地球观测（EO）对于监测环境变化、应对灾害以及管理自然资源至关重要。在此背景下，基础模型有助于遥感图像分析，以准确且高效地提取相关地理信息。然而，随着这些模型规模的增大，微调面临日益严峻的计算资源与成本挑战，限制了其可及性与可扩展性。此外，全量微调可能导致预训练特征遗忘，甚至降低模型泛化能力。为解决这一问题，参数高效微调（PEFT）技术提供了一种有前景的解决方案。本文针对多种基础模型架构与PEFT技术，在五个不同的地球观测数据集上进行了广泛实验，评估其有效性。结果提供了全面的对比分析，揭示了PEFT方法在何种情境下以及如何支持预训练地理空间模型的适应。我们证明，PEFT技术在性能上可达到甚至超越全量微调，并提升模型对未见地理区域的泛化能力，同时显著降低训练时间与内存需求。额外实验探讨了架构选择（如解码器类型或元数据使用）的影响，建议采用UNet解码器并避免使用元数据作为最优配置。我们已将所有评估的基础模型与技术集成至开源工具包TerraTorch，以支持快速、可扩展且低成本的模型适配。

Earth observation (EO) is crucial for monitoring environmental changes, responding to disasters, and managing natural resources. In this context, foundation models facilitate remote sensing image analysis to retrieve relevant geoinformation accurately and efficiently. However, as these models grow in size, fine-tuning becomes increasingly challenging due to the associated computational resources and costs, limiting their accessibility and scalability. Furthermore, full fine-tuning can lead to forgetting pre-trained features and even degrade model generalization. To address this, Parameter-Efficient Fine-Tuning (PEFT) techniques offer a promising solution. In this paper, we conduct extensive experiments with various foundation model architectures and PEFT techniques to evaluate their effectiveness on five different EO datasets. Our results provide a comprehensive comparison, offering insights into when and how PEFT methods support the adaptation of pre-trained geospatial models. We demonstrate that PEFT techniques match or even exceed full fine-tuning performance and enhance model generalisation to unseen geographic regions, while reducing training time and memory requirements. Additional experiments investigate the effect of architecture choices such as the decoder type or the use of metadata, suggesting UNet decoders and fine-tuning without metadata as the recommended configuration. We have integrated all evaluated foundation models and techniques into the open-source package TerraTorch to support quick, scalable, and cost-effective model adaptation.