## 1 Introduction  
Land surface dynamics of the vegetation cover is an important indicator of natural forcings such as climate change and human-related activities. Mapping and quantifying the status of the spatial and temporal shift in land surface dynamics, together with drivers of change, is vital for identifying vulnerable areas, assessing the level of impact on various ecosystems, their adaptive capacity, and the overall long-term sustainability of the present state land cover [1]. Phenology is the study of recurring life-cycle events that are initiated and driven by environmental factors and provide a framework for observation of the temporal changes in the vegetated land surface. Phenological studies integrate climate-biosphere relationships and can be used to document and evaluate the effects of climate change at scales from individual species to broader levels. Observing and documenting changes in the phenology of various species support efforts to reconstruct past climates and make predictions about biological responses to future climate scenarios [2]. Land surface phenology [3; 4; 5] is defined as the spatio-temporal pattern of the vegetated land surface on the regional or global scale and is typically characterized using satellite remote sensing products. In addition to determining the effect of the climate impacts [6],land surface phenology (LSP) metrics can be used to demonstrate the anthropogenic effects on the land cover, such as the large-scale institutional changes [3]. Nowadays, remote sensing presents an inevitable technology that makes possible synoptic observations of the land surface dynamics at the scales that are very appropriate for precise evaluation of the behavior of various natural or man-made ecosystems. In evaluating the spatio-temporal dynamics of the vegetation on global or regional scales, there are two \"workhorses\", or satellite data sources, which are frequently used: advanced very-high resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS). AVHRR provides a global analysis of vegetation dynamics and the longest time period of observation from 1982 onwards at a coarser spatial resolution of about 1 km [7]. MODIS presents a constellation of two space instruments, Terra and Aqua, which continuously provide Earth observations (EO) from 1999 (2002). Both instruments, AVHRR and MODIS, have very high temporal resolution; they image the entire Earth every 1 to 2 days. Due to the mentioned characteristics and the observation period of more than four (AVHRR) or two decades (MODIS), they are very useful to track changes on Earth over time. In this regard, the goal of this work is to make an overview of the most recent findings that utilize various applications of these satellite data after multi-decadal (full timeframe) operative monitoring of the Earth's surface. Of particular interest in this review were studies and analyses of the observed impact of climate change on the vegetation dynamics and land surface phenology, together with other man-made drivers such as urbanization, afforestation, and land abandonment. Machine learning has become an increasingly important tool in agriculture and forestry, particularly in the areas of vegetation phenology prediction and crop suitability assessment [8; 9]. By utilizing rich datasets from diverse remote sensing sources, such as satellite imagery, unmanned aerial vehicles, and ground sensors, machine learning models can effectively analyze and interpret the complex interactions between environmental factors and biological responses in plants [10]. These models excel at managing the inherent variability in vegetation phenology across geographic regions, adapting to local conditions and phenological behaviors. The ability to integrate various environmental covariates, such as temperature, precipitation, soil moisture, and solar radiation, enables these models to provide predictions of the timing of critical biological events, such as flowering and harvesting [11].