**4. Discussion**

It can be seen from the above results that the performance of a traditionally used water index method is not satisfying, especially in urban areas. This indicates the common problems of water index which are, at least partly, based on thresholds: the thresholds change largely with time and space; the determination of threshold is highly subjective and contains a lot of background information [20,52]. The biggest advantage of NDWI lies in that it is simple and can generate a water map in a very short time. The proposed DenseNet-based water identification method can extract water bodies from the GF-1 images with high accuracy, but it needs hours of training time. However, considering the improvement it has made in recognition accuracy, and once the network is trained, the time to use this network is comparable to NDWI.

So, this network is still a better tool compared to the water index method. Meanwhile, the comparison of the proposed method with other four neural networks shows that it is a more powerful tool for water body recognition.

There are more and more studies using the deep convolution neural network to classify remote sensing images [68]. Our results have approved that, for big remote sensing data like GF-1 images with high spatial and temporal resolutions, the deep learning method can be used to extract water bodies with accurate results e fficiently. It can be seen from water area changes in the recent years that the derived water areas from the deep learning method can well reflect the local drought or flooding conditions. Therefore, using the proposed method, the changes of water bodies, such as river and lakes, and wetland as well, can be timely and e ffectively monitored [69].

The algorithm proposed in this study shows a certain deviation in distinguishing water bodies and clouds, which can be further improved by modifying the model structure and parameters. Also, the cloud area can be removed using image preprocessing to avoid such misjudgment. In this study, we did not preprocess to remove the cloud, such that the original information of the input images are kept. In addition, we use the cloud as one of the indicators to evaluate the e ffect of water recognition algorithm. When a flooding event occurs, the cloud is always a barrier for water body monitoring with optical remote sensing image. In such a case, the identification results can be improved by removing clouds first or adding samples containing clouds. For cloud removal, it is a solution to integrate optical with microwave remote sensing images. The deficiency of optical remote sensing can be made up by combining with the advantage of microwave remote sensing to penetrate clouds and fog [70,71].
