Mashaba-Munghemezulu, ZinhleNduku, LwandileMunghemezulu, CilenceChirima, Johannes George2025-08-062025-08-062024-06Mashaba-Munghemezulu, Z.; Nduku, L.; Munghemezulu, C.; Chirima, G.J. Research Progress in the Application of Google Earth Engine for Grasslands Based on a Bibliometric Analysis. Grasses 2024, 3, 69–83. https://doi.org/10.3390/grasses3020006.2813-3463 (online)10.3390/grasses3020006http://hdl.handle.net/2263/103794DATA AVAILABILITY STATEMENT : Data used in this study are available on request.Grasslands cover approximately 40% of the Earth’s surface. Thus, they play a pivotal role in supporting biodiversity, ecosystem services, and human livelihoods. These ecosystems provide crucial habitats for specialized plant and animal species, act as carbon sinks to mitigate climate change, and are vital for agriculture and pastoralism. However, grasslands face ongoing threats from certain factors, like land use changes, overgrazing, and climate change. Geospatial technologies have become indispensable to manage and protect these valuable ecosystems. This review focuses on the application of Google Earth Engine (GEE) in grasslands. The study presents a bibliometric analysis of research conducted between 2016–2023. Findings from the analysis reveal a significant growth in the use of GEE and different remote sensing products for grassland studies. Most authors reported grassland degradation in most countries. Additionally, China leads in research contributions, followed by the United States and Brazil. However, the analysis highlights the need for greater involvement from developing countries, particularly in Africa. Furthermore, it highlights the global distribution of research efforts, emphasizes the need for broader international participation.en© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/4.0/)Google Earth Engine (GEE)GrasslandsBibliometric analysisRemote sensingArticle