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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 (Basel, Switzerland), 2021-02, Vol.13 (4), p.554 [Peer Reviewed Journal]

2021. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs13040554

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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
Author: Ahmed, A. A. Masrur ; Deo, Ravinesh C ; Raj, Nawin ; Ghahramani, Afshin ; Feng, Qi ; Yin, Zhenliang ; Yang, Linshan
Subjects: Agricultural practices ; Agricultural resources ; Algorithms ; Artificial neural networks ; Climate models ; Data collection ; Decomposition ; Deep learning ; deep learning algorithm ; El Nino ; Farm buildings ; Forecasting ; gated recurrent unit ; Hydrology ; Model testing ; MODIS ; Neural networks ; Precipitation ; Rain ; Remote sensing ; Remote sensors ; satellite models of soil moisture ; Satellite observation ; Soil moisture ; Soil surfaces ; Stream flow ; Sustainable agriculture ; Sustainable practices ; Teaching methods ; Temporal resolution ; Terrestrial environments ; Time series ; Variables ; Water management ; Water resources ; Water resources management ; Wavelet transforms
Is Part Of: Remote sensing (Basel, Switzerland), 2021-02, Vol.13 (4), p.554
Description: 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.
Publisher: Basel: MDPI AG
Language: English
Identifier: ISSN: 2072-4292
EISSN: 2072-4292
DOI: 10.3390/rs13040554
Source: ROAD: Directory of Open Access Scholarly Resources
ProQuest Central
DOAJ Directory of Open Access Journals

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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

2021. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs13040554

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