# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 6 Conclusions  
In this paper, a new methodology for mapping irrigated areas using Sentinel-1 time series was introduced. First, a temporal series of SAR backscattering coefficients from the S1 data was obtained at plot scale in VV and VH polarization. To remove the ambiguity between rainfall and irrigation events, the S1 signal at plot scale was compared to that obtained at grid scale (10 km \(\times\) 10 km). Then, two different transformations including the PCA and the WT were applied to both the S1 temporal series at plot scale \(\left(\sigma_{\text{p}}^{0}\right)\) and the temporal series of the difference between plot and grid \(\left(\Delta\sigma_{\text{pG}}^{0}\right)\). Using the PC dimensions and the WT coefficients, irrigated areas were classified in two different random forest classifiers. The results show a good overall accuracy (OA) for the RF classifier using the PC values (90.7%) and the WT coefficients (89.1%). Moreover, the PC transformation was applied on the NDVI time series obtained from Sentinel-2 optical images. Results showed that the RF classifier using optical data (NDVI) performs well with OA = 89.5%. The combined use of optical and SAR data (in PC values) slightly improved the classification accuracy (OA = 92.3%).  
Another approach tested in this study was the one-dimensional convolutional neural network (CNN). The convolutional neural network was applied to the S1 time series obtained at plot scale \(\sigma_{\text{P}}^{0}\) and difference between plot and grid scales \(\left(\Delta\sigma_{\text{P}\text{G}}^{0}\right)\). The results of the validation of CNN approach showed very high accuracy (OAm = 94.1%) compared to that obtained using the RF classifiers. The accuracy of the irrigated class increased when using the CNN approach thus allowing better detection of irrigated plots. The use of NDVI data only in a CNN classifier produced lower overall accuracy (91.5%) than that obtained using S1 data only. However, the combined use of both data types in the CNN did not significantly improve the accuracy.  
The proposed approach should be applied to other agricultural areas to better assess its relevance and possible usage in operational mode. Given the very good accuracy obtained and the fact that S1 data is free and open access, the use of Sentinel-1 data is relevant. Even though the optical data presents good results, its use could be problematic in certain geographical areas due to the presence of cloud cover. However, machine learning algorithms including classical or advanced approaches still require training data to obtain good classification results. Thus, future work should also focus on building effective approaches to detect irrigation using less training data.  
[PERSON]. ([PERSON]) and [PERSON] conceived and designed the experiments; [PERSON]. ([PERSON]) and [PERSON] performed the experiments; [PERSON]. ([PERSON]) and [PERSON] analyzed the results; [PERSON]. ([PERSON]) wrote the article; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] ([PERSON]), [PERSON], and [PERSON] revised the paper.  
This research received funding from the French Space Study Center (CNES, TOSCA 2019 project), and the National Research Institute of Science and Technology for Environment and Agriculture (IRSTEA).  
The authors wish to thank the French Space Study Center (TOSCA 2019) and the National Research Institute of Science and Technology for Environment and Agriculture (IRSTEA). The authors wish also to thank the European Space Agency (ESA) for the Sentinel-1 and Sentinel-2 data and Theia pole for the calibration of the Sentinel-2 images. Finally, authors would like to thank the General Direction of Rural Development of the Department of Agriculture, Livestock, Fishing and Food of the Generalitat of Catalunya for providing the SIGPAC data.  
The authors declare no conflicts of interest.