Distribution-Based Approach for Efficient Storage and Indexing of Massive Infrared Hyperspectral Sounding Data

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 21; p. 4088
• Main Authors: Li, Han, Gu, Mingjian, Shi, Guang, Hu, Yong, Xie, Mengzhen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2024
• Subjects: Algorithms; Atmospheric sounding; Big Data; Cloud computing; Data acquisition; Data assimilation; Data storage; Datasets; Detectors; Distributed processing; distributed storage; Documents; Efficiency; Geospatial data; HBase; HIRAS; Indexing; Information storage and retrieval; Infrared detectors; infrared hyperspectral sounding data; kubernetes; Metadata; Meteorological satellites; Network reliability; Queries; Remote sensing; Satellite observation; Satellites; Spatial data; Vertical distribution; China; United States > US; Europe
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16214088

Hyperspectral infrared atmospheric sounding data, characterized by their high vertical resolution, play a crucial role in capturing three-dimensional atmospheric spatial information. The hyperspectral infrared atmospheric detectors HIRAS/HIRAS-II, mounted on the FY3D/EF satellite, have established an initial global coverage network for atmospheric sounding. The collaborative observation approach involving multiple satellites will improve both the coverage and responsiveness of data acquisition, thereby enhancing the overall quality and reliability of the data. In response to the increasing number of channels, the rapid growth of data volume, and the specific requirements of multi-satellite joint observation applications with infrared hyperspectral sounding data, this paper introduces an efficient storage and indexing method for infrared hyperspectral sounding data within a distributed architecture for the first time. The proposed approach, built on the Kubernetes cloud platform, utilizes the Google S2 discrete grid spatial indexing algorithm to establish a grid-based hierarchical model for unified metadata-embedded documents. Additionally, it optimizes the rowkey design using the BPDS model, thereby enabling the distributed storage of data in HBase. The experimental results demonstrate that the query efficiency of the Google S2 grid-based embedded document model is superior to that of the traditional flat model, achieving a query time that is only 35.6% of the latter for a dataset of 5 million records. Additionally, this method exhibits better data distribution characteristics within the global grid compared to the H3 algorithm. Leveraging the BPDS model, the HBase distributed storage system adeptly balances the node load and counteracts the detrimental effects caused by the accumulation of time-series remote sensing images. This architecture significantly enhances both storage and query efficiency, thus laying a robust foundation for forthcoming distributed computing.