The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network
Open accessAbstract
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.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
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Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
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Machine Learning in Agriculture: A Review
Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018) · Sensors · doi:10.3390/s18082674 · Cited by 3,441
Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks
(2018) · doi:10.5194/hess-22-6005-2018 · Cited by 2,114
Deep learning in environmental remote sensing: Achievements and challenges
Yuan, Q., Shen, H., Li, T., Li, Z., Li, S., Jiang, Y., Xu, H., Tan, W., Yang, Q., Wang, J., et al. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.111716 · Cited by 1,697
Feng, D., Fang, K., & Shen, C. (2020) · Water Resources Research · doi:10.1029/2019wr026793 · Cited by 633
Self-citationHybrid forecasting: blending climate predictions with AI models
Slater, L. J., Arnal, L., Boucher, M.-A., Chang, A. Y.-Y., Moulds, S., Murphy, C., Nearing, G., Shalev, G., Shen, C., Speight, L., et al. (2023) · Hydrology and Earth System Sciences · doi:10.5194/hess-27-1865-2023 · Cited by 281
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Metadata
- Authors
- Fang, K., Shen, C., Kifer, D., & Yang, X.
- Venue
- Geophysical Research Letters
- Publisher
- American Geophysical Union (AGU)
- Published
- 2017-11-09
- Volume
- 44
- Issue
- 21
- ISSN
- 0094-8276 (ISSN-L)
- 1944-8007
- OpenAlex
- W2738911869
- Cited by
- 297
- References
- 4
- FWCI
- 7.83
- Type
- journal article
- Language
- en
- Open access
- green
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