globalchange  > 气候变化与战略
DOI: 10.5194/hess-22-5341-2018
论文题名:
Global downscaling of remotely sensed soil moisture using neural networks
作者: Hamed Alemohammad S.; Kolassa J.; Prigent C.; Aires F.; Gentine P.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2018
卷: 22, 期:10
起始页码: 5341
结束页码: 5356
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
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.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/163168
Appears in Collections:气候变化与战略

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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, Columbia Water Center, Columbia University, New York, NY, United States, Earth Institute, Columbia University, New York, NY, United States

Recommended Citation:
Hamed Alemohammad S.,Kolassa J.,Prigent C.,et al. Global downscaling of remotely sensed soil moisture using neural networks[J]. Hydrology and Earth System Sciences,2018-01-01,22(10)
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