## 1 Introduction  
Water is a fundamental natural element and an invaluable resource for various human endeavors [1, 2]. Contemporary shifts in global climate, coupled with intensified human activities, have precipitated pronounced spatial and temporal variations in water resources, commonly manifesting as reductions in both area and volume [3]. These problems pose substantial challenges to human livelihoods and agricultural production, underscoring the imperative for meticulous extraction and dynamic monitoring of surface water bodies. Remote sensing technology has become an effective tool for such monitoring, attributed to its accessibility, extensive coverage, frequent updating, and immediacy [4, 5]. In recent years, the growing water use conflict in the Yellow River Basin has seriously affected human welfare and livelihoods, highlighting the critical importance of surveilling the dynamic fluctuations of Surface Water Area (SWA) within this region. In recent years, deep learning has been widely used in tasks such as image classification [6], object detection [7], and semantic segmentation [8]. Water bodies extraction is a typical semantic segmentation task, where methods rooted in deep learning significantly improve the accuracy of traditional extraction techniques [9]. However, the application of deep learning in water bodies extraction faces several challenges, including diverse morphologies, complex scenarios (e.g., rivers, lakes, urban water bodies, and aquaculture), and variations in dimensions, with particular difficulty presented by small water bodies [8]. Convolutional Neural Network (CNN) have emerged as a prevalent strategy in water bodies extraction research, owing to their proficiency in semantic information of remote sensing images through convolutional operations processes [8; 10; 11; 12]. Despite their merits, CNNs are constrained by their receptive field dimensions, leading to a predominant focus on local features while ignoring the spatial correlation within a water body. This limitation often culminates in inaccurate edges of lake boundaries [10; 11]. Furthermore, the progressive enlargement of the receptive field amidst continuous down-sampling can result in the neglect of smaller water bodies' features, leading to fragmented extraction outcomes [12]. The advent of Fully Convolutional Network (FCN) marked a pivotal transition by introducing an encoder-decoder structure for image segmentation, facilitating a shift from image-level classification to a more fine-grained, pixel-level classification [13; 14]. [PERSON] et al. [15] used a FCN model for water bodies extraction from Very High Resolution (VHR) images, demonstrating the efficacy of pixel-level classification. However, this method suffers from the inaccurate extraction of small-size water bodies due to an inherent information attrition during the convolutional operations. To address the limitations of CNNs, particularly the loss of information through continuous down-sampling, researchers have integrated attention mechanisms and dilated convolution modules into existing methodologies. [PERSON] et al. [16] introduced a two-way channel attention mechanism and a depth-expanded residual structure forming MIE-Net network, aiming to improve lake segmentation accuracy. [PERSON] [17] demonstrated the superior performance of a DeeplabV3+ model with an Atrous Spatial Pyramid Pooling (ASPP) module in extracting various water body sizes compared to its counterpart without ASPP structure. [PERSON] et al. [18] proposed an Enhanced ASPP (EASPP) module, adept at maintaining the prominence of high-dimensional features and improving the recognition of small-size water bodies. However, the primary focus remains on expanding the receptive field, rather than truly encoding contextual information. This limitation tends to overshadow the intricate local traits of small-sized water bodies, leading to inconsistencies in the extraction outcomes for rivers and lakes [19].