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Article

Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data

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School of Science, University of Southern Queensland, Springfield, QLD 4300, Australia
2
Key Laboratory of Ecohydrology of Inland River Basin and Northwest, Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Academic Editor: Nemesio Rodriguez-Fernandez
Remote Sens. 2021, 13(4), 554; https://doi.org/10.3390/rs13040554
Received: 16 December 2020 / Revised: 18 January 2021 / Accepted: 27 January 2021 / Published: 4 February 2021
Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed for daily time-step surface soil moisture (SSM) forecasts, employing the gated recurrent unit (GRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and convolutional neural network (CNN). To establish the objective model’s viability for SSM forecasting at multi-step daily horizons, the hybrid CEEMDAN-CNN-GRU model is tested at 1st, 5th, 7th, 14th, 21st, and 30th day ahead period by assimilating a comprehensive pool of 52 predictor dataset obtained from three distinct data sources. Data comprise satellite-derived Global Land Data Assimilation System (GLDAS) repository a global, high-temporal resolution, unique terrestrial modelling system, and ground-based variables from Scientific Information Landowners (SILO) and synoptic-scale climate indices. The results demonstrate the forecasting capability of the hybrid CEEMDAN-CNN-GRU model with respect to the counterpart comparative models. This is supported by a relatively lower value of the mean absolute percentage and root mean square error. In terms of the statistical score metrics and infographics employed to test the final model’s utility, the proposed CEEMDAN-CNN-GRU models are considerably superior compared to a standalone and other hybrid method tested on independent SSM data developed through feature selection approaches. Thus, the proposed approach can be successfully implemented in hydrology and agriculture management. View Full-Text
Keywords: deep learning algorithm; MODIS; gated recurrent unit; satellite models of soil moisture deep learning algorithm; MODIS; gated recurrent unit; satellite models of soil moisture
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MDPI and ACS Style

Ahmed, A.A.M.; Deo, R.C.; Raj, N.; Ghahramani, A.; Feng, Q.; Yin, Z.; Yang, L. Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data. Remote Sens. 2021, 13, 554. https://doi.org/10.3390/rs13040554

AMA Style

Ahmed AAM, Deo RC, Raj N, Ghahramani A, Feng Q, Yin Z, Yang L. Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data. Remote Sensing. 2021; 13(4):554. https://doi.org/10.3390/rs13040554

Chicago/Turabian Style

Ahmed, A. A.M., Ravinesh C. Deo, Nawin Raj, Afshin Ghahramani, Qi Feng, Zhenliang Yin, and Linshan Yang. 2021. "Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data" Remote Sensing 13, no. 4: 554. https://doi.org/10.3390/rs13040554

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