## 1 Introduction  
Bread wheat (_Triticum aestivum_ L.) is an important grain in Spain, representing 3% of the total of European production [1]. The majority of wheat production is located in central and southern Spain, which corresponds to the Castille and Andalusia regions. Navarre's wheat croplands in northern Spain, where this paper focuses, also have significant production. Furthermore, Navarre hosts several Spanish agroclimates and a wide range of water regimes in a relatively compact spatial area due to its topographical heterogeneity, which can serve as a good proxy for helping to understand wheat performance across all Spanish agrosystems. Estimating wheat grain yield, mapping wheat crop lands, and studying spatial interactions that affect grain yields can be of great help for farmers and agricultural institutions. This is especially relevant in the European Union, as the Common Agricultural Policy (CAP) subsidies requirements are linked to cropland uses and member states have to verify the declared field area, the sowed crop type and harvest [2]. Regarding wheat grain yield, there are two main ways to estimate it that take advantage of remote sensing data; on the one hand empirical models based on actual field data and spectral-based vegetation indices, which have been successfully used with Sentinel-2 data and wheat grain yield estimation [3; 4], and on the other hand growth models [5] involving physiological parameter estimations based on, for instance, radiative transfer models. The availability of data [6] and computer processing requirements needed for crop growth modelling make empirical models more versatile to use in practice for limited datasets, which is the case of this study. Furthermore, in order to scale field-level grain yield predictions regionally, crop type mapping is essential as it can target the fields on which to apply the grain yield prediction models. Both field-level classifications and total wheat cropland area estimations are of great interest for improving agricultural management. spatial resolution). Added limitations in relation to the lack of representativeness of the crop cut in comparison to the whole field-level grain yield are expected in addition to difficulties when applying such models at a broader, namely regional scale. If such models aim to be applied to actual crop fields, addressing field-level yields, capturing field level variability, and obtaining such accurate data in coordination with farmers while stressing the mutual benefits of such an approach is pivotal. Regarding cropland and crop type classification, Sentinel-2 imagery has also been used to successfully classify and map agricultural sites [23; 24; 25; 26; 27]. Moreover, the Sentinel-2 improved multispectral and temporal frequency can champion grain yield estimation [28] and crop type classification in comparison with other openly accessible satellites [29]. Thus, the use of Sentinel-2 imagery and field-level wheat grain yield data in coordination with the local farmers brings a novel approach for estimating wheat performance and allows for exploring the context effects (i.e., topographic features and rainfall) on wheat grain yield at the field-level. Mapping wheat grain yield regionally can also contribute to furthering our understanding of spatial factors that impact crop performance. In agriculture, a relatively important limiting factor is topography, as many topographically influenced landscape attributes affect grain yield [30]. Examples of topographical features that are likely to affect yields are slope [31], altitude and aspect [32]. Besides topography, water is often the main limiting factor in agricultural performance impeding optimal yields. Therefore, understanding the spatial dynamics of rainfall can help in understanding grain yield gaps, especially in rain-fed wheat agrosystems like those prevailing in Navarre that also represent the great majority of wheat cultivation environments in Spain [33].