地理空间基础模型（GFMs）通常缺乏对高光谱成像（HSI）的原生支持，原因在于高维光谱数据的复杂性与巨大体量。本研究探讨了TerraMind这一多模态地理空间基础模型在未进行HSI特定预训练的情况下，对HSI下游任务的适应能力。为此，我们实施并比较了两种通道适配策略：简单波段选择与基于物理的光谱响应函数（SRF）分组。总体结果表明，具备原生HSI数据支持的深度学习模型具有普遍优势。实验还证明，TerraMind可通过波段选择实现对HSI下游任务的适应，尽管性能有所下降。因此，本研究的发现为HSI集成建立了关键基线，强调了未来多模态模型架构中引入原生光谱标记化的必要性。

Geospatial Foundation Models (GFMs) typically lack native support for Hyperspectral Imaging (HSI) due to the complexity and sheer size of high-dimensional spectral data. This study investigates the adaptability of TerraMind, a multimodal GFM, to address HSI downstream tasks \emph{without} HSI-specific pretraining. Therefore, we implement and compare two channel adaptation strategies: Naive Band Selection and physics-aware Spectral Response Function (SRF) grouping. Overall, our results indicate a general superiority of deep learning models with native support of HSI data. Our experiments also demonstrate the ability of TerraMind to adapt to HSI downstream tasks through band selection with moderate performance decline. Therefore, the findings of this research establish a critical baseline for HSI integration, motivating the need for native spectral tokenization in future multimodal model architectures.