Cloud computing is the technology that can address this issue, with Google Earth Engine (GEE) and AWS being prominent commercial services. The most convenient way to use satellite image ARD, such as Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS), and Sentinel, is to utilize GEE. The GEE platform provides these satellite data and offers preprocessed ARD data that users can directly utilize by performing various processing steps [17]. The GEE platform offers satellite information processing algorithms that support various time series satellite big data analysis applications [18]. Users can upload their own processed results to be used as ARD data. GEE has the potential to support both artificial intelligence (AI) methods and infrastructure to utilize satellite information as a type of big data, but a smoother integration of the two technologies is needed to achieve this, according to [PERSON] et al. [19]. As for an ARD guideline for remote sensing products, the Committee on EO Satellites (CEOS) has pioneered a basic definition and specification [20]. The CEOS-ARD can be defined as attributes of actual measurement products for global remote sensing users with remote sensing applications such as surface reflectance, surface temperature, and normalized backscatter, and it means the minimum level for time series analysis and multi-typed data interoperability. Especially, CEOS Analysis Ready Data for Land (CARD4L) products are processed to minimum requirements and organized into a guideline that allow for immediate analysis with a minimum of additional user-side efforts. They can be resampled as a common geometric grid and would provide reference data for increased interoperability over time and with other datasets. [PERSON] et al. [21] discussed data cubes linked to a cloud computing environment for EO data sets. [PERSON] et al. [22] tested Earth Observation Data Cube (EODC) to facilitate the production of essential variables at different scales and to benefit from the spatial and temporal dimensions of satellite EO data for enhanced environmental monitoring. [PERSON] et al. [23] implemented data cube of Sen2 Cube for Sentinel-2 MultiSpectral Instrument (MSI) images covering Austria, with knowledge-based large EO data analysis capabilities. The practical benefits of applying the data cube approach as an infrastructure for managing and analyzing large-scale satellite imagery have been demonstrated by initiatives such as Euro Data Cube [24]. Geoscience Australia (GA) and Digital Earth Australia (DEA) initiated the Open Data Cube project intending to develop tools for processing and analyzing large-scale geospatial data. Initially, the development focused on Landsat satellite image data. In 2015, Geoscience Australia released the Australian Geoscience Data Cube. The overall goal of the ODC is to facilitate the management of extensive data collection, eliminating the need for data to adhere to specific storage formats or locations. In essence, this entails the ability to direct the ODC to data repositories and index the data in its existing location, thereby abstracting the complexities associated with monitoring large and distributed data holdings [25; 26; 27]. ODC operates in a cloud environment due to its handling of large-scale EO data, and the ODC application services developed so far are deployed in an AWS-based environment. [PERSON] et al. [28; 29] developed the Brazil Data Cube on AWS, to provide services to create, integrate, discover, access, and process the data sets to produce land use and cover maps using time series analysis and machine learning schemes, with ARD generation capabilities. The Japan Aerospace Exploration Agency (JAXA) provides that the public release of the ScanSAR products are provided as CEOS-ARD normalized radar backscatter (NRB) on AWS data exchange [30]. Digital Earth Africa provides Sentinel-1 backscatter products developed to comply with the CARD4L specifications on AWS [31; 32]. [PERSON] et al.