Scholarlink

<p>Land and water are vital natural resources that underpin ecosystems, sustain life, and are essential for achieving sustainable development. However, these resources are increasingly threatened by climate change, population growth, urbanization, and pollution, necessitating advanced technological solutions for effective management. Google Earth Engine (GEE), a cloud computing-based geospatial platform, leverages Google’s vast computational infrastructure to provide open access to petabytes of data, enabling efficient and comprehensive analysis of land and water resources. While GEE has been widely applied in land and water resources (LWR) management, its potential remains underexplored, highlighting the need for further investigation. This study addresses this research gap by offering readers ‘one-stop overview’ of recent trends, key contributions, and future directions through a global scientometric analysis of GEE-enabled LWR research. A total of 507 articles published between 2010 and 2023 in high-impact journals, sourced from the Web of Science and Scopus databases, were analyzed across four key dimensions: descriptive, performance, keyword, and methodological analyses. The findings reveal a notable increase in GEE-enabled LWR research post-2019, with significant contributions originating from China, the USA, and India. Despite this growth, regions such as Africa, Central Asia, and Eastern Europe remain underrepresented. The co-authorship network also indicates limited collaboration, characterized by numerous isolated clusters. Prominent research themes identified include integrated water resource management, land use and land cover (LULC) change detection, agricultural monitoring, and sustainable development. Landsat data emerges as the most frequently utilized, appearing in 235 publications, while the Random Forest algorithm is the predominant method, employed in 27% of studies. Furthermore, the study underscores existing challenges and outlines future directions for advancing sustainable land and water resource management.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

State-of-the-Art Status of Google Earth Engine (GEE) Application in Land and Water Resource Management: A Scientometric Analysis

  • 期刊论文

  • 发表日期：

    2025年2月26日

查看全文
  • Nishtha Sharnagat,
  • Anupam Kumar Nema,
  • Prabhash Kumar Mishra,
  • Nitesh Patidar,
  • Rahul Kumar,
  • Ashwini Suryawanshi,
  • Lakey Radha

摘要

Land and water are vital natural resources that underpin ecosystems, sustain life, and are essential for achieving sustainable development. However, these resources are increasingly threatened by climate change, population growth, urbanization, and pollution, necessitating advanced technological solutions for effective management. Google Earth Engine (GEE), a cloud computing-based geospatial platform, leverages Google’s vast computational infrastructure to provide open access to petabytes of data, enabling efficient and comprehensive analysis of land and water resources. While GEE has been widely applied in land and water resources (LWR) management, its potential remains underexplored, highlighting the need for further investigation. This study addresses this research gap by offering readers ‘one-stop overview’ of recent trends, key contributions, and future directions through a global scientometric analysis of GEE-enabled LWR research. A total of 507 articles published between 2010 and 2023 in high-impact journals, sourced from the Web of Science and Scopus databases, were analyzed across four key dimensions: descriptive, performance, keyword, and methodological analyses. The findings reveal a notable increase in GEE-enabled LWR research post-2019, with significant contributions originating from China, the USA, and India. Despite this growth, regions such as Africa, Central Asia, and Eastern Europe remain underrepresented. The co-authorship network also indicates limited collaboration, characterized by numerous isolated clusters. Prominent research themes identified include integrated water resource management, land use and land cover (LULC) change detection, agricultural monitoring, and sustainable development. Landsat data emerges as the most frequently utilized, appearing in 235 publications, while the Random Forest algorithm is the predominant method, employed in 27% of studies. Furthermore, the study underscores existing challenges and outlines future directions for advancing sustainable land and water resource management.