Earth observation (EO) satellite missions have been providing detailed images about the state of Earth and its land cover for over 50 years. Long-term missions, such as those of NASA’s Landsat, Terra, and Aqua satellites, and more recently, the European Space Agency’s (ESA’s) Sentinel missions, record images of the entire world every few days. Although single images provide point-in-time data, repeated images of the same area, or satellite image time series (SITS), provide information about the changing state of vegetation and land use. These SITS are useful for modeling dynamic processes and seasonal changes, such as plant phenology. They have potential benefits for many aspects of land and natural resource management, including applications in agricultural, forest, water, and disaster management; urban planning; and mining. However, the resulting SITS are complex, incorporating information from the temporal, spatial, and spectral dimensions. Therefore, deep learning (DL) methods are often deployed, as they can analyze these complex relationships. This review article presents a summary of the state-of-the-art methods of modeling environmental, agricultural, and other EO variables from SITS data using DL methods. We aim to provide a resource for remote sensing experts interested in using DL techniques to enhance EO models with temporal information.

中文翻译：

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卫星图像时间序列分析的深度学习：综述


50 多年来，地球观测 (EO) 卫星任务一直在提供有关地球状况及其土地覆盖的详细图像。长期任务，例如 NASA 的 Landsat、Terra 和 Aqua 卫星的任务，以及最近欧洲航天局 (ESA) 的哨兵任务，每隔几天就会记录一次整个世界的图像。虽然单幅图像提供时间点数据，但同一区域的重复图像或卫星图像时间序列 (SITS) 可以提供有关植被和土地利用状态变化的信息。这些 SITS 对于动态过程和季节变化建模非常有用，例如植物物候学。它们对土地和自然资源管理的许多方面都有潜在的好处，包括在农业、森林、水和灾害管理方面的应用；城市规划；和采矿。然而，由此产生的 SITS 很复杂，包含来自时间、空间和频谱维度的信息。因此，经常部署深度学习（DL）方法，因为它们可以分析这些复杂的关系。这篇综述文章总结了使用深度学习方法根据 SITS 数据对环境、农业和其他 EO 变量进行建模的最先进方法。我们的目标是为有兴趣使用深度学习技术通过时间信息增强EO模型的遥感专家提供资源。