Thoroughly utilizing scale-aware context features to accurately segment entire salient regions remains a significant challenge in salient object detection (SOD) for optical remote sensing images (RSIs). In this letter, we propose a novel dynamic context coordination network (DCC-Net), which is capable of highlighting complete salient regions by exploiting feature coordination across different network levels. DCC-Net is composed of three components: a feature encoder, a bilinear purification (BP) module, and a dynamic context coordination (DCC) module. First, we adopt a standard feature encoder to extract multiscale features. Second, a BP module is proposed to establish a global correlation between horizontal and vertical directions and purify saliency representations. Finally, a DCC module is designed to facilitate context coordination from low to high resolutions by conditioning the convolution kernels dynamic on adjacent context. Comparison experiments are performed on the ORSSD, EORSSD, and ORSI-4199 datasets, demonstrating the superiority of our proposed method against other state-of-the-art methods and verifying the effectiveness of the DCC.