气候变化领域集成服务门户(CCPortal): Global downscaling of remotely sensed soil moisture using neural networks

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DOI	10.5194/hess-22-5341-2018
	Global downscaling of remotely sensed soil moisture using neural networks
	Hamed Alemohammad S.; Kolassa J.; Prigent C.; Aires F.; Gentine P.
发表日期	2018
ISSN	1027-5606
起始页码	5341
结束页码	5356
卷号	22 期号:10
英文摘要	Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., of the order of 1km) is necessary in order to quantify its role in regional feedbacks between the land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information at fine spatial scales. Soil moisture estimates from current satellite missions have a reasonably good temporal revisit over the globe (2-3-day repeat time); however, their finest spatial resolution is 9km. NASA's Soil Moisture Active Passive (SMAP) satellite has estimated soil moisture at two different spatial scales of 36 and 9km since April 2015. In this study, we develop a neural-network-based downscaling algorithm using SMAP observations and disaggregate soil moisture to 2.25km spatial resolution. Our approach uses the mean monthly Normalized Differenced Vegetation Index (NDVI) as ancillary data to quantify the subpixel heterogeneity of soil moisture. Evaluation of the downscaled soil moisture estimates against in situ observations shows that their accuracy is better than or equal to the SMAP 9km soil moisture estimates. © Author(s) 2018.
语种	英语
scopus关键词	Atmospheric boundary layer; Image resolution; NASA; Surface measurement; Agricultural management; In-situ observations; Land-surface process; Remotely sensed soil moisture; Satellite mission; Soil moisture active passive (SMAP); Spatial resolution; Spatio-temporal scale; Soil moisture; agricultural management; algorithm; artificial neural network; boundary layer; downscaling; in situ measurement; NDVI; remote sensing; satellite mission; soil moisture; spatial resolution; spatiotemporal analysis
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159885
作者单位	Hamed Alemohammad, S., Department of Earth and Environmental Engineering, Columbia University, New York, NY, United States, Columbia Water Center, Columbia University, New York, NY, United States, Radiant Earth Foundation, Washington, DC, United States; Kolassa, J., Universities Space Research Association, Columbia, MD, United States, Global Modelling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, United States; Prigent, C., Department of Earth and Environmental Engineering, Columbia University, New York, NY, United States, Columbia Water Center, Columbia University, New York, NY, United States, Observatoire de Paris, Paris, 75014, France; Aires, F., Department of Earth and Environmental Engineering, Columbia University, New York, NY, United States, Columbia Water Center, Columbia University, New York, NY, United States, Observatoire de Paris, Paris, 75014, France; Gentine, P., Department of Earth and Environmental Engineering, Columbia University, New York, NY, United States, C...
推荐引用方式 GB/T 7714	Hamed Alemohammad S.,Kolassa J.,Prigent C.,et al. Global downscaling of remotely sensed soil moisture using neural networks[J],2018,22(10).
APA	Hamed Alemohammad S.,Kolassa J.,Prigent C.,Aires F.,&Gentine P..(2018).Global downscaling of remotely sensed soil moisture using neural networks.Hydrology and Earth System Sciences,22(10).
MLA	Hamed Alemohammad S.,et al."Global downscaling of remotely sensed soil moisture using neural networks".Hydrology and Earth System Sciences 22.10(2018).