## 1 Introduction  
The world's cultivated area depends on crop yields, as increased yields would reduce the area of land used for agriculture. This leads to considerable efforts to produce according to efficiency criteria, more with less [1]. Rice is the staple food in several countries and is essential for food security [2]. Breeding programs are vital to increase yields, but their techniques are usually time-consuming [3]. In this sense, efficient crop management through Precision Agriculture (PA) tools can be an alternative to improve average yields [4]. Precision Agriculture is based on the recognition of spatial and temporal variability in crop production. It uses information technology (IT) in agricultural management with the aim of increasing productivity. Cropmanagement is carried out considering a yield goal per cultivar; field decisions during the season when an adverse condition occurs, e.g., weather constraints, failures in management, or pest incidence, are based on a large amount of data from different sources. Therefore, several authors studied rice yield variability to build a pre-harvest forecast model, joining the different crop inputs [5; 6; 7; 8; 9]. The ORYZA2000 model developed by the International Rice Research Institute (IRRI) is a suitable example [10], but its applicability depends on the availability of an enormous database [11]. The use of PA requires different approaches to quickly evaluate field situations [12]. As a solution, in recent years, remote sensing (RS) in satellite platforms has emerged as a helpful technology in improving yield forecasts [13]. Images acquired by the Sentinel-2 satellite have introduced a new era in RS applications due to free data access, high temporal (5 days) and spatial resolution (10 m), and accuracy in measured reflectance [14]. Other existing technologies can be of higher spatial resolution, i.e., drone systems and equipped cameras on the farm machinery [15]; furthermore, private satellite platforms, such as the Planet Dove constellation, exhibit a better temporal resolution [16]. However, the open access to Sentinel-2 derived from the European Union (EU) public service demands novelty studies to deeply understand the data profitability for agronomic decisions at the farm scale [17]. Both industry and public institutions can benefit from analyzing the relations between crop productivity and data from public investments in new technologies, a key challenge in the EU's future [18]. Currently, in the application of Sentinel-2 data, the analysis at the within-field level based on commercial fields across different years and areas to obtain an interpretable framework is a critical necessity [19; 20]. The analytic methods to estimate yield variability are changeable. According to [PERSON] et al. [21], empirical, process-based crop models, and semi-empirical models are the three main categories. On the one hand, empirical models are simple and site-specific; the equations are dependent on the database used in the study. On the other hand, process-based and semi-empirical models can be mechanistic and have high accuracy; however, several parameters about crop management and growing are required [22]. These mechanistic methods can lead to limitations similar to those of the classical models described above due to the needed complementary sensor systems and the problematic implementation in a changing agricultural system at the commercial level [23]. New efforts in the empirical approaches using Machine Learning (ML) techniques could obtain a transferable solution at the within-field level [24; 25; 26] and provide a fast yield forecast. However, interpretability is still challenging when the results are applied to a different database [27]. In addition, linear regression analysis can be performed to interpret ML outputs. The literature has widely studied linear regression, which is usually considered a pre-analysis for developing RS-based models [28].