Remote sensing image-text retrieval has received increasing attention in recent years. Most existing methods rely on aligning global features between images and texts to perform retrieval， with a strong focus on feature extraction and fusion. However， they typically overlook the alignment between fine-grained object regions in images and semantic phrases in texts （i.e.， local alignment）. This limitation is particularly problematic in remote sensing， where intra-modality samples often exhibit high visual similarity， confusing global alignment. To address this， we propose GLAPA （Global and local alignment with phrase augmentation）， a novel framework that complements global alignment with fine-grained local alignment. Specifically， GLAPA extracts conceptual phrases from textual descriptions and dynamically aligns them with relevant image patches， enhancing the model’s ability to capture detailed cross-modal semantics. In addition， we incorporate masked modeling to strengthen intra-modal feature learning， further improving retrieval performance. Extensive experiments on the RSICD and RSITMD datasets—overing ablation studies， hyperparameter analysis， and visualization—demonstrate that GLAPA significantly outperforms state-of-the-art methods. Visualization results also confirm the effectiveness of our local alignment strategy.