摘要：基础模型（FMs）已将机器学习从孤立的、面向特定任务的模型开发转变为在广泛数据上预训练并可适应多种下游任务的通用模型。地球观测（EO）是该范式的重要领域，因为卫星和航空档案数量庞大、重访率高且日益多模态，而可靠的实地标签通常稀疏。遥感基础模型（RSFMs）在没有领域特定适应的情况下无法可靠/最优地迁移。这是因为 EO 数据受测量物理和操作决策约束的支配。本章回顾了由这些领域特定约束产生的设计原则。它首先定义了遥感（RS）中的基础模型范式，然后综合了当前的模型格局、预训练目标、架构设计、下游适应和可信性要求。本章还纳入了最近的基准证据，表明没有单一的地理空间基础模型是普遍最优的，而且评估不一致仍然是公平比较和可靠部署的主要问题。此外，两个简短的环境监测案例研究：用于有害藻华预测的物理信息光谱定向掩码，以及用于自适应环境监测站选择的强化学习，以说明基础模型领域指导原则的实践。本章提出，下一代遥感基础模型不仅应通过基准准确性来评估，还应通过模态感知迁移和物理上合理的表征来评估，以实现可信的地球观测决策。

Abstract: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.