Zero-shot visual recognition with vision-language models (VLMs) has shown strong generalization to unseen categories in natural-image benchmarks, yet its effectiveness in remote-sensing (RS) imagery remains less explored. In this paper, we investigate whether meta-prompting with large language models (LLMs) can improve zero-shot scene classification in RS by automatically generating semantically rich class descriptions. Building on the Meta-Prompting for Visual Recognition (MPVR) framework, we evaluate three open-source LLMs, Mixtral-8×7B, Qwen 2.5 7B, and LLaMA 3.1 8B, as prompt generators across five RS benchmark datasets. The resulting descriptions are encoded with several VLMs, including CLIP, MetaCLIP, RemoteCLIP, and CLIP-LAION-RS, and compared against generic single-template and handcrafted domain-specific prompting baselines. Our results show that LLM-generated prompts are competitive with, and in several cases improve upon, manually designed templates, while revealing that the gains depend on both the dataset and the visual backbone. Overall, the study highlights the potential of open-source LLMs as scalable prompt generators for zero-shot remote-sensing recognition and provides insight into the transferability of meta-prompting beyond natural-image domains.