Remote Sensing Time Series Analysis: A Review of Data and Applications

Remote sensing time series research and applications are advancing rapidly in land, ocean, and atmosphere science, demonstrating emerging capabilities in space-based monitoring methodologies and diverse application prospects. This prompts a comprehensive review of remote sensing time series observations, time series data reconstruction, derived products, and the current progress, challenges, and future directions in their applications. The high-frequency new data, i.e., a constellation strategy, increasing computing power and advancing deep learning algorithms, are driving a paradigm shift from traditional point-in-time mapping to near-real-time monitoring tasks, and even to modeling integration of parameter inversion and prediction in land, water, and air science. Correspondingly, the 3 main projects, namely, the Global Climate Observing System, the United States Geological Survey/National Aeronautics and Space Administration (USGS/NASA) Landsat Science team, and the China Global Land Surface Satellite (GLASS) team, along with other time series-derived products, have found widespread applications in the research of Earth's radiation balance and human-land systems. They have also been utilized for tasks such as land use change detection, assessing coastal effects, ocean environment monitoring, and supporting carbon neutrality strategies. Moreover, the 3 critical challenges and future directions were highlighted including multimode time series data fusion, deep learning modeling for task-specific domain adaptation, and fine-scale remote sensing applications by using dense time series. This review distills historical and current developments spanning the last several decades, providing an insightful understanding into the advancements in remote sensing time series data and applications.