## 4 Discussion
The correlation value in a year and location comparison could be due to weather adversities, as different authors have recently developed models integrating satellite data with meteorology [74,75]. Models incorporating weather variables are designed to provide the potential yield loss, but crop management can be crucial to modify the expected behavior [76,77]. Few studies have analyzed the physiological response to different crop-management strategies in extensive data at the within-field level [78,79]; regarding this, we found difficulties in explaining the differences between locations and years. The correlations between Sentinel-2 and yields were also variable depending on the spectral region and growth stage. Firstly, the \(R\) values were similar to those obtained by the literature in similar studies with drone imagery and ML techniques; in this regard, [PERSON] et al. [68] also reported low yield correlations. Correlations were of high values when an external source of variability was studied, e.g., different nitrogen rates [80,81]. Secondly, in Sentinel-2 studies, high correlations can also be found when external variability is included in the experimental design [82]. From this consideration, few studies on rice at the within-field level examine the general estimation of yield variability with Sentinel-2 in a large dataset. Consequently, a comparison with similar studies in the experimental design is addressed to field-level studies; [PERSON] et al. [21] reviewed the state-of-the-art reporting with satellite imagery, low coefficients of determination, and considerable errors. In addition, nonlinear and ML approaches showed similar final transferability to the linear analysis. In other cereal and soybean crops, we can find studies at a within-field level in commercial fields with a similar experimental design. [PERSON] and [PERSON] [50] compared airborne and satellite imagery using yield maps from previous years; the correlations with yields using the images were weak and similar to those reported in our study. The historical yield map was a better source of information for anticipating variability when repeated over the years. However, they found a representative number of fields with errors in the estimation when such variability was not repeated, making the images a suitable alternative. The results of those authors could be related to the present work. Thus, the weak correlation in a large dataset of commercial fields should be an expected result. As noted in the study, the correlation between yield and the red reflectance was weak but consistently negative over locations and years. Similarly, [PERSON] et al. [47] reported higher importance of NIR reflectance in estimating the yield in various studies [46,83]. However, the correlation values were also insufficient; other authors have shown the year dependence [51] and random patterns as significant limitations in providing an applicable framework [84]. Consequently, the industry's applicability is focused on identifying similar management zones based on VIs rather than absolute yield values [85]. We can find recent studies in the EU with the use of Sentinel-2 [86]. Thus, despite the difficulties in finding high-accuracy models, PA practices consider such limitations and base its applicability on the positive or negative sense of correlations with yields, as well as the theoretical basis that reflectance shows with some biophysical parameters (LAI, LCC, or biomass) [87,88,89]. Visible bandsstrongly correlate with the chlorophyll content, decreasing its reflectance values when the pigment content is increased [39]. The correlation between a high chlorophyll content and a high yield can be found in several studies about rice genetics [90; 91; 92], showing an indirect correlation between the reflectance and yield. Knowing this interaction, we can discuss the different results across the growth stages.