## 5 Conclusions  
We believe that Sentinel-2's improved temporal frequency benefitted the grain yield prediction models by adapting to phenology across ecozones in the geographically diverse region of Navarre and improved the spectral separation of bread wheat from other crop types by using precisely timed images at a moment of phenological differences. The improved Sentinel-2 spatial detail was also optimally harnessed by capturing field level variability with the use of zonal statistics as model input parameters, and contributed to improving total estimates related to both yield and crop types. Furthermore, the use of field-level grain yield data for both training and validation of the wheat grain yield estimation model proved to be an efficient approach. The matching of crop type classification and field-level grain yield estimation models are scarce in the scientific literature, as often studies either focus on classification [23; 24; 25; 26] or grain yield estimation [17; 18; 19; 20; 21; 22] alone. Notwithstanding, the combination of both as presented in this study provided improved data for a more robust model (i.e., comparison to official cropland area and regional yield statistics) and for testing its applicability in actual agricultural contexts. Moreover, another novel aspect that this research addresses is the study of large-scale field-level topographic effects on grain yield, which has not been studied extensively, as single fields or reduced-scale approaches are used in the majority of studies on this topic [73]. In addition, we have applied here various techniques to take maximal advantage of several different types of openly accessible data sources for the Navarre region in northern Spain, where no such study had been previously reported. It is especially relevant, as in 2020 the CAP in the European Union will be reformulated aiming to optimize resources and advance towards an integrated regional management. The CAP accounts for around 30% of the total European budget [77], and member states are responsible of supervising the declared croplands and harvests. The results obtained and the methodology here developed can be easily used and reproduced for both Navarre's regional government and other European regions with equivalent data availability and frameworks, easing their expensive field works. We here used polygon databases from agricultural fields declared in the CAP subvention system, public topographic and environmental data, as well as European Union and European Space Agency-launched Sentinel-2 openly accessible imagery. Furthermore, we believe that in order to obtain field-level crop yields in actual agricultural contexts, coordination with local farmers is central to this endeavor. In summary, this research successfully showed the ability of our relatively straightforward and potentially operational method for estimating wheat grain yield at field-level before harvest and determining the spatial factors that influence grain yield. Concerning grain yield estimation, two observations were deemed most pertinent: on the one hand this approach allows grain yield at approximately two months before harvesting (at the phenological stage of heading) to be estimated correctly, and on the other hand, it is applicable across at least a two-season period. The combination of our grain yield estimation model and crop type mapping also achieved accurate results with regards to official statistics. The techniques and models presented can be said to have successfully mapped wheat croplands and calculated regional grain yields. Furthermore, the observed spatial variation caused by topographic features (slope, aspect, and altitude) and rainfall could be attributed to explain on average 11% to 20% of spatial grain yield variation. Additional work is warranted in relation to the other genotype/varietal, environmental, or management (GxExM) factors that may account for regional grain yield variability across local to regional scales, such as those presented here for Navarre, Spain.