## 5 Conclusions  
This review highlights the significant advancements in monitoring and predicting LSP enabled by satellite EO missions and machine learning techniques. Satellite missions such as AVHRR, MODIS, and Landsat have been instrumental in capturing vegetation cycles across varying spatial and temporal scales. They provide essential data for understanding ecosystem dynamics, climate variability, and anthropogenic impacts on land surfaces. While these EO missions have advanced LSP research, their varying resolutions and data continuities pose certain limitations, highlighting the need for complementary approaches and improved data integration.  
Machine learning methods offer promising capabilities for LSP prediction, enabling more accurate short-term vegetation phenology prediction and cropland suitability as  
Figure 5: Time series values of soil moisture calculated in Euro DC using S1 backscatter data.  
sessment. These approaches are particularly valuable for improving decision making in agriculture and land management, where understanding short-term phenological shifts can serve as a basis for adaptation strategies. In addition, data cubes provide a transformative solution for accessing and analyzing EO data. They enable the integration of diverse data sources into structured, multidimensional formats that streamline LSP analysis.  
Overall, this review highlights key EO satellite missions for analyzing LSP across various spatial and temporal scales. It highlights the use of open EO satellite data and machine learning techniques for processing big data using cube infrastructures to improve LSP monitoring and prediction. Future research should focus on improving data accessibility, advancing machine learning models tailored to LSP applications, and fostering interdisciplinary collaborations to address both global and regional environmental challenges. By consolidating the current state of knowledge and identifying emerging trends, this review provides a foundation for further innovation in LSP research that will ultimately contribute to more resilient and informed environmental and agricultural practices.  
Conceptualization, M.G.; methodology, M.G., I.P., D.R. and D.D.; software, M.G., I.P., D.R. and D.D.; validation, M.G. and I.P.; formal analysis, D.R. and D.D.; investigation, M.G., I.P., D.R. and D.D.; resources, M.G.; data curation, M.G., I.P., D.R. and D.D.; writing--original draft preparation, M.G., I.P., D.R. and D.D.; writing--review and editing, M.G. and I.P.; visualization, D.R. and D.D.; supervision, M.G.; project administration, M.G.; funding acquisition, M.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Croatia Science Foundation, grant number IP-2022-10-5711.  
Not applicable.  
This work was supported by the Croatian Science Foundation for the ALCAR project: \"Assessment of the Long-term Climatic and Anthropogenic Effects on the Spatio-temporal Vegetated Land Surface Dynamics in Croatia using Earth Observation Data\" (Grant No. HRZZ IP-2022-10-5711).  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.