## 1 Introduction
Especially, the linear correlations between surface reflectance and important biophysical parameters in assessing crop status, such as the Leaf Area Index (LAI), Leaf Chlorophyll Content (LCC), or biomass, are used to explain the implementation of predictive models for such parameters [29]. Regarding this, leaf reflectance is directly correlated with the mentioned biophysical parameters [30], but regarding yield assessments, correlations can turn indirect, and the expected implications that reflectance and derived products show on yield can be highly variable [31; 32]. Farmers aim to obtain fast control of all yield input parameters; for that reason, crop systems are often designed based on linear correlations to modify crop management rapidly [33; 34]. The difficulty relies on identifying the limit of linear correlations; for instance, nitrogen rates are increased linearly up to a saturation point, and a weed incidence assessment is performed considering a partial linear correlation with yields in pest and disease management or weather effects [35; 36; 37; 38]. Despite having the Sentinel-2 constellation fully completed at the operational level since 2017, few studies have simulated the real applicability of rice, comparing various years and regions at the within-field level [32]. The implementation of models to estimate yield variability in the rice industry when satellite imagery is used is commonly based on the spectral band's combination in Vegetation Indices (VIs), aiming for a normalized value to interpret the measurement efficiently [39]. Several indices were designed based on linear correlations among bands, canopy properties, and the negative collinearity in comparing visible (blue, green, red) and near-infrared (NIR) [40]. In different satellite platforms of lower spatial (MODIS) and temporal resolution (Landsat missions), the use of VIs has been the solution to ensure easy data interpretability in cereal crops [41], but the results were highly variable to confirm the actual yield variability accurately [42]. The yield physiology may be uncorrelated with LAI, LCC, and biomass, owing to the carbohydrate partitioning in the grain-filling process [43]. Specifically, the correlation between reflectance and yield may vary with rice growth stages, summarized as intensive vegetative growth to produce fertile tillers, the process of panicle differentiation and flowering, and the accumulation of biomass in the grains, consuming the previous vegetative structures [44]. Therefore, the expected knowledge of RS can be altered to interpret each growth stage, modifying some usually assumed correlations; for example, an excessive crop greenness can result from low grain filling, resulting in higher NDVI but lower yields [45]. Recent studies have used the satellite bands from Sentinel-2, without combination in an index, to obtain linear regression models in wheat, corn, soybean, and rice [46; 47; 48]. The results were promising for better transferability because when only VIs are used, one spectral band can be overwhelmed by the other, losing information in the actual reflectance at each spectral region. [PERSON] et al. [49] reported that the red band reaches a saturation value level before the NIR band. The increasing resolution and accuracy for future spatial missions add special significance to analyzing individual reflectance values. The Sentinel-2 mission provides new bands at a 20 m resolution in the Red edge region (wavelengths between visible and NIR) and the Short Wave Infrared (SWIR) region. Spectral signatures of crop canopies can improve the applicability of the VIs, enhancing their interpretability. Similarly, ML algorithms require the analysis of basic correlations between each spectral band and crop yields at the within-field level. Thus, a fusion among spectral properties, VIs, and yield at commercial farms to study interpretable correlations between all variables lacks extensive database works in the rice industry. The study of [PERSON] et al.