## 1 Introduction
Sentinel-2's relatively high temporal (5 days at the equator) and spectral resolution (13 spectral bands) make it particularly suitable for tracking subtle changes in crop conditions throughout the growing season [24]. Time-series vegetation index data have been widely used in previous studies, providing a potential way to improve crop yield potential prediction accuracy compared to traditional methods [25]. However, yield forecasting can be further refined by incorporating phenology metrics, which capture the timing and duration of key crop growth stages [26]. Phenology modeling utilizes remote sensing to identify critical transition points in the growing cycle, such as the start of season (SOS), peak of season (POS), and end of season (EOS), which are influenced by climatic conditions, soil properties, and agronomic practices in the field [27]. With the relatively high temporal resolution of the Sentinel-2 satellite mission allowing for the calculation of dense phenological curves, the detection of anomalies, such as delayed emergence or premature senescence due to drought or disease, is enabled [28]. These metrics can be integrated into machine learning regression models to improve relation ships between phenological dynamics and crop yield potential but the present state of research on the topic is scarce. Recent studies, such as [PERSON] et al. [29], [PERSON] [30], and [PERSON] et al. [31], have shown that combining phenological features with machine learning improves yield prediction accuracy across crops and environments, especially when using time-sensitive indices during flowering or grain-filling stages. Specifically, [PERSON] et al. [31] demonstrated significant performance gains in maize yield forecasting when using phenology-based vegetation features, which were focused on growing degree day-based modeling. Similarly, [PERSON] et al. [32] and [PERSON] et al. [33] emphasized that integrating dynamic indicators like phenology stages into machine learning enhances spatial and temporal generalizability compared to using aggregated indices alone. Despite these advancements, neither study considered a comprehensive combination of several vegetation indices and phenology metrics, particularly under varied climatic and management regimes. The integration of machine learning into agriculture has notably improved traditional farming practices by enabling data-driven decision-making, improving resource efficiency, and enhancing crop productivity [34; 35; 36]. One of the most promising applications of machine learning is crop yield potential prediction, which utilizes computational algorithms to analyze complex agricultural datasets and forecast yields with high accuracy [37]. By processing big data derived from multispectral satellite imagery, machine learning models can uncover non-linear relationships that cannot be detected using conventional statistical methods, thereby optimizing agricultural management and reducing uncertainty in yield potential prediction [38]. A key advantage of machine learning in agriculture is its ability to integrate multiple combinations of vegetation indices and phenology metrics, which are critical for assessing crop health and growth stages [39]. Vegetation indices serve as proxies for chlorophyll content, biomass, and photosynthetic activity, while phenology metrics capture the timing of key growth stages [28]. Machine learning algorithms based on a wide range of prediction approaches, including decision trees, support vector machines and neural networks, can process these temporal and spatial datasets to identify yield-influencing covariates [40]. By training models on historical yield data alongside real-time remote sensing inputs, machine learning systems can predict yield potential at field, regional, or global scales, accounting for variability in soil types, microclimates, and farming practices [41].