Earth Observation (EO) data analysis has been significantly revolutionized by deep learning (DL), with applications typically limited to grid-like data structures. Graph Neural Networks (GNNs) emerge as an important innovation, propelling DL into the non-Euclidean domain. Naturally, GNNs can effectively tackle the challenges posed by diverse modalities, multiple sensors, and the heterogeneous nature of EO data. To introduce GNNs in the related domains, our review begins by offering fundamental knowledge on GNNs. Then, we summarize the generic problems in EO, to which GNNs can offer potential solutions. Following this, we explore a broad spectrum of GNNs' applications to scientific problems in Earth systems, covering areas such as weather and climate analysis, disaster management, air quality monitoring, agriculture, land cover classification, hydrological process modeling, and urban modeling. The rationale behind adopting GNNs in these fields is explained, alongside methodologies for organizing graphs and designing favorable architectures for various tasks. Furthermore, we highlight methodological challenges of implementing GNNs in these domains and possible solutions that could guide future research. While acknowledging that GNNs are not a universal solution, we conclude the paper by comparing them with other popular architectures like transformers and analyzing their potential synergies.

翻译：深度学习（DL）已显著革新了地球观测（EO）数据分析，但其应用通常局限于栅格状数据结构。图神经网络（GNNs）作为一种重要创新出现，将深度学习推进至非欧几里得领域。自然地，GNNs 能够有效应对地球观测数据多模态、多传感器及异构性带来的挑战。为在相关领域引入 GNNs，本综述首先提供关于 GNNs 的基础知识。接着，我们总结了地球观测中 GNNs 可提供潜在解决方案的通用问题。随后，我们探讨了 GNNs 在地球系统科学问题中的广泛应用，涵盖天气与气候分析、灾害管理、空气质量监测、农业、土地覆盖分类、水文过程建模及城市建模等领域。文中阐释了在这些领域采用 GNNs 的理论依据，以及为不同任务组织图结构和设计适宜架构的方法。此外，我们强调了在这些领域实施 GNNs 时面临的方法学挑战及可能指导未来研究的解决方案。在承认 GNNs 并非万能解决方案的同时，我们通过将其与 Transformer 等其他流行架构进行比较并分析其潜在协同效应来结束本文。