With the rapid development of Composed Image Retrieval （CIR） technology， its application is explored in the field of Remote Sensing （RS） image retrieval to improve the accuracy of retrieving target images from RS image libraries. However， the existing algorithms fail to bridge the semantic gap between image and text modalities effectively and are limited by the lack of high-quality annotated datasets for training CIR models in the RS field. To address these challenges， a zero-shot composed image retrieval for Remote Sensing images based on Image Edit Proxy （IEP4RS） algorithm was proposed to use image editing techniques to generate proxy images aligned with the query image and text description， thereby enhancing the query representation. In IEP4RS algorithm， image editing instructions were generated based on the query image and the text description of the target image， these instructions， along with the query image， were then fed into an image editing model to produce a proxy image， and composite query image features were constructed by fusing the features of the proxy image and the original query image. In this algorithm， the semantic gap between image and text modalities was bridged effectively through direct image feature matching， and a zero-shot learning paradigm was used to avoid the dependency on annotated datasets required by traditional algorithms. Experimental results on the public RS CIR benchmark dataset， PatternCom， demonstrate that IEP4RS algorithm improves retrieval performance significantly. With RemoteCLIP as the feature encoder， compared to the baseline algorithm WEICOM （WEIghted COMposed image retrieval）， IEP4RS algorithm has the mean Average Precision （mAP） improved by 9.74 percentage points. Furthermore， IEP4RS algorithm outperforms the mainstream zero-shot composed image retrieval （ZS-CIR） algorithms， achieving improvements of 11.79， 7.81， and 3.99 percentage points in mAP compared to Pic2Word （mapping Pictures to Words for zero-shot composed image retrieval）， SEARLE （zero-Shot composEd imAge Retrieval with textuaL invErsion）， and FREEDOM （composed image retrieval for training-FREE DOMain conversion）， respectively.