An EXPERIMENT TESTBED FOR Sentinel2 products

The increasing availability of remote sensing data from various satellite platforms presents significant challenges due to the complexity and heterogeneity of the images and sensors. This growing complexity makes it difficult for users and scientists to search, access, and utilize Earth Observation (EO) imagery quickly and efficiently. Additionally, transforming EO imagery into useful information, such as ready-to-use aggregated indices across various scales, remains a challenge. To address these issues, advanced tools are needed to manage and analyze large volumes of time-series data. Discrete Global Grid Systems (DGGS) offer a unified framework for integrating EO data, combining multisource data, and utilizing cloud computing on a global scale.

The global scale of geospatial data requires dividing the Earth into regions, often using local coordinates reference systems, to address inconsistencies and distortions in traditional map projections. Indexing is essential for efficiently querying remote sensing data, and DGGS provide a solution by subdividing the Earth's surface into equal-size regions, enabling more effective geospatial data indexing. By aggregating cells and using hierarchical properties, the size of the indexed data, such as cloud masks, can be significantly reduced without losing information. DGGS indexing also allows faster and more accurate querying compared to traditional approaches. This facilitates the retrieval of products based on precise information corresponding to a region of interest, while most data provider APIs only implement global metrics, such as the cloud percentage for an entire product.