## 5 Results and Discussion
### Trends to Be Further Explored
#### 5.3.4 Data Cubes and ARD  
The unprecedented availability of satellite data derived from recent changes in costs of imagery and computing technology have enabled a new approach to work with big data, integrating different platforms and data providers [22; 33]. One potential form for encompassing data storage and processing, network requirements, and web applications, providing interoperability, access, analytics, and the sharing of EO imagery is the organization of the data in the broadly referred data cube format [49; 200]. [PERSON] et al. [137] defined data cube as a regular, non-overlapping, tiles of gridded sensor data, stacked according to the time of data capture (observation), leading to a data cube visual metaphor. [PERSON] et al. [143] considered a data cube as an infrastructure defined to allow data analysis, ready to be used, and interoperable at the programmatic level of the users. This term is widely used to refer to data integrated into multidimensional structures. [PERSON] and [PERSON] [201] considered data cube as a 3D array with two spatial dimensions, one temporal dimension, and an arbitrary number of attributes with the spatial dimensions which refer to a spatial reference system; the cells of a data cube have a constant spatial size; there is no spatial or temporal overlap between the cells; temporal reference is a known set of temporal intervals; and a cell has a vector of attributes for every combination of dimensions. Both data cube and big data concepts converge along with remote sensing imagery into Earth Observation Data Cubes (EODC), an approach to storing, organizing, managing, and analyzing Earth Observation data [202]. The main objective of EODC is to facilitate EO data usage by addressing Volume, Velocity, Variety challenges, and providing access to large spatio-temporal data in an analysis-ready format [203]. Focusing on analyzing LULCC trajectories, EODC can be observed as a time series associated with spatially aligned partitions of space ready for analysis [201], outperforming other technologies for time series analysis of large satellite analysis-ready data (ARD) [143]. Many initiatives have been developed to build high temporal frequency data cubes to generate accurate LULC classification, such as the Australian Data Cube [137] and the Swiss Data Cube [57]. Additionally, operational platforms and technologies, such as Open Data Cube (ODC) [137], e-sensing [204], and Google Earth Engine (GEE) [205] are capable of analyzing EO data in a data cube format. These initiatives are paving the way for broadening the use of EO data to larger communities of users, supporting decision-makers with information converted into meaningful geophysical variables, and ultimately unlocking the information power of EO data [136]. [PERSON] et al. [137] described the creation of data cubes using surface reflectance products from remote sensing images and a software environment to manage them called the Australian Geoscience Data Cube (AGDC). This definition includes not only the data cubes of satellite imagery, but also the processes to build and the software environment required to access, prepare, index, and manage them. The AGDC was transformed into the AGDCv2, supporting other coordinate systems, formats, and data provenance, and renamed to ODC with operational deployments initiatives, such as the operational EO service called Digital Earth Australia (DEA) [206]. [PERSON] et al. [57] used interoperable service chains to produce Landsat ARD for the Swiss Data Cube (SDC). To have ARD products ingested, stored in a database, and readily available, the SDC defined a workflow for ARD production, based on data acquisition, pre-processing, and conversion to surface reflectance. [PERSON] et al. [207] proposed the semantic EO data cube, which has for each observation at least one categorical interpretation that can be queried in the same instance.