Remote-sensing　image-text　(RSIT)　retrieval　involves　the　use　of　either　textual　descriptions　or　remote-sensing　images　(RSI)　as　queries　to　retrieve　relevant　RSIs　or　corresponding　text　descriptions.　Many　traditional　cross-modal　RSIT　retrieval　methods　tend　to　overlook　the　importance　of　capturing　salient　information　and　establishing　the　prior　similarity　between　RSIs　and　texts,　leading　to　a　decline　in　cross-modal　retrieval　performance.　In　this　article,　we　address　these　challenges　by　introducing　a　novel　approach　known　as　multiscale　salient　image-guided　text　alignment　(MSITA).　This　approach　is　designed　to　learn　salient　information　by　aligning　text　with　images　for　effective　cross-modal　RSIT　retrieval.　The　MSITA　approach　first　incorporates　a　multiscale　fusion　module　and　a　salient　learning　module　to　facilitate　the　extraction　of　salient　information.　In　addition,　it　introduces　an　image-guided　text　alignment　(IGTA)　mechanism　that　uses　image　information　to　guide　the　alignment　of　texts,　enabling　the　effective　capture　of　fine-grained　correspondences　between　RSI　regions　and　textual　descriptions.　In　addition　to　these　components,　a　novel　loss　function　is　devised　to　enhance　the　similarity　across　different　modalities　and　reinforce　the　prior　similarity　between　RSIs　and　texts.　Extensive　experiments　conducted　on　four　widely　adopted　RSIT　datasets　affirm　that　the　MSITA　approach　significantly　enhances　cross-modal　RSIT　retrieval　performance　in　comparison　to　other　state-of-the-art　methods.