## 5 Results and Discussion
### Optimal Time Period for Collecting Satellite Images  
This section is devoted to detecting the optimal temporal windows for gathering the satellite imagery time series, as well as crucial time periods for under-greenhouse crop identification. The first step was to assess the classification accuracy attained from each of the nine available single orthoimages, always using the no GLCM strategy. Table 6 shows that the best classification results were obtained from the WV2 orthoimage, likely due to its much higher spatial resolution. With regard to the eight Landsat 8 orthoimages, the best OA values were assessed from the images taken in August  
Figure 6: Decision tree computed for the Landsat 8 time series dataset and no GLCM strategy. Only nine out of 34 terminal nodes are shown. N is the number of objects in each node, Class 1 (C1) = aubergine, C2 = cucumber, C3 = pepper, C4 = tomato. AGO11, 11 August; JUL26, 26 July; JUN24, 24 June; NOV15, 15 November; SEP12, 12 September. (73.2%), on 26 July (69.7%) and in September (67%). The OA values for the rest of the images ranged from 65.4% to 63.2%. According to the agricultural practices of the working area, the importance of the images taken in August and late July could lie in the application of white paint. Along September, some crops reach a significant size, and therefore, leaf pigments (chlorophyll, carotenoids and anthocyanins) begin to influence the signal captured by the satellites (e.g., NIR band), although in this month, there are still numerous greenhouse coverings painted white (Figure 5g). In November, most of the greenhouses have been washed, so being transparent to the electromagnetic radiation reflected from the crops inside (Figure 5h). However the OA value computed from this single period was quite poor. Table 7 shows the accuracy results for different multi-temporal satellite imagery combinations. The best combination of three Landsat images included the scenes corresponding to 26 July, August and September, achieving an OA of 75.5%. The time series profile was improved by adding the November information, the OA value increasing to 77.8%. The information contained on the scene taken in June, the period related to the greenhouse preparation for the transplant stage, also turned out to be relevant to improve OA up to a value of 79.8%. Information about previous crops given by the May scene did not raise the final accuracy. When the WV2 dataset was also added to Landsat time series, the best OA values (81.1% and 82.3%) were achieved. Likely, including more temporal data within the growing season from June 2013 to March 2014 would have led to improving the current results. It is worth noting that a couple of medium spatial resolution super-spectral satellites, named Sentinel-2, is going to be launched in 2015-2016 by the European Space Agency. These satellites will be able to acquire images of the Earth covering 13 spectral bands with spatial resolutions of 10 m (four visible and NIR bands), 20 m (six red-edge/SWIR bands) and 60 m (three atmospheric correction bands). The revisit time will be only five days from the two-satellite constellation with open and free data access [54]. Another issue to consider in further works would be the use of seasonal statistics [55] or some automated crop mapping algorithms based on the shape of the time series profile of VIs [2], which should be more consistent over years than snapshot values the original images. Both options could contribute to making the multi-temporal approach less dependent on the availability of particular acquisition dates and less susceptible to missing data on crucial dates (e.g., due to cloud presence or incomplete spatial coverage).