in remote sensing of wetlands by the extracted keywords from 500 latest and top-cited papers. Three outlook bullets were presented from the wetland classification and landscape dynamics in the era of big data, the fine remote sensing observations in wetland ecological variables, and the spatial decision support for sustainable wetland management. Based on cloud platforms (such as GEE), carrying out large-scale and long-term wetland mapping and landscape dynamic analysis by means of time series remotely sensed data (i.e., Landsat and Sentinel), investigating the application potential of diverse machine learning algorithms (i.e., random forest and deep learning) for wetland ecological parameter inversion at different geographic scales, establishing a scientific indicator system, and fully applying the multisource and multiplatform remote sensing observation technology to solve the actual ecological environment problems are important development trends and research hotspots of future wetland remote sensing studies. We hope that this review not only provides a glimpse, but also a framework understanding of wetland remote sensing research. With the improvement on the awareness of the importance of wetland ecosystem, the number of scholars engaged in research of remote sensing of wetlands increases. The review is expected to be beneficial for understanding the development history and international frontiers for studies in remote sensing of wetlands and to support their layout domestically and abroad.

Schlüsselwort

global scale;remote sensing of wetlands;review;long time series;big data;artificial intelligence;cloud platform;sustainable development

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