## 1 Introduction  
With the advancement of remote sensing technology, it has become increasingly convenient to obtain high-resolution surface land images [1], providing ample data support for agricultural water conservancy surveys and the extraction of water body information. As a core technology in agricultural remote sensing, the identification of water bodies in remote sensing images is of crucial significance for applications such as agricultural production, farmland irrigation, and ecological protection [2]. Therefore, in view of the complex background of the rural environment [3], developing a reliable and highly generalizable water body segmentation method is crucial for the implementation of precision agriculture and the ecological protection of river basins [4]. In the field of water body segmentation of remote sensing images, the research methods mainly include threshold algorithms, machine learning algorithms, and deep learning algorithms [5]. Threshold algorithms such as the Normalized Difference Water Index (NDWI) [6], the Modified Normalized Difference Water Index (MNDWI) [7], and the Automated Water Extraction Index (AWEI) [8] achieve the extraction of targets by analyzing the response characteristics of water bodies in specific spectral intervals. However, these methods suffer from high data acquisition costs and heavy reliance on manual parameter tuning, limiting their effectiveness in modern agricultural remote sensing applications. To overcome the limitations of threshold segmentation methods, machine learning methods such as Support Vector Machine (SVM) [9], Maximum Likelihood Classification (MLC) [10], Decision Tree (DT) [11], and Random Forest (RF) [12] have been introduced into the task of water body extraction. These methods, by learning the decision boundaries between categories from labeled training samples, exhibit better segmentation accuracy than traditional index methods in specific datasets. However, their limited capacity to extract deep features makes them inadequate for complex scenarios, such as water scale variations and vegetation occlusion in agricultural watersheds [1; 3]. In addition to optical image-based methods, synthetic aperture radar (SAR) data has been widely used in water body segmentation research due to its weather-independent nature and the ability to highlight open water features through low backscatter signals [13]. SAR is particularly effective in flood mapping and large-scale hydrological applications. However, its application in small agricultural watersheds is limited. The spatial resolution of most SAR systems is insufficient to detect narrow streams and irrigation ditches. Additionally, specular reflections from smooth, non-water surfaces such as wet roads or bare soil may lead to misclassification [14]. These issues reduce the applicability of SAR-based segmentation methods in fine-scale agricultural scenarios. Deep learning has made significant progress in the field of remote sensing image analysis, providing effective solutions for complex tasks such as water body extraction. convolutional neural networks (CNNs) [15] are widely applied in semantic segmentation due to their powerful feature representation capabilities. Models such as the Fully Convolutional Network (FCN) [16], U-Net [17], Pyramid Scene Parsing Network (PSPNet) [18], and DeepLab [19; 20; 21] all adopt the encoder-decoder structure to preserve spatial details and improve the segmentation accuracy. Deep High-Resolution Representation Learning for Visual Recognition (HRNet) [22] further integrates the feature information of each layer by connecting branches with different resolutions in parallel. However, when these models are directly applied to the task of agricultural watersheds identification, due to the insufficient consideration of multi-scale semantic features and spatial correlations during the feature extraction process, the extraction accuracy is difficult to meet the practical application requirements of agricultural remote sensing monitoring.