## 4 Discussion  
In the NDVI time series in Figure 6, the maize crop decreased from the end of mid-season until harvesting (senescence stage). This results from the fact that most of the maize parcels were sown in September/October 2014, while the first EO acquisition date was 26 March 2015 (DOY 85). The same trend was also observed in the VV + VH backscatter time series, indicating that both types of EO data are correlated. Although only half of a crop growth cycle is retrieved from the EO data, the results are promising because they demonstrate that this correlation is also possible for the late season period. Bean parcels, sown in late February 2015, exhibited higher NDVI values in the beginning of the curve corresponding to the leaf development stage (Figure 6). At the senescence stage, the bean crop presented the same trend as that of maize. Because soybean was sown in December 2014, the lower NDVI values in the beginning of the curve are explained by the crop growth stage (senescence) and by the typical short height of soybean (approximately 0.45 m). Pasture showed a smoother curve for the three time series, in agreement with the fact that pasture is always in the leaf development stage due to it being cut several times during the growing season. From the NDVI single-plot curves, it is possible to distinguish the two different stage plots for maize (one leaf development stage plot and one flowering stage plot) from the remaining 26 senescence stage plots. For bean, there is also a clear distinction among the three different phenological stages (one leaf development plot, one fruit stage plot and three flowering stage plots). However, using the VV + VH backscatter curves, it is not possible to distinguish among different stage crop plots because, due to the lack of the northern part of the Sentinel-1A images for three epochs, 14 maize plots and two bean plots were not considered for the analysis (the excluded plots included the leaf development and the flowering stage plots for maize and the leaf development and fruit stage plots for bean). Figure 12: Total crop water requirement (m\({}^{3}\cdot\) day\({}^{-1}\)) volume. \begin{table}
\begin{tabular}{c c} \hline \hline
**Crop Type** & **Total Area (ha)** \\ \hline Maize & 1311.47 \\ Soybean & 302.87 \\ Pasture & 62.72 \\ Bean & 97.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Total area of all of the parcels identified in the field for each crop type (ha). In Figure 7, a significant correlation is observed, especially between the VV backscatter and the NDVI values, demonstrating the consistency of both optical and microwave time series and that optical data affected by clouds can be replaced by microwave data. This overcomes one of the main limitations of these type of studies, _i.e._, the reduced amount of EO data due to meteorological conditions. This result agrees with those of [41], in which the potential of different TerraSAR-X incidence angles and polarizations for mapping sugarcane harvests, where a high correlation between the radar signal and NDVI index, calculated from SPOT-4/5, was observed. However, our results differ from those obtained during the AgriSAR campaign [42], in which gamma VH backscatter correlated strongly with NDVI for conalo and field pea throughout the entire growth cycle. In AgriSAR, it was verified that among cereal crops, the correlation with NDVI is much weaker when analyzed over the entire growth cycle, exhibiting only a strong correlation for the initial vegetative growth stages until booting/inflorescence. According to the overall accuracy and kappa coefficient values in Table 7, the NN classification results were more accurate than those of the SVM classifier (-8% for the overall accuracy and ~15% for the kappa coefficient). SVM applied in [43] for land cover characterization using MODIS time series data was compared to two conventional non-parametric image classification algorithms: NN and classification and regression trees (CART).