## 5 Conclusions  
Correlations between the yield and spectral bands were low. When locations and years were compared, it was observed that the limiting factor was the year, as the differences in \(R\) values between yield and all spectral bands showed a weak correlation between one year and another but not between different locations. The results of [PERSON]'s analysis were similar to those of [PERSON]'s coefficients, with no improvement. The low and non-uniform correlations have complicated the design of an interpretable framework for agronomic decisions using RS data. The influence of growth stages in modifying the spectral response on the canopy and the yield physiology could represent significant limitations to directly identifying yield variability with only RS data. The expected behavior at each spectral region was changeable, affecting correlation values and the theoretical assumptions. The NIR (B08) spectral band was the most reliable for the estimating yield, consistently showing the highest \(R\) values at PI. This band is also available at a 10 m spatial resolution, making it particularly valuable for a detailed analysis. Visible spectral bands were inadequate to assess the NIR reflectance influence, and the correlations between both regions were disrupted.  
The final recommendation is to consider two periods for using RS data. The first, with only vegetative structures (from MT to PI), is designing strategies to increase NIR reflectance and decrease red reflectance. The second, from PI to harvest, is avoiding the relations between crop greenness and NIR reflectance, i.e., high NIR values would not be observed on low visible reflectance areas, and excessive crop greenness would not result in higher yields. Due to the differences between years, future studies are needed to analyze the mentioned pattern. NIR reflectance was a better variable than VIs, but the single use of this spectral band could be improved using complementary variables.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15073870/s1](https://www.mdpi.com/article/10.3390/app15073870/s1)]([https://www.mdpi.com/article/10.3390/app15073870/s1](https://www.mdpi.com/article/10.3390/app15073870/s1)), Figure S1 shows the detailed year analysis from Figure 5, Figure S2 shows an example about the spatial variability in the study area, all tables (Tables S1-S6) show the study of correlations for each location and year by spectral reflectance or VI throughout the different growth stages.  
Conceptualization, A.S.B., C.R. and B.F.; methodology, A.S.B., D.F., C.R., A.U., S.C.-I. and B.F.; software, A.S.B., D.F., C.R., D.T.-S. and S.C.-I.; validation, A.S.B., D.F., C.R., A.U., S.C.-I. and B.F.; formal analysis, A.S.B., D.F., C.R., A.U. and B.F.; investigation, A.S.B., D.F., C.R., A.U., S.C.-I., D.T.-S. and B.F.; resources, A.S.B., C.R. and B.F.; data curation, A.S.B., C.R., D.F., A.U., S.C.-I. and D.T.-S.; writing--original draft preparation, A.S.B., D.F., C.R., A.U., S.C.-I., D.T.-S. and B.F.; writing--review and editing, A.S.B., D.F., C.R., A.U. and B.F.; visualization, A.S.B., D.F., C.R., S.C.-I. and B.F.; supervision, A.S.B., D.F., C.R., A.U., S.C.-I., D.T.-S. and B.F.; project administration, A.S.B. and C.R.; funding acquisition, A.S.B., C.R. and B.F. All authors have read and agreed to the published version of the manuscript.  
This research has been funded by the PREDIC-PRO project SCPP2100C008733 XV0 (CPP2021-008733) of the State Research Agency of the Ministry of Science, Innovation and Universities, and ACIF Generalitat Valenciana, European Union (European Social Fund. Investing in Your Future) (CIACIF/2021/143).  
Not applicable.  
The original contributions presented in this study are included in the article and Supplementary Materials. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.