# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 3 Methodology
### Convolutional Neural Network  
Convolutional Neural Networks are deep learning models that achieve state-of-the-art performance on image related classification tasks [55] and, nowadays, they are getting increasing attention in the field of remote sensing [35]. Here, one dimensional (1D) CNN model (CNN1D) was proposed to leverage the convolutional characteristics of such networks to manage the time dimension of Sentinel-1 time series data. In this scenario, a simple and effective CNN architecture that involves standard operation was conceived: Convolution, nonlinear activation function, batch normalization and dropout. Each convolutional layer is applied on the valid portion of the time series (without any kind of padding) and it is associated with a convolution combined with a rectifier linear unit (ReLU) activation function [56] to induce non-linearity. Successively, a batch normalization step [57] is employed to accelerate the network convergence and reduce the internal covariate shift. Finally, Dropout [58] was adopted with a drop rate equal to 0.4, i.e., 40% of the neurons are randomly deactivated at each propagation step.  
Due to the fact that S1 time series and NDVI time series have different lengths, a two branch architecture (one CNN1D for the S1 time series and one CNN1D for the NDVI time series) that performs late fusion [59] of the input sources was proposed. The fusion is achieved with a simple concatenation of the features extracted from each branch. The concatenated features are successively used by two fully connected layers to produce the final classification. The fully connected layers are still associated with a nonlinear activation function (ReLU) and both of them contain 512 neurons. The two-branch network architecture is trained end-to-end. Figure 5 represents the architecture of the developed CNN. Each layer of the network involves the following operations: Convolution with ReLU function (Conv), batch normalization (BN), and dropout (DropOut): DropOut(BN(Conv(.))). For each convolution, the number of filters used are 64, 128, or 256, the Kernel size is \(7\times 1\), \(5\times 1\), \(3\times 1\) or \(1\times 1\) and the stride value is 1 or 2. Once the features are combined (concatenation), two free fully connected layers are employed to provide the final decision. The output layer (the last layer of the network) has as many neurons as the number of classes to predict (2).  
For all deep learning approaches, the Adam method [60] to learn the model parameters with a learning rate equals to \(1\times 10^{-4}\) was used. The training process is conducted over 1000 epochs with a batch size of 256. Considering the train/test splits (50%, 50%) for the neural network strategies, the data was split as 50%, 40% and 10% for training, validation and test set respectively [36]. The model that achieves the best performances on the validation set will be successively blindly evaluated on the test set.  
Similar to what was proposed for the RF classifiers, two scenarios were first considered for the deep learning approach one using only the S1 time series data and the other using only the NDVItime series. To do this, one of the two branches of the model is completely dropped and the one corresponding to the specific data source is kept. Then, for the third scenario using SAR and optical data, the full model with both branches was used.