# _remote sensing_
## 3 Results  
### Coupling EO Data with Crop Models for Yield Prediction  
The activity aims to test the performances of the EO data assimilation into biophysical crop models to improve durum wheat predictions. The growing season of 2017-2018 in Maccarese (Italy) was used for calibration, obtaining a range of parameter values that represent a more realistic range for subsequent simulations. The following year (2018-2019) was used to test the efficiency of data assimilation on several wheat fields. EO imagery collected during the durum wheat growing season was aggregated by field, preprocessed, and used for data assimilation. For each field, the fitted LAI was assimilated into SAFV using EnKF, and a \"standard\" simulation without LAI assimilation (open loop) was also run (Figure 7). In addition to visual comparison of LAI trends, performance metrics were also calculated on yield (Table 2). LAI curves show a clear distinction between standard and EnKF simulations. The standard simulations of the five fields with wheat emerging in the first half of December (Figure 7a-e) generated almost identical LAI curves and, in turn, similar yield predictions. Indeed, initial conditions and meteorological forcing during the growing season were common to all the fields, and soil water limits were similar due to the homogeneity and low resolution of the ESDAC-JRC soil maps with respect to the farm dimensions. This resulted in similar simulations when EnKF was not used (open loop). In the other two fields (Figure 7f,g), durum wheat was sown and harvested earlier, and this was reflected by the LAI development. In all the cases, however, the EnKF procedure simulated LAI following almost exactly the curves fitted on Sentinel-2 data. This permitted adjusting the simulation trajectory daily, obtaining not only a better representation of LAI, but also a more realistic quantification of in-season biomass and yield growth. In fact, the validation on yield at the field level showed a three-fold improvement in yield estimations when EnKF was used, with respect to the standard simulation (Table 2). Figure 6: A false color map of the research area and survey area of Ningqiang, Shaanxi Province; the wheat planting area is shown in green, and the sampling points are marked yellow. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Simulation** & **RMSE t-ha\({}^{-1}\)** & **RRMSE \%** & **MAE \%** \\ \hline With data & \(1.42\) & \(31.7\) & \(1.14\) \\ assimilation & Open loop & \(4.42\) & \(97.3\) & \(3.97\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance metrics of yield estimations for 2019 with and without data assimilation. RMSE (root mean square error) and MAE (mean absolute value) are in tons per hectare, RRMSE (relative RMSE) is in percentage. Metrics are obtained by comparing predicted and observed yield for each field. Figure 7: Data assimilation of LAI retrieved from Sentinel 2 into SAFysw model. For each durum wheat field (a–g), the graphs represent the LAI curve fitted on Sentinel-2, the LAI simulated without data assimilation (open loop) and the LAI simulated with data assimilation (see legend in panel **h**)). EO-derived LAI curves and LAI curves simulated with data assimilation overlaps. The improvement in the simulation performances with data assimilation is testified by the lower discrepancies between predicted yield and yield measured by the combine harvester (lower RMSE, RRMSE and MAE). The data assimilation results are comparable to those of [13] for common wheat at the field scale in China. The authors obtained, over three years of experiments, an average RMSE of 1.1 t-ha\({}^{-1}\). Their RRMSE (18%) was lower than in our case, however, probably due to a narrower range of the observed yield, approximately from 3 to 8 t-ha\({}^{-1}\). In our case, durum wheat yield ranged from about 1 t-ha\({}^{-1}\) to more than 8 t-ha\({}^{-1}\).