In the era of explainable AI, rapid data processing, analysis, and generation have become essential. Over the past few years, many approaches have been developed to process such heavy data and present it in an explainable manner, including in the field of remote sensing. One of such applications is remote sensing scene description. Many established workflows and models exist, but these models either fail to incorporate essential geospatial information or suffer from hallucination. We present a hybrid multimodal captioning methodology that tightly couples semantic segmentation outputs (via a LoRA-adapted Segment Anything Model) with a small, high-quality LLM- Mistral to produce descriptive, interpretable, and data-grounded scene captions. Rather than relying on direct image-to-text pipelines, our approach first extracts structured scene statistics (class proportions), spatial context (quadrant dominance and object localization), and color fingerprints (dominant colors per semantic class). These structured signals are converted into compact, factual prompts that the LLM consumes to generate coherent, informative, and verifiable captions. A comparison with the established Florence-2 model in terms of quantitative description demonstrates a significant improvement, with the Precision Vocabulary Index increasing from 0.077 to 0.232 due to the proposed workflow.