![](/images/graphics-bg.png)
Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network
Joint Authors
Meng, Qingyan
Zhang, Linlin
Xie, Qiuxia
Yao, Shun
Chen, Xu
Zhang, Ying
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-08-15
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Soil moisture is the basic condition required for crop growth and development.
Gaofen-3 (GF-3) is the first C-band synthetic-aperture radar (SAR) satellite of China, offering broad land and ocean imaging applications, including soil moisture monitoring.
This study developed an approach to estimate soil moisture in agricultural areas from GF-3 data.
An inversion technique based on an artificial neural network (ANN) is introduced.
The neural network was trained and tested on a training sample dataset generated from the Advanced Integral Equation Model.
Incidence angle and HH or VV polarization data were used as input variables of the ANN, with soil moisture content (SMC) and surface roughness as the output variables.
The backscattering contribution from the vegetation was eliminated using the water cloud model (WCM).
The acquired soil backscattering coefficients of GF-3 and in situ measurement data were used to validate the SMC estimation algorithm, which achieved satisfactory results (R2 = 0.736; RMSE = 0.042).
These results highlight the contribution of the combined use of the GF-3 synthetic-aperture radar and Landsat-8 images based on an ANN method for improving SMC estimates and supporting hydrological studies.
American Psychological Association (APA)
Meng, Qingyan& Zhang, Linlin& Xie, Qiuxia& Yao, Shun& Chen, Xu& Zhang, Ying. 2018. Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network. Advances in Meteorology،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1118989
Modern Language Association (MLA)
Meng, Qingyan…[et al.]. Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network. Advances in Meteorology No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1118989
American Medical Association (AMA)
Meng, Qingyan& Zhang, Linlin& Xie, Qiuxia& Yao, Shun& Chen, Xu& Zhang, Ying. Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network. Advances in Meteorology. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1118989
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1118989