Earth observation (EO) satellite missions have been providing detailed images about the state of the Earth and its land cover for over 50 years. Long term missions, such as NASA's Landsat, Terra, and Aqua satellites, and more recently, the 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 satellite image time series (SITS) are complex, incorporating information from the temporal, spatial, and spectral dimensions. Therefore, deep learning methods are often deployed as they can analyze these complex relationships. This review presents a summary of the state-of-the-art methods of modelling environmental, agricultural, and other Earth observation variables from SITS data using deep learning methods. We aim to provide a resource for remote sensing experts interested in using deep learning techniques to enhance Earth observation models with temporal information.

翻译：地球观测（EO）卫星任务已持续50多年，提供关于地球状态及其土地覆盖的详细影像。长期任务如NASA的Landsat、Terra和Aqua卫星，以及近期ESA的Sentinel任务，每隔数天便会记录全球影像。尽管单幅影像仅提供时间点数据，但同一区域的重复影像——即卫星影像时间序列（SITS）——却能反映植被与土地利用的动态变化。这些SITS对植物物候等动态过程与季节性变化的建模至关重要，并在农业、森林、水资源、灾害管理、城市规划及矿业等诸多自然资源管理领域具有潜在价值。然而，卫星影像时间序列（SITS）本身具有高度复杂性，融合了时间、空间和光谱维度的信息。因此，常采用深度学习方法分析这些复杂关系。本综述系统总结了利用深度学习方法从SITS数据中建模环境、农业及其他地球观测变量的前沿技术，旨在为希望借助深度学习技术增强含时相信息地球观测模型的遥感领域专家提供参考。