Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2017GL075619 |
Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental US Using a Deep Learning Neural Network | |
Fang, Kuai1; Shen, Chaopeng1; Kifer, Daniel2; Yang, Xiao2 | |
2017-11-16 | |
发表期刊 | GEOPHYSICAL RESEARCH LETTERS |
ISSN | 0094-8276 |
EISSN | 1944-8007 |
出版年 | 2017 |
卷号 | 44期号:21 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | The Soil Moisture Active Passive (SMAP) mission has delivered valuable sensing of surface soil moisture since 2015. However, it has a short time span and irregular revisit schedules. Utilizing a state-of-the-art time series deep learning neural network, Long Short-Term Memory (LSTM), we created a system that predicts SMAP level-3 moisture product with atmospheric forcings, model-simulated moisture, and static physiographic attributes as inputs. The system removes most of the bias with model simulations and improves predicted moisture climatology, achieving small test root-mean-square errors (<0.035) and high-correlation coefficients >0.87 for over 75% of Continental United States, including the forested southeast. As the first application of LSTM in hydrology, we show the proposed network avoids overfitting and is robust for both temporal and spatial extrapolation tests. LSTM generalizes well across regions with distinct climates and environmental settings. With high fidelity to SMAP, LSTM shows great potential for hindcasting, data assimilation, and weather forecasting. |
英文关键词 | SMAP deep learning LSTM soil moisture hindcasting remote sensing |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000418572900033 |
WOS关键词 | LAND-SURFACE MODEL ; SOIL-MOISTURE ; FORECASTS |
WOS类目 | Geosciences, Multidisciplinary |
WOS研究方向 | Geology |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/25696 |
专题 | 气候变化 |
作者单位 | 1.Penn State Univ, Dept Civil & Environm Engn, University Pk, PA 16802 USA; 2.Penn State Univ, Dept Comp Sci & Engn, University Pk, PA 16802 USA |
推荐引用方式 GB/T 7714 | Fang, Kuai,Shen, Chaopeng,Kifer, Daniel,et al. Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental US Using a Deep Learning Neural Network[J]. GEOPHYSICAL RESEARCH LETTERS,2017,44(21). |
APA | Fang, Kuai,Shen, Chaopeng,Kifer, Daniel,&Yang, Xiao.(2017).Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental US Using a Deep Learning Neural Network.GEOPHYSICAL RESEARCH LETTERS,44(21). |
MLA | Fang, Kuai,et al."Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental US Using a Deep Learning Neural Network".GEOPHYSICAL RESEARCH LETTERS 44.21(2017). |
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