Unveiling the research landscape of planetscope data in addressing earth-environmental issues: a bibliometric analysis

Abstract

Abstract The PlanetScope (PS) satellite constellation, developed by Planet Labs Inc., represents a significant advancement in Earth observation, offering high spatial resolution and daily revisit capabilities. This study provides a comprehensive bibliometric analysis of PS satellite imagery, exploring its utilization in scientific research from 2017 to 2023. Using data extracted from the Scopus database, 582 documents were analyzed to uncover the publication trends, key research disciplines, collaboration networks, and research themes related to PS imagery. The results highlight the increasing use of PS data in Earth and Planetary Sciences, Environmental Science, and Computer Science, with a notable concentration of research outputs from the United States, China, and Brazil. Furthermore, our findings indicate that PS data is applied in diverse fields, including land use/land cover classification, agriculture, environmental monitoring, and disaster assessment. Notably, machine learning techniques are increasingly applied to PS data, enhancing analysis capabilities. Despite the growing adoption of PS imagery, challenges related to data access, particularly in low-income regions, were identified, and PS data often plays a secondary or supplementary role in many studies. Recommendations for enhancing interdisciplinary collaboration, expanding open-access data programs, and integrating advanced processing techniques are proposed to maximize the impact of PS data in addressing global environmental challenges. This study provides valuable insights into the evolving landscape of PS-based research, emphasizing the potential of PS data and identifying areas for future exploration.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 7 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.