In order to solve the problems of low water-body extraction accuracy, poor multi-scale feature capture ability, high model complexity of Convolutional Neural Network (CNN) in complex background image, a Receptive Field Attention LinkNet (FRA-LinkNet) is proposed. FRA-LinkNet is a high-resolution optical remote sensing image water-body extraction model, which is a combination of Receptive Field Block (RFB) and Channel Attention Block (CAB) based on LinkNet. Firstly, RFB is used to obtain high-level water-body semantic information and multi-scale feature map; Secondly, CAB is added to weighted aggregate encoding and decoding features, to suppress background features and enhance water-body semantics. Compared with state-of-the-art CNN model, the proposed RFA-LinkNet has high performance, robustness, and precision water-body extraction.