## 1 Introduction
The classic threshold methods used in flood coverage extraction from radar data include the Otsu method (maximizing inter-class variance) [6], the entropy threshold method [34; 35], and the bimodal histogram method [36]. Many scholars have conducted research based on threshold methods. [PERSON] et al. investigated the performance of fifteen automatic thresholding methods and summarized the advantages and disadvantages of each method and the scope of application [37]. [PERSON] et al. presented a water-body detection framework with an automatic threshold determination procedure [38]. [PERSON] et al. proposed a local thresholding method for water delineation [39]. However, the method required a long time for processing complex images [40]. Moreover, accurate thresholds are difficult to determine because test areas are widely variable. The accuracy of the threshold method depends on the size of the water area in the image. If the grayscale histogram is unimodal, the threshold is difficult to detect [41]. On the other hand, machine learning methods have achieved accurate extraction results and improved handling of the influence of noise [42]. [PERSON] et al. proposed a new flood mapping technique based on bagging as a meta-classifier and K-nearest neighbor (KNN), using Sentinel-1 for flood mapping [43]. [PERSON] et al. combined the gray-level co-occurrence matrix (GLCM) and the support vector machine (SVM) to extract the target area of the water and, based on the gray information and the spatial distribution information of the pixels, calculated GLCM texture features in four spatial directions [44]. However, the parameters greatly affect the classification results, so much calculation and debugging are required. The training dataset is difficult to obtain, and the classifier training is time-consuming [45; 46]. Random forests (RF) is a supervised machine learning algorithm that has recently started to gain prominence in water resources applications. [PERSON] et al. reviewed RF applications in water resources, highlighting the potential of the original algorithm and its variants, and assessed the degree of RF exploitation in a diverse range of applications [47]. Considering the advantages and disadvantages of the two mainstream methods, the current research automates the threshold selection and improves the accuracy and speed of extraction. For the benefit of retrieval, our investigation of method comparison is concisely summarized in Table 1.  
The contributions of this work are summarized as follows. 1. A fully automated thresholding method was developed for extracting flood ranges with higher temporal resolution. Existing global water monitoring products are mostly generated by optical remote sensors. There are also annual composite data because of the influence of cloud cover. The inundation area during the flood period can be ascertained by our method, which makes up for the lack of temporal resolution of optical products. 2. The proposed method is tested over reservoir and watershed study sites in China. The area of water and non-water can be extracted quickly and accurately with our fully automated approach. The classification results of the two types of sites also show that our method is better than existing thresholding methods (e.g., Otsu [6]). This paper is organized into six sections. Section 2 introduces the study area and data used in the paper. Section 3 describes the main process of the proposed approach in detail. Experimental results at two sites are presented in Section 4, demonstrating the feasibility and superiority of the proposed approach. Section 5 discusses the advantages and limitations of the method and the future development. Section 6 draws a conclusion regarding our work.