干旱区生态环境知识资源中心(Arid): Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning

Arid

DOI	10.3390/rs13040584
	Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning
	Zhu, Linglong; Zhang, Yonghong; Wang, Jiangeng; Tian, Wei; Liu, Qi; Ma, Guangyi; Kan, Xi; Chu, Ya
通讯作者	Zhang, YH (corresponding author), Nanjing Univ Informat Sci & Technol, Jiangsu Collaborat Innovat Ctr Atmospher Environm, Nanjing 210044, Peoples R China. ; Zhang, YH (corresponding author), Nanjing Univ Informat Sci & Technol, Sch Automat, Nanjing 210044, Peoples R China.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:4
英文摘要	Accurate high spatial resolution snow depth mapping in arid and semi-arid regions is of great importance for snow disaster assessment and hydrological modeling. However, due to the complex topography and low spatial-resolution microwave remote-sensing data, the existing snow depth datasets have large errors and uncertainty, and actual spatiotemporal heterogeneity of snow depth cannot be effectively detected. This paper proposed a deep learning approach based on downscaling snow depth retrieval by fusion of satellite remote-sensing data with multiple spatial scales and diverse characteristics. The (Fengyun-3 Microwave Radiation Imager) FY-3 MWRI data were downscaled to 500 m resolution to match Moderate-resolution Imaging Spectroradiometer (MODIS) snow cover, meteorological and geographic data. A deep neural network was constructed to capture detailed spectral and radiation signals and trained to retrieve the higher spatial resolution snow depth from the aforementioned input data and ground observation. Verified by in situ measurements, downscaled snow depth has the lowest root mean square error (RMSE) and mean absolute error (MAE) (8.16 cm, 4.73 cm respectively) among Environmental and Ecological Science Data Center for West China Snow Depth (WESTDC_SD, 9.38 cm and 5.36 cm), the Microwave Radiation Imager (MWRI) Ascend Snow Depth (MWRI_A_SD, 9.45 cm and 5.49 cm) and MWRI Descend Snow Depth (MWRI_D_SD, 10.55 cm and 6.13 cm) in the study area. Meanwhile, downscaled snow depth could provide more detailed information in spatial distribution, which has been used to analyze the decrease of retrieval accuracy by various topography factors.
英文关键词	downscaling deep learning snow depth MWRI data fusion
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000624443700001
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
来源机构	南京信息工程大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/351484
作者单位	[Zhu, Linglong; Zhang, Yonghong] Nanjing Univ Informat Sci & Technol, Jiangsu Collaborat Innovat Ctr Atmospher Environm, Nanjing 210044, Peoples R China; [Zhu, Linglong; Ma, Guangyi] Nanjing Univ Informat Sci & Technol, Sch Elect & Informat Engn, Nanjing 210044, Peoples R China; [Zhang, Yonghong] Nanjing Univ Informat Sci & Technol, Sch Automat, Nanjing 210044, Peoples R China; [Wang, Jiangeng] Nanjing Univ Informat Sci & Technol, Sch Atmospher Phys, Nanjing 210044, Peoples R China; [Tian, Wei; Liu, Qi] Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China; [Kan, Xi] Nanjing Univ Informat Sci & Technol, Binjiang Coll, Wuxi 214105, Jiangsu, Peoples R China; [Chu, Ya] China Meteorol Adm, Huayun Informat Technol Engn Co Ltd, Beijing 100081, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Linglong,Zhang, Yonghong,Wang, Jiangeng,et al. Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning[J]. 南京信息工程大学,2021,13(4).
APA	Zhu, Linglong.,Zhang, Yonghong.,Wang, Jiangeng.,Tian, Wei.,Liu, Qi.,...&Chu, Ya.(2021).Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning.REMOTE SENSING,13(4).
MLA	Zhu, Linglong,et al."Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning".REMOTE SENSING 13.4(2021).