Remote　sensing　image-text　retrieval　(RSITR)　is　a　cross-modal　task　that　integrates　visual　and　textual　information,　attracting　significant　attention　in　remote　sensing　research.　Remote　sensing　images　typically　contain　complex　scenes　with　abundant　details,　presenting　significant　challenges　for　accurate　semantic　alignment　between　images　and　texts.　Despite　advances　in　the　field,　achieving　precise　alignment　in　such　intricate　contexts　remains　a　major　hurdle.　To　address　this　challenge,　this　article　introduces　a　novel　context-aware　local-global　semantic　alignment　(CLGSA)　method.　The　proposed　method　consists　of　two　key　modules:　the　local　key　feature　alignment　(LKFA)　module　and　the　cross-sample　global　semantic　alignment　(CGSA)　module.　The　LKFA　module　incorporates　a　local　image　masking　and　reconstruction　task　to　improve　the　alignment　between　image　and　text　features.　Specifically,　this　module　masks　certain　regions　of　the　image　and　uses　text　context　information　to　guide　the　reconstruction　of　the　masked　areas,　enhancing　the　alignment　of　local　semantics　and　ensuring　more　accurate　retrieval　of　region-specific　content.　The　CGSA　module　employs　a　hard　sample　triplet　loss　to　improve　global　semantic　consistency.　By　prioritizing　difficult　samples　during　training,　this　module　refines　feature　space　distributions,　helping　the　model　better　capture　global　semantics　across　the　entire　image-text　pair.　A　series　of　extensive　experiments　demonstrates　the　effectiveness　of　the　proposed　method.　The　method　achieves　an　mR　score　of　32.07%　on　the　RSICD　dataset　and　46.63%　on　the　RSITMD　dataset,　outperforming　baseline　methods　and　confirming　the　robustness　and　accuracy　of　the　approach.