سابِق|تال

Benefits of Google Earth Engine in remote sensing

  • role： First author第一作者
  • Affiliation：

    Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China

    College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China

  • Email：2190902117@cnu.edu.cn
  • Introduction：王小娜，1997年生，女，硕士研究生，研究方向为湿地遥感。E-mail： 2190902117@cnu.edu.cn
WANG Xiaona13,  
  • role： Corresponding author通信作者
  • Affiliation：

    Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China

    State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China

  • Email：tjyremote@126.com
  • Introduction：田金炎，1988年生，男，讲师，研究方向为湿地遥感、时空大数据融合。E-mail： tjyremote@126.com
TIAN Jinyan12*,  
  • Affiliation：

    Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China

    College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China

LI Xiaojuan13,  
  • Affiliation：

    Department of Geography, The State University of New York at Buffalo, Buffalo, NY, 14261, USA

WANG Le4,  
  • Affiliation：

    State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China

GONG Huili2,  
  • Affiliation：

    Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China

CHEN Beibei1,  
  • Affiliation：

    Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China

LI Xiangcai1,  
  • Affiliation：

    College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China

GUO Jinghan3

ملخص

The rapid development of remote sensing technology has enabled accumulation of massive earth observation data in recent years. Traditional desktop-based remote sensing platforms (e.g., ERDAS and ENVI) cannot satisfy the current application requirements of remote sensing big data. Google Earth Engine (GEE), as a leading cloud-based remote sensing platform, has not only changed the traditional processing and analysis means of data but also brought new opportunities for the rapid processing and information mining of massive data. To date, many researchers have successfully conducted a large number of works and published many academic papers and reviews based on GEE. However, no one has systematically analyzed how GEE promotes the development of remote sensing science. Therefore, this study aims to explore the innovative changes caused by GEE in the aspect of resources, methods, and applications, compared with the traditional desktop-based platform. (1) In terms of resources, GEE breaks the separation of traditional data, model algorithms, and computing power, realizes cloud deployment of the three, and shows great potential in the rapid processing and analysis of massive data through combining big data and cloud computing together. (2) With regard of methods, innovative remote sensing analysis methods provided by GEE break through the bottleneck of traditional remote sensing technology and promote the technological innovation of data processing and analysis. Thus, the efficiency of massive data processing and information mining greatly improves. (3) In the perspective of applications, GEE not only brings development opportunities for the rapid global-scale long-time analysis but also promotes the rapid sharing of data, algorithms, and products, which further ushers in the era of opening and sharing remote sensing. Systematically summarizing the advantages of GEE will not only help the potential new users understand GEE as well as deepen the existing users’ understanding but also encourage Google developers to perfect and improve GEE while catalyzing new discoveries in the scientific research of the earth system.

مفهوم

Google Earth Engine;cloud-based remote sensing platform;desktop-based remote sensing platform;remote sensing big data;pixel-based analysis method

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