Global trends in vegetation carbon stock monitoring using Google Earth Engine and NDVI: A systematic review (2017–2024)

Accurate estimation of vegetation carbon stocks is essential for monitoring climate change impacts, assessing ecosystem services, and informing global mitigation strategies. In recent years, the integration of remote sensing techniques with cloud-based platforms—particularly Google Earth Engine (GEE)—has transformed how vegetation dynamics and carbon fluxes are analyzed, largely through the widespread use of the Normalized Difference Vegetation Index (NDVI). This study presents a comprehensive bibliometric and thematic review of global research trends in vegetation carbon stock monitoring using GEE and NDVI, covering 91 peer-reviewed articles published between 2017 and early 2024. Analyses were conducted using the Bibliometrix R package and included publication patterns, leading contributors, geographic distribution, keyword evolution, sensor usage, and collaborative networks. Results indicate a substantial increase in scientific output since 2017, with China, the United States, and Brazil emerging as leading contributors. Most studies relied on MODIS, Landsat, and Sentinel-2 imagery within GEE workflows, with a growing trend toward multi-sensor integration and machine learning applications. Despite technical advancements, the review identifies persistent gaps in policy integration, in-situ validation, and geographic representation—particularly in carbon-rich but underrepresented regions of the Global South. We conclude by recommending enhanced international collaboration, expanded ground-truth validation efforts, and stronger alignment with climate policy instruments such as REDD+ and the Sustainable Development Goals (SDGs). This review provides a structured synthesis of the current state of GEE-based carbon monitoring research and highlights key opportunities to increase its scientific impact and policy relevance. • Growing focuses on applying GEE, NDVI, and remote sensing to carbon and environmental monitoring research. • Google Earth Engine and vegetation indices like NDVI are revolutionizing carbon stock monitoring and management. • Improved tools for measuring carbon stocks with greater precision. • Contributions align with major frameworks, including the UN 2030 Agenda and REDD+. • Essential to embed these tools into public policy frameworks.

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Publication Details

Journal
Remote Sensing Applications Society and Environment
Published
2025-12-31
DOI
https://doi.org/10.1016/j.rsase.2025.101863
Primary Topic
Remote Sensing in Agriculture
Type
article
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article

Global trends in vegetation carbon stock monitoring using Google Earth Engine and NDVI: A systematic review (2017–2024)

Adriana Bilar Chaquime dos Santos, Micaella Lima Nogueira, Deimison Rodrigues Oliveira, Reginaldo Brito da Costa et al.
Remote Sensing Applications Society and Environment
Remote Sensing in Agriculture
article

Global trends in vegetation carbon stock monitoring using Google Earth Engine and NDVI: A systematic review (2017–2024)

Adriana Bilar Chaquime dos Santos, Micaella Lima Nogueira, Deimison Rodrigues Oliveira, Reginaldo Brito da Costa, César Iván Álvarez, Patricia Pedrozo Lamberti
article en

Abstract

Accurate estimation of vegetation carbon stocks is essential for monitoring climate change impacts, assessing ecosystem services, and informing global mitigation strategies. In recent years, the integration of remote sensing techniques with cloud-based platforms—particularly Google Earth Engine (GEE)—has transformed how vegetation dynamics and carbon fluxes are analyzed, largely through the widespread use of the Normalized Difference Vegetation Index (NDVI). This study presents a comprehensive bibliometric and thematic review of global research trends in vegetation carbon stock monitoring using GEE and NDVI, covering 91 peer-reviewed articles published between 2017 and early 2024. Analyses were conducted using the Bibliometrix R package and included publication patterns, leading contributors, geographic distribution, keyword evolution, sensor usage, and collaborative networks. Results indicate a substantial increase in scientific output since 2017, with China, the United States, and Brazil emerging as leading contributors. Most studies relied on MODIS, Landsat, and Sentinel-2 imagery within GEE workflows, with a growing trend toward multi-sensor integration and machine learning applications. Despite technical advancements, the review identifies persistent gaps in policy integration, in-situ validation, and geographic representation—particularly in carbon-rich but underrepresented regions of the Global South. We conclude by recommending enhanced international collaboration, expanded ground-truth validation efforts, and stronger alignment with climate policy instruments such as REDD+ and the Sustainable Development Goals (SDGs). This review provides a structured synthesis of the current state of GEE-based carbon monitoring research and highlights key opportunities to increase its scientific impact and policy relevance. • Growing focuses on applying GEE, NDVI, and remote sensing to carbon and environmental monitoring research. • Google Earth Engine and vegetation indices like NDVI are revolutionizing carbon stock monitoring and management. • Improved tools for measuring carbon stocks with greater precision. • Contributions align with major frameworks, including the UN 2030 Agenda and REDD+. • Essential to embed these tools into public policy frameworks.

Remote Sensing Applications Society and EnvironmentVol. 41
University of Augsburg (DE), Universidad Católica de Santiago de Guayaquil (EC), Universidade Católica Dom Bosco (BR)
Climate action
Openalex Percentile: Top 28%
Remote Sensing in Agriculture
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