基础模型（FMs）已将机器学习范式从孤立的任务专用模型开发，转向基于广泛数据预训练、并适配多种下游任务的通用模型。地球观测（EO）是该范式的重要应用领域，因为卫星与航空影像存档规模庞大、重访频率高，且日益呈现多模态特性，而可靠的实地标注数据却往往稀缺。遥感基础模型（RSFMs）若未经领域特定适配，则无法被可靠或最优地迁移应用。这是因为EO数据受测量物理规律及运行决策约束所支配。本章综述了源于这些领域特有约束的设计原则：首先界定遥感（RS）中的FMs范式，继而系统梳理当前模型格局、预训练目标、架构设计、下游适配方法及可信性要求。本章还引入近期基准测试证据，表明尚无单一地理空间基础模型在所有场景下均表现最优，且评估标准不一致仍是阻碍公平比较与可靠部署的主要问题。此外，通过两个简短的环境监测案例——面向有害藻华预测的物理信息驱动光谱目标掩码（physics-informed spectral targeted masking）与面向自适应环境监测站点选择的强化学习——阐释FMs领域引导原则的实际应用。本章主张：下一代RSFMs的评估不应仅依赖基准准确率，还应涵盖模态感知迁移能力及物理上合理表征能力，以支撑可信的地球观测决策。

Foundation models (FMs) have transformed machine learning from isolated task-specific model development toward general-purpose models pretrained on broad data and adapted to multiple downstream tasks. Earth observation (EO) is an important domain for this paradigm because satellite and airborne archives are large, high-revisit, and increasingly multimodal, while reliable field labels are often sparse. Remote sensing foundation models (RSFMs) cannot be transferred reliably/optimally without domain-specific adaptation. This is because EO data are governed by measurement physics and operational decision constraints. This chapter reviews the design principles arising from these domain-specific constraints. It first defines the FMs paradigm in remote sensing (RS), then synthesizes the current model landscape, pretraining objectives, architecture designs, downstream adaptation and trustworthiness requirements. The chapter also incorporates recent benchmark evidence showing that no single geospatial foundation model is universally best and that inconsistent evaluation remains a major issue to fair comparison and reliable deployment. In addition, two brief environmental monitoring case studies; physics-informed spectral targeted masking for harmful algal bloom prediction and reinforcement learning for adaptive environmental monitoring station selection to illustrate the FMs domain-guided principles in practice. This chapter posits that next-generation RSFMs should be evaluated not only by benchmark accuracy, but also by modality-aware transfer and physically plausible representations for trustworthy EO decisions.