## 6 Conclusions  
As far as we know, this work is the first attempt to identify horticultural crops that are growing under greenhouse plastic coverings by using multi-temporal and multi-source satellite images. To this end, several spectral, textural and VIs object-based features were extracted from a WV2 orthoimage and eight multi-temporal Landsat 8 pan-sharpened orthoimages by using an OBIA approach. These previously-segmented objects were subsequently analyzed by means of the DT classifier. When the full Landsat 8 time series was applied, classification results reached an OA of 81.3% from performing the 10-fold cross-validation method over 694 reference greenhouses or the ground truth. The classification results were quite good in the case of tomato and pepper crops, presenting \(F_{\beta}\) values of around 87%, but worse on cucumber and aubergine crops, with \(F_{\beta}\) values ranging from 50% to 63%. These results hardly improved by adding the information of the WV2 single image, which had been used to undertake the initial segmentation stage. In fact, the best OA reached 82.3% (kappa coefficient of 0.71) when the WV2 data were added to five Landsat images (8 June, 26 July, August, September and November). The most relevant object-based features were related to spectral information (mean reflectance and SD values) and VIs. The most important spectral information for correct classification of different crops under greenhouses was mainly due to the greenhouse management practices. As regard VIs features, GVI, GNDVI and NDSVI turned out to be the most significant ones. The second-order textural parameters based on GLCM added no information in order to improve the classification accuracy. As is widely known, outdoor crop classification is rather complex due to agronomic factors and farming decisions. Different crops can exhibit very similar developmental patterns and growth calendars. In addition, the same crop may be sown on different dates due to the farmers' decision. All of these variables become much more complex in the case of crops under greenhouses, where the existence of plastic coverings and their particular management during the growing season make the \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Combinations**} & \multirow{2}{*}{**No. of**} & \multirow{2}{*}{**OA (\%)**} & \multirow{2}{*}{**Kappa**} & \multicolumn{4}{c}{\(F_{\beta}\) (\%)**} \\  & & & & **Coefficient** & & **Aubergine** & **Cucumber** & **Pepper** & **Tomato** \\ \hline \multirow{8}{*}{**Only Landsat**} & 26 July, August, September & 63 & 75.5 & 0.58 & 17.6 & 39.0 & 87.4 & 81.9 \\  & 26 July, August, & & & & & & & \\  & September, November & & & & & & & \\  & 8 June, 26 July, August, & & & & & & & \\  & May, 8 June, 26 July, August, & & & & & & & \\  & September, November & & & & & & & \\ \hline \multirow{8}{*}{**WV2 + Landsat**} & WV2, 26 July, August, & & & & & & & \\  & August, September & & & & & & & \\  & WV2, 26 July, August, & & & & & & & \\  & September, November & & & & & & & \\  & 8 June, 26 July, August, & & & & & & & \\  & September, November & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 7: Accuracy indices from different combinations of multi-temporal orthoimages using the no GLCM strategy. crop identification via remote sensing more difficult. The most defining characteristic for crop identification in this work turned out to be the unique whitewashing management in the case of pepper crop. Thus, high reflectance values in the SWIR1 and SWIR2 bands in the months of July and August were related to the white paint applied to the plastic coverings of this crop. In this regard, pepper could be discriminated from other crops through a DT binary classification by using Landsat 8 time series and only five features (AGO11 mean SWIR2, JUL26 mean SWIR1, JUN24 mean green, SEP12 SD blue and JUL26 GVI), attaining an OA of 92.7% and a kappa coefficient of 0.81.