气候变化领域集成服务门户(CCPortal): A Fast Generative Adversarial Network Combined With Transformer for Downscaling GRACE Terrestrial Water Storage Data in Southwestern China

CCPortal

DOI	10.1109/TGRS.2024.3349548
	A Fast Generative Adversarial Network Combined With Transformer for Downscaling GRACE Terrestrial Water Storage Data in Southwestern China
	Gu, Songwei; Zhou, Yun; Zhao, Long; Ma, Mingguo; She, Xiaojun; Zhang, Lifu; Li, Yao
发表日期	2024
ISSN	0196-2892
EISSN	1558-0644
起始页码	62
卷号	62
英文摘要	The Gravity Recovery and Climate Experiment (GRACE) satellite provides an unprecedented tool for monitoring large-scale terrestrial water storage (TWS) changes. Yet, its coarse resolution restricts its effectiveness in areas with complex hydrogeological environments, such as southwestern China. To address this limitation, we propose a novel method to improve the spatial resolution of GRACE observations. Our approach leverages a deep learning downscaling model that integrates generative adversarial networks (GANs) and transformer attention mechanisms to derive the spatial patterns of TWS variations. The model incorporates the estimated total water storage changes from GRACE and some hydrological variables-including the digital elevation model (DEM), soil moisture, evapotranspiration, temperature, and precipitation-to enhance the resolution and accuracy of GRACE data. By implementing this method, we successfully increased the spatial resolution of GRACE observations from 0.25 degrees to 0.05 degrees. The advanced neural network downscaling model can accurately characterize local water storage variations, with Nash-Sutcliffe efficiency (NSE) values ranging from 0.58 to 0.92. Moreover, this model not only significantly increases the spatial resolution but also maintains the spatial distribution, offering valuable insights for regional water resources management and fostering small-scale hydrological research. The results have profound implications for sustainable water resources management and climate change assessment.
英文关键词	Deep learning; downscaling; generative adversarial networks (GANs); Gravity Recovery and Climate Experiment (GRACE); terrestrial water storage (TWS)
语种	英语
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001173248900027
来源期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/297461
作者单位	Southwest University - China; Southwest University - China; Chinese Academy of Sciences; Aerospace Information Research Institute, CAS
推荐引用方式 GB/T 7714	Gu, Songwei,Zhou, Yun,Zhao, Long,et al. A Fast Generative Adversarial Network Combined With Transformer for Downscaling GRACE Terrestrial Water Storage Data in Southwestern China[J],2024,62.
APA	Gu, Songwei.,Zhou, Yun.,Zhao, Long.,Ma, Mingguo.,She, Xiaojun.,...&Li, Yao.(2024).A Fast Generative Adversarial Network Combined With Transformer for Downscaling GRACE Terrestrial Water Storage Data in Southwestern China.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,62.
MLA	Gu, Songwei,et al."A Fast Generative Adversarial Network Combined With Transformer for Downscaling GRACE Terrestrial Water Storage Data in Southwestern China".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62(2024).