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

doi:10.11867/j.issn.1001-8166.2023.022

地球科学进展 ›› 2023, Vol. 38 ›› Issue (5): 441 -452. doi: 10.11867/j.issn.1001-8166.2023.022

综述与评述下一篇

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

黄春林 ¹(

), 侯金亮 ¹, 李维德 ², 顾娟 ³, 张莹 ¹, 韩伟孝 ¹, 王维真 ¹, 温小虎 ⁴, 朱高峰 ³

^1.中国科学院西北生态环境资源研究院，甘肃省遥感重点实验室，甘肃兰州 730000
^2.兰州大学数学与统计学院，甘肃兰州 730000
^3.兰州大学资源环境学院，甘肃兰州 730000
^4.中国科学院西北生态环境资源研究院，内陆河流域生态水文重点实验室，甘肃兰州 730000

收稿日期:2022-10-24 修回日期:2023-04-19 出版日期:2023-05-10
基金资助:
国家自然科学基金项目“深度学习融合遥感大数据的陆地水文数据同化理论、方法与集成技术”(42130113)

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

Chunlin HUANG ¹( ), Jinliang HOU ¹, Weide LI ², Juan GU ³, Ying ZHANG ¹, Weixiao HAN ¹, Weizhen WANG ¹, Xiaohu WEN ⁴, Gaofeng ZHU ³

^1.Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
^2.School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China
^3.College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
^4.Key Laboratory of Ecohydrology of Inland River Basin, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China

Received:2022-10-24 Revised:2023-04-19 Online:2023-05-10 Published:2023-05-10
About author:HUANG Chunlin (1979-), male, Qingtongxia City, Ningxia Hui Autonomous Region, Professor. Research areas include hydrological remote sensing, multi-source remote sensing data assimilation, SDGs monitoring and evaluation. E-mail: huangcl@lzb.ac.cn
Supported by:
the National Natural Science Foundation of China “Data assimilation of terrestrial hydrological in theory, method and integration technology based on deep learning fusing remote sensing big data”(42130113)

全文 ( 127 ) 下载PDF ( 1848 )

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。

关键词: 深度学习, 遥感大数据, 数据同化, 水循环

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: $①$ How can the temporal and spatial representativeness of samples be enhanced when deep learning inverts remote sensing products? $②$ How can a new physics-guided deep learning method be developed within the framework of data assimilation? $③$ How 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.

Key words: Deep learning, Remote sensing big data, Data assimilation, Water cycle

中图分类号:

P339

图1 机器学习/深度学习在地球系统科学中的10个应用方向（据参考文献［ 12 ］修改）
各应用方向按照物理模型的参与程度（横轴）和机器学习/深度学习代码易用、成熟程度（纵轴）组织

Fig. 1 Ten ways to apply machine/deep learning in the Earth and Space Sciences （modified after reference ［ 12 ］）
Each application direction is organized by the degree of involvement of physics-based models （horizontal scale） and the degree to which machine/deep learning codes are available and readily applicable （vertical scale）

图2 深度学习与陆面水文数据同化系统融合框架
DL1~DL6表示数据驱动模型分别用于陆面/水文过程模型参数优化、模型的模块/子模块代理、模型系统偏差校正、观测算子代理、观测误差估计、数据同化方法替代

Fig. 2 Coupling of deep learning and land data assimilation
DL1~DL6 refer to deep learning used for model parameter optimization， model process/sub-process agent， assimilation system deviation correction， observation operator modeling， observation error estimation and DA algorithm substitution， respectively

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