Visual Grounding (VG) is a core multimodal task that localizes image targets via natural language descriptions, and it is crucial for Unmanned Aerial Vehicle (UAV) applications. However, existing remote sensing (RS) VG datasets primarily rely on rule-driven explicit descriptions, which are inconsistent with real-world demands for interpreting implicit descriptions based on context, common sense, or domain knowledge. In addition, the cross-lingual robustness of Large Vision-Language Models (LVLMs) in implicit VG remains to be thoroughly investigated. This study evaluates the cross-lingual performance of Qwen2.5-VL-7B and InternVL3.5-8B across nine languages, incorporating analyses of text length dynamics, visual attention, and language structural effects. The results demonstrate that Qwen2.5-VL-7B exhibits outstanding performance in maintaining consistent task paradigm alignment (explicit VG outperforms implicit VG) and balanced text output, which benefits from the syntactic stability and low cognitive load of East Asian languages. In contrast, InternVL3.5-8B presents task paradigm misalignment, uncontrolled text expansion, and generative hallucinations. Furthermore, differences in language structures: East Asian languages depend on word order for semantic expression, whereas Western languages feature complex lexical morphology, significantly affect attention allocation and VG accuracy. This study provides key insights for optimizing cross-lingual vision-language alignment of LVLMs and advancing practical multimodal applications in UAV scenarios.