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Yumeng Tao, Xiaogang Gao, Alexander Ihler, Soroosh Sorooshian, and Kuolin Hsu

extracting useful information in the satellite imageries and linking it to precipitation estimates ( Nasrollahi et al. 2013 ; Sorooshian et al. 2011 ). In recent years, deep learning techniques, also known as deep neural networks, have been developed and widely applied in the machine learning and computer vision areas ( Bengio 2009 ; Hinton et al. 2006 ; Vincent et al. 2010 ). Tao et al. (2016a , b) applied the methods to satellite-based precipitation estimation and demonstrated their effectiveness

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Kuai Fang and Chaopeng Shen

function can be regarded as piecewise linear, to be estimated locally at each pixel. However, it is not clear if such a linear model could fully describe the process of soil moisture change, and, especially, provide strong performance for forecasts with a few days of latency. Deep learning (DL) is well known for its ability to learn nonlinear mapping relationships and model dynamical systems ( Shen 2018 ; LeCun et al. 2015 ; Schmidhuber 2015 ). In our previous work ( Fang et al. 2017 ), we employed

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Yumeng Tao, Kuolin Hsu, Alexander Ihler, Xiaogang Gao, and Soroosh Sorooshian

related to rainfall. In recent years, deep learning algorithms, also known as deep neural networks (DNNs), have been widely applied in many fields, including signal and image processing, computer vision, and language, in part for their ability to perform complex feature extraction ( Bengio 2009 ; Hinton et al. 2006 ; LeCun et al. 2015 ). According to many recent studies ( Glorot et al. 2011 ; Hinton et al. 2006 ; Lu et al. 2013 ; Tao et al. 2016a , 2018 ; Vincent et al. 2008 ; Yang et al. 2017

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Yumeng Tao, Xiaogang Gao, Kuolin Hsu, Soroosh Sorooshian, and Alexander Ihler

key to making the best use of these datasets is promoting advanced methods that assist in the extraction of valuable information from the raw data ( Nasrollahi et al. 2013 ; Sorooshian et al. 2011 ). In recent years, multiple novel techniques for deep learning have been developed in the scientific discipline of machine learning, which is a breakthrough for dealing with large and complex datasets, especially for feature extraction from a large amount of image data ( Bengio 2009 ; Hinton et al

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Mohammad Reza Ehsani, Ali Behrangi, Abishek Adhikari, Yang Song, George J. Huffman, Robert F. Adler, David T. Bolvin, and Eric J. Nelkin

algorithm like deep neural networks (DNNs) may find better solutions. 2) Brightness temperatures and cloud probability fields of AVHRR used for training the machine learning algorithm are matched up with corresponding CloudSat snowfall rate meeting certain spatial and temporal conditions. The matchup process introduces noise to the features and decreases the capacity of the machine learning algorithms to fully exploit the information within these features. 5. Concluding remarks Despite its importance

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Anne Felsberg, Gabriëlle J. M. De Lannoy, Manuela Girotto, Jean Poesen, Rolf H. Reichle, and Thomas Stanley

-derived data products of soil moisture in deeper layers (at least 1 m) rather than satellite retrievals of surface (0–5 cm) soil moisture conditions, because the shear planes of both deep-seated and shallow landslides are typically much deeper than 5 cm. Estimates of the deeper soil moisture based on physically based land surface models were preferred ( Brocca et al. 2016 ; Kirschbaum et al. 2020 ; Thomas et al. 2019 ) and found to be superior compared to those of, for example, simple exponential filters

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Meixia Lv, Zhuguo Ma, and Naiming Yuan

spatial values are very difficult to obtain. For instance, Zhang et al. (2020) evaluated the correlation of annual trends between GRACE-based GWS in China and observations without converting the groundwater level changes to storage changes, and they did not distinguish unconfined and confined groundwater. However, the contribution of groundwater depletion from deep aquifers has been emphasized by Feng et al. (2013) in north China. Zhang et al. (2019) calculated the relative contributions of

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Eli J. Dennis and Ernesto Hugo Berbery

current simplifications of soil processes [see Van Looy et al. (2017) for a review]. PTFs vary markedly in complexity; some use advanced mathematical techniques like machine learning and neural networks, while others use physically based relationships that allow soil properties to more accurately reflect the environmental conditions. Advanced versions of the PTFs are being developed that will improve the representation of water flow through the intricate soil–vegetation system. Soil hydrophysical

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Mojtaba Sadeghi, Ata Akbari Asanjan, Mohammad Faridzad, Phu Nguyen, Kuolin Hsu, Soroosh Sorooshian, and Dan Braithwaite

related and co-related factors. Therefore, applying more advanced data-driven methodologies for automatically extracting features from the input data will enhance precipitation estimation accuracy. Recent advances in the field of machine learning (ML) offer exciting opportunities to expand our knowledge about the Earth system ( Lary et al. 2016 ). Among the different machine learning methods, the deep neural network (DNN) method is a fast-growing branch characterized by its flexibility and capacity to

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Nina Raoult, Catherine Ottlé, Philippe Peylin, Vladislav Bastrikov, and Pascal Maugis

also find rs* to be the parameter with the highest reduction in error highlighting its importance in accurately modeling drydowns. The value of root profile z is reduced for most sites, especially for deciduous forests. This again may be linked to the fact that the model was drying out faster than the observations. By reducing the root profile, the roots do not reach as deep in the soil and therefore have access to less water. More water is kept in the root zone and transferred to the surface

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