The　cross-modal　drone　image-text　(DIT)　retrieval　task　involves　using　either　text　or　drone　images　as　queries　to　retrieve　relevant　drone　images　or　corresponding　text.　The　primary　challenge　stems　from　the　diverse　and　intricate　nature　of　drone　images,　making　effective　alignment　between　image　and　text　challenging.　In　response,　we　propose　an　innovative　approach　called　visual　contextual　semantic　reasoning　(VCSR),　aimed　at　precisely　aligning　information　across　different　modalities.　VCSR　employs　textual　cues　to　guide　rich　semantic　reasoning　within　the　visual　context,　reducing　redundancy　in　visual　information.　Furthermore,　the　method　captures　drone　image　information　relevant　to　the　text,　revealing　subtle　correspondences　between　drone　image　regions　and　textual　content.　To　enhance　visual　semantic　learning,　context　region　learning　(CRL)　term　and　consistency　semantic　alignment　(CSA)　terms　are　introduced　for　stronger　guidance,　further　intensifying　the　cross-modal　interaction　between　textual　and　visual　data,　resulting　in　more　robust　feature　representation.　Extensive　experiments　conducted　on　two　self-constructed　DIT　datasets　demonstrate　that　VCSR　outperforms　alternative　methods　in　terms　of　DIT　retrieval　performance.　The　codes　are　accessible　at　https://github.com/huangjh98/VCSR.