气候变化领域集成服务门户(CCPortal): 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

CCPortal

DOI	10.3390/rs13040554
	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
	Ahmed, A. A. Masrur; Deo, Ravinesh C.; Raj, Nawin; Ghahramani, Afshin; Feng, Qi; Yin, Zhenliang; Yang, Linshan
发表日期	2021
EISSN	2072-4292
卷号	13 期号:4
英文摘要	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.
英文关键词	deep learning algorithm; MODIS; gated recurrent unit; satellite models of soil moisture
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS关键词	SHORT-TERM-MEMORY ; GLOBAL SOLAR-RADIATION ; NINO SOUTHERN-OSCILLATION ; MACHINE MODEL ; REFINED INDEX ; PREDICTION ; DECOMPOSITION ; RAINFALL ; PRECIPITATION ; PERFORMANCE
WOS记录号	WOS:000624408000001
来源期刊	REMOTE SENSING
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/239445
作者单位	[Ahmed, A. A. Masrur; Deo, Ravinesh C.; Raj, Nawin; Ghahramani, Afshin] Univ Southern Queensland, Sch Sci, Springfield, Qld 4300, Australia; [Feng, Qi; Yin, Zhenliang; Yang, Linshan] Chinese Acad Sci, Key Lab Ecohydrol Inland River Basin & Northwest, Inst Ecoenvironment & Resources, Lanzhou 730000, Peoples R China
推荐引用方式 GB/T 7714	Ahmed, A. A. Masrur,Deo, Ravinesh C.,Raj, Nawin,et al. 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[J]. 中国科学院西北生态环境资源研究院,2021,13(4).
APA	Ahmed, A. A. Masrur.,Deo, Ravinesh C..,Raj, Nawin.,Ghahramani, Afshin.,Feng, Qi.,...&Yang, Linshan.(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(4).
MLA	Ahmed, A. A. Masrur,et al."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 13.4(2021).