# _remote sensing_
## 4 Discussion  
Joining EO data and crop models through data assimilation proved to be an efficient approach to predict durum wheat yield. The results obtained at the Macacarese site provided three take home messages that are worth mentioning, considering the state of the art of EO studies and of previous works on similar topics. Firstly, yield was predicted by exploiting both EO data and crop models. Both methods have strength and weaknesses, but data assimilation is an efficient tool to get the best of both worlds [10]. Using optical EO data only has a limited applicability for the prediction of crop yield, since yield is an indirect property that, contrarily to canopy properties such as LAI, cannot usually be estimated solely by EO retrieval [10]. Conversely, process-based crop models can simulate complex processes of plant growth and development (and yield formation) by using well-established scientific concepts in the form of mathematical equations, but require some adjustment to real field measurements to obtain reliable results. A process-based approach of dynamic crop models can integrate the EO data with functions for the estimation of crop growth and yield that have general validity [50; 51]. Secondly, the combination of models and EO-retrieved LAI permitted the prediction of yield without requiring field work. The only information collected in the field was durum wheat yield, measured by the combine harvester. Extensive field data would have been required for calibration either with empirical methods based on regression to directly estimate yield from EO data [10], or with a crop model (such as SAFY) used without data assimilation [38]. Data assimilation permitted avoiding field work and at the same time reduced uncertainties in the simulations. Thirdly, so far SAFY has been used with data assimilation mostly for regional scale yield estimations [11; 15], but the experiment at Macacarese allowed for testing the performances of this method at the field scale. This highlighted that yield can be satisfactorily predicted at the field level, especially in an agricultural context such as the Italian one, where field dimensions are often of few hectares and simulations can be carried out at the field level. This first step in this direction opens the door for potential applications of the EO-model combination to plan crop management at the field or within-field scale and adjust it with in-season information. Several approaches can be used to exploit EO data to identify homogeneous management zones in crop fields [52], and crop models can be applied to these zones to plan agronomic management practices and adjust them during the season when new information is available, for example by applying variable rates of N based on the productive potential of each zone [53]. Coupling EO data with modeling approaches can also be used for in-season adjustments of irrigation plans of variable rate irrigation management zones [54]. All these applications primarily require accurate crop growth and development estimates that need little or no field work and that can be adjusted during the season when new EO data become available [55]. The mapping of potential anomalies on agricultural crops (i.e., winter wheat and maize), orchards and particular trees from a worldwide perspective is technically feasible thanks to the present generation and availability of EO based products that can systematically cover large areas of interest. Remote sensing images are widely used to study vegetation status, and can be a real aid in monitoring activities, especially to support \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Healthy** & **Yellow** & **User’s** & **Overall** & **Kappa** \\  & **Wheat** & **Rust** & **Accuracy (\%)** & **Accuracy (\%)** & **Coefficient** \\ \hline Healthy wheat & 16 & 4 & 80 & & \\ Yellow rust & 2 & 17 & 89.5 & 84.6 & 0.742 \\ Producer’s accuracy (\%) & 88.9 & 81 & & & \\ \hline \hline \end{tabular}
\end{table}