The increasing volume, temporal density, and diversity of satellite Earth observation (EO) data have fundamentally transformed quantitative vegetation remote sensing. Dense multi-sensor time series and computationally intensive modelling have rendered traditional download-and-process workflows increasingly impractical. Cloud-native computing—where data access, storage, and computation are co-located and analyses are executed in data-proximate environments—has therefore emerged as a key paradigm for scalable and reproducible vegetation EO analysis. This review provides a science-oriented synthesis of cloud-native EO for quantitative vegetation research. We examine architectural principles, data models, and compute patterns that shape how vegetation analyses are implemented, scaled, and scientifically interpreted. Particular attention is given to machine learning as a system component, including model lifecycle management, domain shift, and evaluation integrity in distributed environments. We analyse how cloud-native data abstractions influence algorithmic assumptions, validation design, and long-term product consistency, highlighting trade-offs between analytical complexity, computational cost, latency, and scientific robustness. We provide a forward-looking perspective on emerging imaging spectroscopy missions and the growing system-level requirements for reproducible, scalable, and uncertainty-aware vegetation analytics at continental-to-global scales. We also outline how cloud-native EO infrastructures are driving new scientific paradigms based on continuous monitoring, systematic reprocessing, and AI-driven modelling.
Verrelst et al. (2026) studied this question.