*Result*: Incremental Data Cube Architecture for Sentinel-2 Time Series: Multi-Cube Approaches to Dynamic Baseline Construction.

*Highlights: What are the main findings? We introduce a Multi-Cube architecture for Sentinel-2 time series that integrates spatial hashing, dynamic baseline policies, intelligent parallelism and automated subdivision of large areas into independent, temporally consistent cubes using Zarr-based storage. This design enables fully incremental and scalable data cube construction. Applied to 83,755 Sentinel-2 Level-2A images (2016–2024), the framework achieves 5.4× faster end-to-end processing and over two orders of magnitude less disk Input/Output than a conventional sequential pipeline, thanks to its incremental update engine and optimized multi-tile handling strategy. What are the implications of the main finding? The framework provides interoperable cubes that grow incrementally and remain independent of analytical methods, allowing flexible integration with diverse workflows. This design offers a future oriented backbone for cloud-native EO systems, enabling analysts to adopt or replace algorithms without restructuring the data architecture and supporting reproducibility, long-term maintainability and seamless integration while also benefiting from shorter processing times. Beyond incremental ingestion, the architecture addresses the challenge of preserving temporal consistency across multi-tile areas by automatically generating stable and hash indexed units with coherent temporal baselines. This reduces mosaicking artifacts and configuration overhead, enabling reliable multi-year monitoring with minimal manual intervention. Incremental computing is becoming increasingly important for processing large-scale datasets. In satellite imagery, spatial resolution, temporal depth, and large files pose significant computational challenges, requiring efficient architectures to manage processing time and resource usage. Accordingly, in this study, we propose a dynamic architecture, termed Multi-Cube, for optical satellite time series. The framework introduces a modular and baseline-aware approach that enables scalable subdivision, incremental growth, and consistent management of spatiotemporal data. Built on NetCDF, xarray, and Zarr, Multi-Cube automatically constructs stable multidimensional data cubes while minimizing redundant reprocessing, formalizing automated internal decisions governing cube subdivision, baseline reuse, and incremental updates to support recurrent monitoring workflows. Its performance was evaluated using more than 83,000 Sentinel-2 images (covering 2016–2024) across multiple areas of interest. The proposed approach achieved a 5.4× reduction in end-to-end runtime, decreasing execution time from 53 h to 9 h, while disk I/O requirements were reduced by more than two orders of magnitude compared with a traditional sequential reprocessing pipeline. The framework supports parallel execution and on-demand sub-cube extraction for responsive large-area monitoring while internally handling incremental updates and adaptive cube management without requiring manual intervention. The results demonstrate that the Multi-Cube architecture provides a decision-driven foundation for integrating dynamic Earth observation workflows with analytical modules. [ABSTRACT FROM AUTHOR]

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