Application of Google Earth Engine to Monitor Greenhouse Gases: A Review

Damar David Wilson, Gebrekidan Worku Tefera and Ram L. Ray ()
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Damar David Wilson: College of Agriculture Food and Natural Resources, Prairie View A&M University, Prairie View, TX 77446, USA
Gebrekidan Worku Tefera: College of Agriculture Food and Natural Resources, Prairie View A&M University, Prairie View, TX 77446, USA
Ram L. Ray: College of Agriculture Food and Natural Resources, Prairie View A&M University, Prairie View, TX 77446, USA

Data, 2025, vol. 10, issue 1, 1-17

Abstract: Google Earth Engine (GEE) is a cloud-based platform revolutionizing geospatial analysis by providing access to vast satellite datasets and computational capabilities for monitoring environmental and societal issues. It incorporates machine learning (ML) techniques and algorithms as part of its tools for analyzing and processing large geospatial data. This review explores the diverse applications of GEE in monitoring and mitigating greenhouse gas emissions and uptakes. GEE is a cloud-based platform built on Google’s infrastructure for analyzing and visualizing large-scale geospatial datasets. It offers large datasets for monitoring greenhouse gas (GHG) emissions and understanding their environmental impact. By leveraging GEE’s capabilities, researchers have developed tools and algorithms to analyze remotely sensed data and accurately quantify GHG emissions and uptakes. This review examines progress and trends in GEE applications, focusing on monitoring carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide/nitrogen dioxide (N 2 O/NO 2 ) emissions. It discusses the integration of GEE with different machine learning methods and the challenges and opportunities in optimizing algorithms and ensuring data interoperability. Furthermore, it highlights GEE’s role in pinpointing emission hotspots, as demonstrated in studies monitoring uptakes. By providing insights into GEE’s capabilities for precise monitoring and mapping of GHGs, this review aims to advance environmental research and decision-making processes in mitigating climate change.

Keywords: greenhouse gas; Google Earth Engine; machine learning; artificial intelligence; geographic information system; CDA; GEC (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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