## 4 Data Cubes and Land Surface Phenology--A New Perspectives
DE Australia was also used for land cover mapping across four test sites in two time periods [125]. In the research, an opportunity for generating annual land cover maps, using a consistent and internationally recognized taxonomy, is indicated. [PERSON] and [PERSON] [21] also used cloud-free Landsat orthomosaics from DE Africa for analyzing annual vegetation dynamics, i.e., long-term trends in vegetation dynamics by comparing annual fractional cover metrics. The authors report a decrease in vegetation and an increase in bare soil, equating to a loss that is equivalent to ~7.7% of Djibuti's arable area. Also, the authors estimate that it would take five times longer to analyze data on a local computer compared to the research conducted on the DE Africa servers using the EODC. Current knowledge about practices and lessons learned about EODCs is presented in the research by [PERSON] et al. [126]. By using China data cube, case studies on the spatial variation in water bodies and vegetation based on Landsat 8 and GF-1 imagery data have been conducted, respectively. The aforementioned research commends the use of EODCs for national-level vegetation mapping due to their efficiency in handling large spatio-temporal datasets. However, they highlight the potential for further validation through mobile applications or by incorporating higher-resolution data (e.g., Sentinel-2, PlanetScope, aerial, or drone imagery). Also, [PERSON] et al. [123] mention that, in comparison to optical data, EODC that use radar satellite imagery (e.g., synthetic aperture radar; SAR) are still rare, but it is becoming increasingly common as its applications continue to grow across various fields. [PERSON] et al. [22] demonstrated the feasibility of automatically producing ARD SAR data in an EODC for use in a large spatio-temporal data environment. They also mention that Swiss Data Cube is the first EODC that contains almost the entire Sentinel-1 ARD archive. Further advancements towards SAR-enabled EODCs are made in Australia [127] and the United Kingdom [128]. For instance, the ability to combine optical and radar data (e.g., Sentinel-1 and Sentinel-2) within a single data cube unlocks new opportunities to study vegetation dynamics under cloudy conditions or in densely vegetated regions [129; 130]. In a smaller study, we  
Figure 4: Visual representation of the Earth observation data cube. have investigated possibilities of Euro Data Cube [131] in Croatia, and soil moisture was calculated (Figure 5) by using time series of S1 SAR backscatter data, which is possible thanks to the frequent revisit time. Its usage can be beneficial for drought monitoring [132], agriculture and crop management [133], forestry, and land management [134]. While data cubes offer significant advantages in handling large-scale, multi-temporal satellite data, several challenges remain. [PERSON] et al. [135] focused on two key issues associated with EODC analysis: geometric distortions and spatio-temporal autocorrelation problems. The former issue can be associated with global or continental data analysis, where a simple longitude-latitude projection fits well for visualization, but for advanced data analysis, this can affect the accuracy of analyses, whereas the latter issue depicts adjacent observations that are more autocorrelated in space and time, and therefore test observations close to the training data will yield more accurate results [136]. Furthermore, storing and maintaining large datasets of satellite imagery can be costly and resource intensive, and this is one of the reasons why high-resolution satellite data at national levels are still not involved in EODC. Also, setting up and maintaining EODC requires significant technical expertise, and due to the immense storage requirements in applications that require real-time data (e.g., disaster monitoring, rapid response scenarios), data cubes may lag behind.