Improved daily SMAP satellite soil moisture prediction over China using deep learning model with transfer learning

Li, Qingliang; Wang, Ziyu; Shangguan, Wei; Li, Lü; Yao, Yanjie; Fu, Yu

doi:10.1016/j.jhydrol.2021.126698

Cited by 84 publications

(49 citation statements)

References 38 publications

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“…Deep learning (DL) models currently represent the state‐of‐the‐art in hydrologic prediction (Nearing et al., 2021; Shen et al., 2021), as evidenced by their unmatched accuracy in predicting streamflow (Liu et al., 2020), soil moisture (Li et al., 2021), evapotranspiration (Ahmed et al., 2021), stream temperature (Rahmani et al., 2021), and water quality indicators (Aldhyani et al., 2020; Zhi et al., 2021). For streamflow prediction, long short‐term msemory networks (LSTMs, Hochreiter & Schmidhuber, 1997) have proven particularly effective due to their strong inductive bias toward storing information over time (Hoedt et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change

2022

Water Resources Research

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Deep learning (DL) models currently represent the state-of-the-art in hydrologic prediction (Nearing et al., 2021;Shen et al., 2021), as evidenced by their unmatched accuracy in predicting streamflow (Liu et al., 2020), soil moisture (Li et al., 2021), evapotranspiration (Ahmed et al., 2021), stream temperature (Rahmani et al., 2021, and water quality indicators (Aldhyani et al., 2020;Zhi et al., 2021). For streamflow prediction, long short-term msemory networks (LSTMs, Hochreiter & Schmidhuber, 1997) have proven particularly effective due to their strong inductive bias toward storing information over time (Hoedt et al., 2021). This property is well suited for capturing hydrologic dynamics driven by multi-scale memory effects within a watershed, such as the persistence and release of water from soil moisture and snowpack. LSTMs trained across a large number of watersheds have been shown to outperform process-based hydrologic models by a substantial margin (Kratzert et al., 2018), even at hourly timescales (Gauch, Kratzert, et al., 2021) and for watersheds treated as unseen by the LSTM (Kratzert, Klotz, Herrnegger, et al., 2019). Given their high degree of performance, LSTMs are being considered for a range of hydrologic applications, including forecasting (Cheng et al., 2020;Sharma et al., 2021), streamflow estimation

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Section: Introductionmentioning

confidence: 99%

Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change

2022

Water Resources Research

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“…Deep learning prediction models are prone to overfitting for small data sets, and their prediction accuracy is not high [21,39]. On the other hand, obtaining a perfect data set is expensive and time-consuming.…”

Section: Transfer Learningmentioning

confidence: 99%

Landslide Displacement Prediction Based on Transfer Learning and Bi-GRU

et al. 2022

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Predicting slope deformation prediction is crucial for early warning of slope failure, preventing damage to properties, and saving human lives. However, in practice, equipment maintenance causes discontinuity in the displacement data, and the traditional prediction models based on deep networks do not perform well in this case. To solve the problem of prediction accuracy in case of discontinuous and inadequate data, we propose a combined displacement prediction model that integrates the bidirectional gated recurrent unit (Bi-GRU), attention mechanism, and transfer learning. The Bi-GRU is employed to extract the forward and backward characteristics of displacement series, and the attention mechanism is utilized to give different weights to the extracted information so as to highlight the critical information. Transfer learning is used to guarantee prediction accuracy in case of discontinuous and limited data. The model is then employed to predict the slope displacement of the JinYu Cement Plant in China. Finally, the modeling results excellently agree with measured displacement, especially in case of insufficient sample data.

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“…Recently, deep learning (DL) models have demonstrated high performance for modeling soil moisture (K. Fang et al., 2017, 2019, 2020; Q. Li et al., 2021; O & Orth, 2021). K. Fang et al.…”

Section: Introductionmentioning

confidence: 99%

A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data

Liu

Rahmani

Lawson

2022

Geophysical Research Letters

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Deep learning (DL) models trained on hydrologic observations can perform extraordinarily well, but they can inherit deficiencies of the training data, such as limited coverage of in situ data or low resolution/accuracy of satellite data. Here we propose a novel multiscale DL scheme learning simultaneously from satellite and in situ data to predict 9 km daily soil moisture (5 cm depth). Based on spatial cross‐validation over sites in the conterminous United States, the multiscale scheme obtained a median correlation of 0.901 and root‐mean‐square error of 0.034 m3/m3. It outperformed the Soil Moisture Active Passive satellite mission's 9 km product, DL models trained on in situ data alone, and land surface models. Our 9 km product showed better accuracy than previous 1 km satellite downscaling products, highlighting limited impacts of improving resolution. Not only is our product useful for planning against floods, droughts, and pests, our scheme is generically applicable to geoscientific domains with data on multiple scales, breaking the confines of individual data sets.

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Improved daily SMAP satellite soil moisture prediction over China using deep learning model with transfer learning

Cited by 84 publications

References 38 publications

Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change

Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change

Landslide Displacement Prediction Based on Transfer Learning and Bi-GRU

A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data

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