## 4 Data Cubes and Land Surface Phenology--A New Perspectives  
Phenological research encompasses different directions and focuses on how biological cycles respond to environmental factors and climate change [113]. Key areas include the study of phenological shifts due to global warming, trophic imbalances, and extreme weather events and their effects on ecological interactions. Technological advances, particularly in satellite-based remote sensing and Internet of Things (IoT) sensors, have enabled large-scale and high-resolution monitoring of these changes [114]. Applications in agriculture and forestry address challenges such as crop cycles and forest management, while conservation efforts target migratory species, biodiversity, and invasive species dynamics. Phenology also relates to human health by monitoring allergen cycles and vector-borne disease patterns [115, 116]. Modeling and evolutionary studies provide insights into future changes and adaptation mechanisms, and interdisciplinary approaches link phenology with cultural knowledge and socio-economic impacts. In this context, integrating data cubes represents a new opportunity for analyzing land surface phenology (LSP). It provides unprecedented access to multidimensional satellite Earth observation (EO) data to enhance environmental, agricultural, and forestry applications. Satellite remote sensing imagery presents an inevitable global source of synoptic data that enables retrospective characterization into the dynamics of the vegetated land surface and associated geophysical and biogeochemical processes [117]. Remote sensing enables acquisition both of long-term observations and information about the intra-seasonal dynamics of vegetation, or land surface phenology. LSP allows researchers to study important phenological events such as the start of the growing season, peak greenness, and senescence (vegetation browning) at large spatial scales. Traditionally, LSP has been tracked using time-series data from satellite observations, such as those provided by MODIS or Landsat missions [118, 119, 120]. A major challenge in applying satellite data for LSP is the trade-off between revisit frequency and spatial detail; e.g., the MODIS satellite has high revisit frequency but relatively low spatial resolution, and on the other side, the Landsat satellite missions have finer spatial resolution but lack temporal resolution [121, 122]. Therefore, the emergence of Earth observation data cubes (EODCs) offers a promising new approach to overcoming many of these limitations. Data cubes organize satellite data in a multidimensional structure, where data is indexed by latitude or easting, longitude or northing, and time (Figure 4), allowing for seamless spatio-temporal analysis of large datasets. The  
Figure 3: The number of cropland suitability studies indexed in Web of Science Core Collection (WoSCC) based on topic search of “cropland suitability” or “agricultural land suitability”. capability of EODCs to harmonize data across scales and integrate diverse datasets marks a paradigm shift in phenology research. By using EODCs, researchers can monitor the LSP more efficiently through improved data access (e.g., satellite imagery in analysis-ready format) and integration (e.g., combined data from different sensors such as Landsat, MODIS, and Sentinel, which helps mitigate issues like cloud cover), enhanced temporal and spatial resolution, and efficient big data management. As such, [PERSON] et al. [123] used an analysis-ready data cube with co-registered and harmonized time series of Sentinel-2 and Landsat-8 data for grassland moving events mapping. With such a workflow, and through vegetation index time series, maps of temperate grassland management for four years for all of Germany with a spatial resolution of 10 m were easily generated. Furthermore, [PERSON] et al. [124] used a multi-decadal archive of Landsat imagery from DE Australia to identify inland waterbodies across Australia and to characterize the change in their surface area from 1987-2018.