# GF-1 Satellite Imagery Data Service and Application Based on Open Data Cube
###### Abstract  
With the application of big data in Earth observation, satellite imagery data are gradually becoming important means of observation for monitoring changes in vegetation, water bodies, and urbanization. Therefore, new satellite imagery data organization and management paradigms are urgently needed to fully mine the useful information from these data and provide new ways to better quantify and serve the sustainable development of resources and the environment. In this paper, a framework for processing and analyzing Chinese GF-1 satellite imagery data was developed using the latest technologies such as Open Data Cubeutilized. Therefore, new tools or techniques are urgently required to minimize the time, expertise, and tools needed to prepare, access, and analyze large-scale remote sensing data while mining useful information from these data to the maximum extent to support the sustainable development of resources and the environment [6]. In 2017, the Committee on Earth Observation Satellites (CEOS) launched the Open Data Cube (ODC) program [7]. As a generic term, ODC is often understood as a multidimensional array that simplifies data storage, data access, and data analysis to improve data organization, query, and analysis performance [8]. As a new data management approach applied to the organization and management of satellite imagery data, ODC shifts the paradigm from scene to pixel. ODC grids offer efficient storage for satellite imagery data with multi-temporal, multi-spatial, multi-spectral, and multi-attribute characteristics. In the meantime, ODC grids also take into account the time and space correlation of satellite imagery data and avoid the time and space separation of the scene management method, making the application of long time series and the analysis of image data simple and efficient. Actually, ODC facilitates programmatic access to satellite imagery data, while facilitating large-scale analysis that would not be possible if files needed to be accessed one by one. Currently, exploratory ODC applications have been conducted in countries such as the United States, Australia, and Switzerland. Using ODC as the base framework, these applications improve the effectiveness of satellite imagery data by providing easy data access for data scientists, researchers, local authorities, and business owners, while meeting the requirements of specific scenarios by customizing the programs on demand [9]. Research teams around the world have conducted a series of studies for different application scenarios. Australian researchers constructed ODCs for Landsat data dating back to 1987 [10] and mapped surface water time series across the Australian continent to provide rapid and high-resolution detection of continental-scale surface water changes [11; 12; 13; 14], environmental changes [15], mangrove forest expansion on the northern coast [16], and land coverage [17]. Researchers from Switzerland adopted the ODC to manage large amounts of Landsat and Sentinel imagery data in studies on vegetation [18], urbanization [19], water quality [20], and snow coverage [21], and they improved the understanding of the Swiss environment by monitoring its spatial and temporal changes [22]. Through the ODC initiative and its demonstration application in Africa, African research teams applied the ODC to Landsat and Sentinel imagery data management in key fields such as water coverage, land change, and urbanization consistent with the UN Sustainable Development Goals, which satisfied local and national decision-making needs [23; 24; 25; 26; 27; 28]. By fusing Landsat optical image data and Sentinel-1 radar image data, Vietnamese researchers developed a prototype application for flood monitoring in the lower Mekong River basin based on the ODC [29]. In addition, research teams from Colombia, Mexico, and Brazil applied the ODC to long-time-series Landsat and Sentinel imagery data, covering their countries for thematic studies of hydrology [30], soils [31], vegetation [32], and island area [30].