深度学习融合遥感大数据的陆地水文数据同化:进展与关键科学问题

Data Assimilation in Terrestrial Hydrology Based on Deep Learning Fusing Remote Sensing Big Data:Research Advances and Key Scientific Issues

以深度学习为核心的数据驱动方法逐步应用于地球科学领域,但这类方法在模型的可解释性和物理一致性等方面还存在挑战。在遥感大数据背景下,如何结合深度学习和数据同化方法,发展陆地水循环过程模拟与预报的新技术和新方法成为地球科学领域的重要研究方向。重点梳理了近年来深度学习在改善陆地水循环要素观测数据质量以及深度学习如何减少物理模型不确定性方面的最新进展,从观测、模型和系统集成3个方面凝练出深度学习融合遥感大数据的陆地水文数据同化研究的关键科学问题:(1)深度学习反演遥感产品时,如何增强样本的时空代表性?(2)如何发展数据同化框架下物理导引的深度学习新方法?(3)如何通过“数据—模型”双驱动提升陆地水循环的可预报性?开展相关研究和探索将有助于推动“数据—模型”混合建模方法在水文领域的深入应用,提高陆地水循环过程的模拟和预测能力。

Data-driven methods with deep learning as their core have been gradually applied in Earth science;however,challenges remain regarding the interpretability of models and physical consistency.With the background of remote sensing big data,combining deep learning and data assimilation methods to develop new techniques for the simulation and prediction of terrestrial water cycle processes has become an important research direction in Earth science.Τhe progress in deep learning in recent years combines improving the quality of observation data of terrestrial water cycle components and reducing the uncertainty of physical models.Furthermore,the key scientific issues regarding data assimilation in terrestrial hydrology based on deep learning fusing remote sensing big data are classified according to the observations,physical models,and system integration:①How can the temporal and spatial representativeness of samples be enhanced when deep learning inverts remote sensing products?②How can a new physics-guided deep learning method be developed within the framework of data assimilation?③How can the predictability of the terrestrial water cycle be improved through the“data-model”dual drive?Relevant research and exploration should help promote the in-depth application of the“data-model”hybrid modeling method in the field of hydrology and improve the simulation and prediction capacity of the terrestrial water cycle process.