[33; 34] implemented the China Data Cube (CDC) framework for Landsat 8 and Gaofen-1 (GF-1) ARD based on ODC. The ODC-Spatio Temporal Asset Catalogs (STAC) API provides the functionality to access Sentinel-2 imagery on AWS [35]. Such as these trends, the data cube system for satellite imagery that has been developed or built is mainly based on AWS. The CEOS-ARD data cube application model is also advantageous for integration with AWS. Of course, although AWS is currently the most widely used and versatile cloud environment, there may be situations where it is built on other types of clouds. In addition, application models that integrate open-source cloud environments with data cubes can have many advantages in terms of both economic and technological aspects. Of course, the economic advantage of open-source cloud platforms is that there are no license costs. The technological advantage is that only the necessary elements can be applied according to the target service, making the system lightweight and optimization easier. However, such advantages are based on prior knowledge or experience of open-source cloud technology specifications and development environments. With such technological backgrounds, there are advantages such as the ease of extending the system to multiple clouds. OpenStack is a popular cloud platform for implementing application services such as IaaS, an open-source software cloud computing platform [36]. As expected, there are some essential cases in which a service system has been developed to manage, analyze, and distribute EO satellite images using OpenStack as an IaaS environment. OpenStackis a fundamental technology used in the NASA Earthdata program to support the Earthdata Cloud [37]. The Earthdata Cloud is a cloud-based platform that hosts and provides access to many Earth science data, including remote sensing imagery. OpenStack is also a core technology in the EODC program, a part of the European Space Agency (ESA) Earth Observation Exploitation Platform, which promotes the development of data exploitation platforms to enable efficient access, processing, and analysis of Earth observation data [38]. [PERSON] and [PERSON] [39] proposed a distributed system for remote sensing data pre-processing and ingestion using OpenStack to integrate data into the Hadoop storage system, notably the Hadoop Distributed File System (HDFS), HBase, and Hive. [PERSON] et al. [40] implemented a cloud-based satellite image processing service using the open-source stack for geo-based data processing ([[https://ksatdb.kari.re.kr/main/main.do](https://ksatdb.kari.re.kr/main/main.do)]([https://ksatdb.kari.re.kr/main/main.do](https://ksatdb.kari.re.kr/main/main.do)) (accessed on 26 June 2023)) at the Korea Aerospace Research Institute (KARI). [PERSON] et al. tested the performance of remote sensing image processing services based on the PaaS of Cloud Foundry and OpenStack [41]. [PERSON] and [PERSON] implemented OpenStack-based SaaS to produce top-of-atmospheric reflectance (TOAR) and top-of-canopy reflectance (TOCR) for KOMPSAT-3/3A Satellite Image [42]. In this study, we first described the design and components of the multi-cloud system configuration. We also presented a sequence diagram executed according to this design strategy. As a result of the system implementation, we presented the ODC processing steps in a multi-cloud environment using the TOAR/TOCR ARD of KOMPSAT-3/3A bundled images and a simple example application in the ODC UI.