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
Article
Article Title | 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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ERA Journal ID | 201448 |
Article Category | Article |
Authors | Ahmed, A. A. Masrur (Author), Deo, Ravinesh C. (Author), Raj, Nawin (Author), Ghahramani, Afshin (Author), Feng, Qi (Author), Yin, Zhenliang (Author) and Yang, Linshan (Author) |
Journal Title | Remote Sensing |
Journal Citation | 13 (4), pp. 1-30 |
Article Number | 554 |
Number of Pages | 30 |
Year | 2021 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2072-4292 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/rs13040554 |
Web Address (URL) | https://www.mdpi.com/2072-4292/13/4/554 |
Abstract | 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. |
Keywords | deep learning algorithm; MODIS; gated recurrent unit; satellite models of soil moisture |
ANZSRC Field of Research 2020 | 460207. Modelling and simulation |
410602. Pedology and pedometrics | |
Byline Affiliations | School of Sciences |
Chinese Academy of Sciences, China | |
Institution of Origin | University of Southern Queensland |
Funding source | Grant ID USQ-CAS Grant 2018-2021 |
https://research.usq.edu.au/item/q6307/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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