Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network
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 s...
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Series: | Advances in Meteorology |
Online Access: | http://dx.doi.org/10.1155/2018/9315132 |
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doaj-a046aaceafdc405b825ed53a3b9d6e542020-11-25T01:27:11ZengHindawi LimitedAdvances in Meteorology1687-93091687-93172018-01-01201810.1155/2018/93151329315132Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural NetworkQingyan Meng0Linlin Zhang1Qiuxia Xie2Shun Yao3Xu Chen4Ying Zhang5Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, ChinaDFH Satellite Co. Ltd., Beijing 100094, ChinaInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, ChinaSoil 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.http://dx.doi.org/10.1155/2018/9315132 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Qingyan Meng Linlin Zhang Qiuxia Xie Shun Yao Xu Chen Ying Zhang |
spellingShingle |
Qingyan Meng Linlin Zhang Qiuxia Xie Shun Yao Xu Chen Ying Zhang Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network Advances in Meteorology |
author_facet |
Qingyan Meng Linlin Zhang Qiuxia Xie Shun Yao Xu Chen Ying Zhang |
author_sort |
Qingyan Meng |
title |
Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network |
title_short |
Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network |
title_full |
Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network |
title_fullStr |
Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network |
title_full_unstemmed |
Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network |
title_sort |
combined use of gf-3 and landsat-8 satellite data for soil moisture retrieval over agricultural areas using artificial neural network |
publisher |
Hindawi Limited |
series |
Advances in Meteorology |
issn |
1687-9309 1687-9317 |
publishDate |
2018-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2018/9315132 |
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