## 1 Introduction
Furthermore, the impact of climate change on agricultural practices underscores the necessity of employing advanced predictive tools [12]. Alterations in temperature and precipitation patterns can significantly disrupt traditional growing cycles, necessitating adaptive strategies to maintain crop productivity. The capacity of machine learning for real-time data integration allows for continuous model refinement, providing adaptability, which is crucial in dynamic agricultural settings where conditions fluctuate frequently. In addition to phenology prediction, machine learning plays a vital role in assessing cropland suitability by analyzing complex relationships between abiotic factors, such as soil composition, climate variables, and topography, and their influence on crop yields [13]. The integration of these diverse datasets enables more precise evaluations of land suitability for specific crops, thereby optimizing agricultural practices [14]. As the field progresses, machine learning stands out as a robust framework for understanding and predicting how environmental factors influence vegetation dynamics and agricultural productivity across varied ecosystems. The massive quantity of EO also requires suitable technologies for big data storage and management, such as novel big Earth observation data cube (EODC) infrastructures [15]. Earth observation data cubes (EODCs) represent a transformative approach to how users engage with EO data, offering an innovative solution for the efficient storage, organization, management, and analysis of large-scale EO datasets [16]. Currently, at a national level, various operational EODCs have been developed to support the management and analysis of satellite data for monitoring environmental changes, agriculture, land cover, etc. As such, Digital Earth Australia (DE Australia) [17], as the world's first operational EODC, provides a data cube for Australia, making large-scale spatio-temporal Earth observation data easily accessible [18; 19]. The Swiss Data Cube [20] provides analysis-ready data (ARD) to support environmental monitoring and sustainable development in Switzerland, and Digital Earth Africa (DEA) [17] enables coastline monitoring, cropland extent maps, or analyzing the long-term temporal vegetation trends in arid Africa [21]. Also, Brazil, Mexico, Tanzania, and Vietnam have either adopted or are in the process of adopting an open-source solution. These data cubes not only provide access to national and regional EO data but also include tools and platforms to support analysis, visualization, and decision making across a wide range of sectors. The aforementioned EODCs provide large collections of ARD data, i.e., data that can be processed with a minimum set of requirements [22], including large sets of EO data acquired from optical (e.g., Landsat, Sentinel-2) or radar (Sentinel-1; S1) satellites. Nowadays, EODCs are part of a growing effort to make EO data more accessible and usable for scientists, policymakers, and other stakeholders [23]. This manuscript provides a brief review of key EO satellite missions useful for analyzing LSP dynamics at different spatial and temporal scales. It provides a synthesized overview of how open EO satellite missions, especially those using machine learning methods for big data analysis, can be applied to monitor land surface phenology. The main goals of this brief review are to synthesize current knowledge on the use of global open satellite EO missions suitable for monitoring LSP and to highlight emerging techniques in LSP prediction through machine learning. By examining key EO missions, including AVHRR, MODIS, and Landsat, this study aims to underscore the strengths and limitations of different satellite missions in capturing spatio-temporal variations in LSP. Additionally, the review explores recent advances in machine learning for short-term vegetation phenology forecasting and cropland suitability assessment, which are critical for dynamic land management and agricultural planning.