## 5 Discussion  
In this study, we choose cloud-free images for the experiment. In practice, clouds and their shadows have been a critical issue for flood mapping using multispectral images, especially the as flood is usually accompanied by rainy and cloudy weather. This is because the visible and near infrared spectra cannot penetrate the cloud, so the image quality is frequently affected during flood periods. We put forward some analysis and speculation regarding how this may influence the result of the proposed method. First, the confidence value calculated in the step of STCL will certainly be affected by the clouds. As the STCL method models the correlation of image density and distance, and the cloud has different characteristics with those of the land or the water, the relative relationship will change a lot with the interference of clouds. According to the description in Section 3.1, if there are some clouds present nearby, the confidence value will decrease. However, since we only extract pixels with high confidence values for training the classifier, its impact on the final flood mapping may be limited. On the other hand, the GF-4 is a geostationary satellite. When a disaster happens, it can take images of the same region with a very high time resolution if needed. Through combining the common region of multiple images over a short time, data hidden by clouds and shadows may be recovered. Anyhow, it is a deficiency in our work that no experiment using cloudy data has been carried out. More explorations concerning cloudy data will be made in a future work. In the proposed process for flood mapping, the MOD44W product plays a role in separating permanent pixels into permanent water pixels and permanent non-water pixels. With the introduction of this water mask, some issues are introduced as well. One is that this product is obtained based on the MODIS data from 2000 to 2002, while our case studies are in 2013 and 2016. There was over a decade between the MOD44W product and the experimental data. The outlines of the water are very likely to have altered. Besides the difference in time, the huge gap between the spatial resolutions of the water mask and the experimental data could also lead to problems. The spatial resolution of the MOD44W product is 250 m, which is much lower than that of the HJ-1A/B CCD and GF-4 PMS data. Many jagged edges can be found in the resampled result of the MOD44W product. Moreover, some small water surfaces are omitted because of the low spatial resolution. Both of these issues will bring about errors in labelling the permanent pixels. Nevertheless, as the labelled permanent pixels serve as the training set for the Modest AdaBoost classifier, not the final detailed classification result, we think a certain number of errors can be tolerated. Figures 5c and 9c also show that, in the experimental areas, from the visual effect, the MOD44W product is able to provide a general outline of the water before the flood comes. From another perspective, for the areas near the edges of the rivers and lakes, where most of the differences between the MOD44W product and the study data exist, the confidence value is generally low because of the changes induced by the flood. Therefore, the pixels at these areas are  
Figure 12: Second test—commission, omission and overall accuracy in function of \(n\), the percentage of permanent pixels. less likely to be selected as permanent pixels, and their corresponding MOD4W labels would have little influence on the final result. According to the demand for automation and details in disaster assessment, this study aims to explore a novel solution for flood mapping that can achieve precise results with minimal human intervention. After two experiments on different regions and data, the proposed method shows better performance than other automatic methods. Several important reasons we infer are as follows. The first is the introduction of the machine learning classification method.