High-resolution Pléiades data: an in-depth analysis of applications and future prospects

This comprehensive review explores the multifaceted applications of high-resolution Pléiades data across various scientific disciplines, including agriculture, forestry, oceanography, coastal science, urban planning, sprawl analysis, environmental science, and atmospheric research. The review process utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework for collecting and categorizing the published paper on Pléiades data across various disciplines. The indices-based approach provides biophysical and biochemical parameter estimations, such as biomass and forest/crop health, city planning, etc. There is big potential of the Pléiades data in distinguishing different land use classes using popular classification algorithms like Maximum Likelihood (ML), Spectral Angle Mapper (SAM), Random Forest (RF), and Mahalanobis distance (MD). This study emphasizes the utility of high-resolution data like Pléiades in addressing the United Nations’ Sustainable Development Goals (SDGs). Pléiades can be useful directly in SDG 1,2,6,9,11,13,14, and 15 and indirectly in every one of the goals.