The Convolutional Neural Network (CNN) has unsatisfactory performance in water body extraction from high-resolution optical remote sensing images with complex background,which is low in accuracy,unable to capture multi-scale features,and complex in model structure.Here,we propose an RFA-LinkNet (Receptive Field Attention LinkNet) approach combining Receptive Field Block (RFB) and Channel Attention Block (CAB),from which the high-level water body semantic information and multi-scale feature map can be obtained by RFB,then the CAB is used to realize the weighted fusion of encoding and decoding features,to suppress background features as well as enhance water body semantics.Compared with state-of-the-art CNN models,the proposed RFA-LinkNet can extract water body information from high-resolution optical remote sensing images more efficiently and robustly with high precision.