## 1 Introduction  
Conventional crop yield estimation relies heavily on field surveys, manual sampling, and agrometeorological models, which are often labor-intensive, time-consuming, and prone to human error [1]. Traditional methods involve collecting data from representative plots through destructive sampling, where crops are harvested and weighed to extrapolate yield for larger areas [2]. Additionally, empirical models based on historical yield data, weather parameters, and soil conditions are used to predict productivity [3]. While these approaches have been the backbone of agricultural planning and policymaking, they suffer from several limitations, including spatial inconsistency, delayed reporting, and high operational costs [4]. Moreover, such methods lack real-time monitoring capabilities,making them less responsive to sudden environmental stressors, such as droughs, pests, or diseases [5]. These inefficiencies can lead to inaccurate yield forecasts, affecting food security assessments, market pricing, and resource allocation. Given the increasing global population and climate variability, precise and timely yield potential prediction is critical for ensuring sustainable agricultural productivity [6]. Transitioning from conventional to data-driven approaches can enhance accuracy, reduce costs, and support proactive decision-making in agriculture [7]. Thus, improving yield potential prediction methodologies is not only essential for optimizing farm management but also for strengthening food supply chains and mitigating economic risks in the face of growing climatic uncertainties [8]. Unlike traditional yield mapping, which relies on expensive yield sensors mounted on combine harvesters to collect post-harvest data [9], yield potential prediction based on remote sensing data provides a potential low-cost alternative for optimizing crop production in forthcoming years. By identifying underperforming fields early, precision agriculture strategies can be implemented to narrow yield gaps, leading to more sustainable production [10]. As climate change introduces greater unpredictability in growing conditions, early and accurate yield potential estimation becomes even more critical [11]. By shifting from reactive yield mapping to predictive analytics, agriculture can transition toward data-driven, climate-resilient farming, ensuring stable production in the coming decades [12]. This approach not only improves farm-level profitability but also contributes to global food security by reducing waste and optimizing land use efficiency [13]. Notably, maize and soybean are globally vital crops [14;15], serving as key sources of food, feed, and biofuel, while their symbiotic nitrogen fixation and high productivity contribute significantly to agricultural sustainability and food security. Since traditional yield estimation methods, including field surveys and agrometeorological models, are often limited by spatial and temporal scalability, as well as high costs, remote sensing was increasingly researched to help overcome these obstacles [16]. In particular, multispectral satellite missions, like Sentinel-2, offer a transformative alternative by enabling continuous, non-destructive monitoring of crop health and growth dynamics [17; 18]. Among the most applicable data for crop yield potential prediction derived from satellite data are vegetation indices and phenology metrics, which provide key insights into plant biophysical parameters and developmental stages, thereby enhancing yield prediction models [19]. Vegetation indices based on multispectral imagery, among which the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) are most frequently used in previous research [20; 21; 22], provide high correlations with crop vigor, biomass, and photosynthetic activity. These indices exploit the reflectance properties of vegetation in the red and near-infrared spectral bands, allowing for the quantification of chlorophyll content and canopy structure [23].