## 4 Discussion  
In Section 1, the actual within-field yield anomaly provided the potential yield lost or gained compared to the average field. Few studies on rice cultivation analyze the actual within-field variability. [PERSON] et al. [67] reported a maximum standard deviation of 1013 kg\(\cdot\)ha\({}^{-1}\) in Italy. Also, [PERSON] et al. [68] found a similar deviation in Arkansas (U.S.). Despite the different metrics, this value would have a higher absolute variability than our study. The differences could be explained in terms of the experimental design; the first-mentioned study carried out different PA experimental practices, whereas, in our study, all fields followed the standard grower practices with no PA practices. Older studies, such as the one by [PERSON] and [PERSON] [69], showed differences between the maximum and minimum pixel of around 5000 kg\(\cdot\)ha\({}^{-1}\) in California (U.S.); in that case, fields of larger areas (38 and 52 ha) were studied. In Russia, in a 3.60 ha field, [PERSON] [70] measured differences of about 8000 kg\(\cdot\)ha\({}^{-1}\) between the maximum and minimum yield, attributed to extreme weed growth. The variability found in our work was low. The yield variability when the data were considered as a single matrix (mixing the 10 m pixels of all fields by location and year) made studying the correlation with Sentinel-2 reflectance and VIs possible. For instance, mean-field yields at location 1 differed by an average of 4609 kg\(\cdot\)ha\({}^{-1}\), comparing the best with the worst field for each year; at location 2, the difference was 1940 kg\(\cdot\)ha\({}^{-1}\). These differences between fields would be notable since the cultivar was the same, and there were no water supply restrictions. Regarding the productivity increase, the differences would show an interesting yield gap in both locations, indicating the existence of crop management with excellent yields in some fields. The applicability of Sentinel-2 data should aim to find this variability during the growing season. Locations and years were analyzed as separate matrices, with a similar study conducted by [PERSON] and [PERSON] [50] on corn, wheat, soybean, and cotton, who reported a significant influence of weather variables when years and locations are mixed. Several studies focused on modeling weather effects on rice yield, finding complex interactions between them and crop yields [71; 72; 73]; in our research, we considered year-by-year and location-by-location to avoid these interactions. As a result, we can believe that the vari \begin{table}
\begin{tabular}{c c c c c} \hline  & **B04** & **B08** & **B11** & **NDVI** \\ \hline MT & \(-\)0.38 & 0.40 & 0.35 & 0.43 \\ PI & \(-\)0.16 & 0.54 & 0.41 & 0.33 \\ GF & 0.02 & 0.36 & 0.13 & 0.11 \\ \hline \end{tabular}
\end{table} 
Table 11: Results of the Spearman rank analysis between the yield and the bands B04, B08, and B11, and the index NDVI in the three main stages. Each value is the mean for all locations and years. ability would be due to other yield input parameters. Therefore, the fields in each location were nearby, featuring highly similar annual weather trends. Thus, the main differences appeared between years and locations. Future work should be addressed to examine the inconsistencies in the \(R\) values. We could not find a clear pattern for integrating weather variables into our analysis. This assumption was derived from the database structure, i.e., crop management differed in each field, resulting in multiple strategies to avoid weather constraints. For example, the delayed sowing in 2022 at location 1 provoked essential changes in the timing of drying downs for herbicide spraying and top-dressing fertilization; some farmers carried out shorter drying downs to mitigate the detrimental effect of high temperatures. Consequently, the high temperatures may have impacted the fields differently.