Retrieval Snow Depth by Artificial Neural Network Methodology from Integrated AMSR-E and In-situ Data——A Case Study in Qinghai-Tibet Plateau

CAO Yungang; YANG Xiuchun; ZHU Xiaohua

doi:10.1007/s11769-008-356-2

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Retrieval Snow Depth by Artificial Neural Network Methodology from Integrated AMSR-E and In-situ Data——A Case Study in Qinghai-Tibet Plateau

CAO Yungang, YANG Xiuchun, ZHU Xiaohua

文章导航 > Chinese Geographical Science > 2008 > 18(4): 356-360

CAO Yungang, YANG Xiuchun, ZHU Xiaohua. Retrieval Snow Depth by Artificial Neural Network Methodology from Integrated AMSR-E and In-situ Data——A Case Study in Qinghai-Tibet Plateau[J]. 中国地理科学, 2008, 18(4): 356-360. doi: 10.1007/s11769-008-356-2

引用本文:

CAO Yungang, YANG Xiuchun, ZHU Xiaohua. Retrieval Snow Depth by Artificial Neural Network Methodology from Integrated AMSR-E and In-situ Data——A Case Study in Qinghai-Tibet Plateau[J]. Chinese Geographical Science, 2008, 18(4): 356-360. doi: 10.1007/s11769-008-356-2

Citation:

CAO Yungang, YANG Xiuchun, ZHU Xiaohua. Retrieval Snow Depth by Artificial Neural Network Methodology from Integrated AMSR-E and In-situ Data——A Case Study in Qinghai-Tibet Plateau[J]. Chinese Geographical Science, 2008, 18(4): 356-360. doi: 10.1007/s11769-008-356-2

Retrieval Snow Depth by Artificial Neural Network Methodology from Integrated AMSR-E and In-situ Data——A Case Study in Qinghai-Tibet Plateau

doi: 10.1007/s11769-008-356-2

1.
Department of Survey Engineering, School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China;
2.
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China;
3.
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

基金项目: Under the auspices of Special Basic Research Fund for Central Public Scientific Research Institutes (No. 2007-03)

详细信息

通讯作者:
CAO Yungang. E-mail: yungang78@163.com

Retrieval Snow Depth by Artificial Neural Network Methodology from Integrated AMSR-E and In-situ Data——A Case Study in Qinghai-Tibet Plateau

1.
Department of Survey Engineering, School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China;
2.
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China;
3.
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

摘要: On the basis of artificial neural network (ANN) model, this paper presents an algorithm for inversing snow depth with use of AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System (EOS)) dataset, i.e., brightness temperature at 18.7 and 36.5GHz in Qinghai-Tibet Plateau during the snow season of 2002–2003. In order to overcome the overfitting problem in ANN modeling, this methodology adopts a Bayesian regularization approach. The experiments are performed to compare the results obtained from the ANN-based algorithm with those obtained from other existing algorithms, i.e., Chang algorithm, spectral polarization difference (SPD) algorithm, and temperature gradient (TG) algorithm. The experimental results show that the presented algorithm has the highest accuracy in estimating snow depth. In addition, the effects of the noises in datasets on model fitting can be decreased due to adopting the Bayesian regularization approach.
- artificial neural network /
- Bayesian regularization /
- snow depth /
- brightness temperature /
- Qinghai-Tibet Plateau
Abstract: On the basis of artificial neural network (ANN) model, this paper presents an algorithm for inversing snow depth with use of AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System (EOS)) dataset, i.e., brightness temperature at 18.7 and 36.5GHz in Qinghai-Tibet Plateau during the snow season of 2002–2003. In order to overcome the overfitting problem in ANN modeling, this methodology adopts a Bayesian regularization approach. The experiments are performed to compare the results obtained from the ANN-based algorithm with those obtained from other existing algorithms, i.e., Chang algorithm, spectral polarization difference (SPD) algorithm, and temperature gradient (TG) algorithm. The experimental results show that the presented algorithm has the highest accuracy in estimating snow depth. In addition, the effects of the noises in datasets on model fitting can be decreased due to adopting the Bayesian regularization approach.
- artificial neural network /
- Bayesian regularization /
- snow depth /
- brightness temperature /
- Qinghai-Tibet Plateau

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Retrieval Snow Depth by Artificial Neural Network Methodology from Integrated AMSR-E and In-situ Data——A Case Study in Qinghai-Tibet Plateau

doi: 10.1007/s11769-008-356-2

1. Department of Survey Engineering, School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China;

2. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China;

3. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

基金项目: Under the auspices of Special Basic Research Fund for Central Public Scientific Research Institutes (No. 2007-03)

通讯作者: CAO Yungang. E-mail: yungang78@163.com

收稿日期: 2008-04-26
修回日期: 2008-09-20
刊出日期: 2008-12-20

关键词:

artificial neural network /

Bayesian regularization /

snow depth /

brightness temperature /

Qinghai-Tibet Plateau

摘要: On the basis of artificial neural network (ANN) model, this paper presents an algorithm for inversing snow depth with use of AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System (EOS)) dataset, i.e., brightness temperature at 18.7 and 36.5GHz in Qinghai-Tibet Plateau during the snow season of 2002–2003. In order to overcome the overfitting problem in ANN modeling, this methodology adopts a Bayesian regularization approach. The experiments are performed to compare the results obtained from the ANN-based algorithm with those obtained from other existing algorithms, i.e., Chang algorithm, spectral polarization difference (SPD) algorithm, and temperature gradient (TG) algorithm. The experimental results show that the presented algorithm has the highest accuracy in estimating snow depth. In addition, the effects of the noises in datasets on model fitting can be decreased due to adopting the Bayesian regularization approach.

English Abstract

Retrieval Snow Depth by Artificial Neural Network Methodology from Integrated AMSR-E and In-situ Data——A Case Study in Qinghai-Tibet Plateau

1. Department of Survey Engineering, School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China;

2. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China;

3. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Received Date: 2008-04-26
Rev Recd Date: 2008-09-20
Publish Date: 2008-12-20

Keywords:

Abstract: On the basis of artificial neural network (ANN) model, this paper presents an algorithm for inversing snow depth with use of AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System (EOS)) dataset, i.e., brightness temperature at 18.7 and 36.5GHz in Qinghai-Tibet Plateau during the snow season of 2002–2003. In order to overcome the overfitting problem in ANN modeling, this methodology adopts a Bayesian regularization approach. The experiments are performed to compare the results obtained from the ANN-based algorithm with those obtained from other existing algorithms, i.e., Chang algorithm, spectral polarization difference (SPD) algorithm, and temperature gradient (TG) algorithm. The experimental results show that the presented algorithm has the highest accuracy in estimating snow depth. In addition, the effects of the noises in datasets on model fitting can be decreased due to adopting the Bayesian regularization approach.

引用本文:

CAO Yungang, YANG Xiuchun, ZHU Xiaohua. Retrieval Snow Depth by Artificial Neural Network Methodology from Integrated AMSR-E and In-situ Data——A Case Study in Qinghai-Tibet Plateau[J]. Chinese Geographical Science, 2008, 18(4): 356-360. doi: 10.1007/s11769-008-356-2

Citation:

CAO Yungang, YANG Xiuchun, ZHU Xiaohua. Retrieval Snow Depth by Artificial Neural Network Methodology from Integrated AMSR-E and In-situ Data——A Case Study in Qinghai-Tibet Plateau[J]. Chinese Geographical Science, 2008, 18(4): 356-360. doi: 10.1007/s11769-008-356-2

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Retrieval Snow Depth by Artificial Neural Network Methodology from Integrated AMSR-E and In-situ Data——A Case Study in Qinghai-Tibet Plateau

doi: 10.1007/s11769-008-356-2

通讯作者: CAO Yungang. E-mail: yungang78@163.com

Retrieval Snow Depth by Artificial Neural Network Methodology from Integrated AMSR-E and In-situ Data——A Case Study in Qinghai-Tibet Plateau

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Retrieval Snow Depth by Artificial Neural Network Methodology from Integrated AMSR-E and In-situ Data——A Case Study in Qinghai-Tibet Plateau

doi: 10.1007/s11769-008-356-2

通讯作者: CAO Yungang. E-mail: yungang78@163.com

English Abstract

Retrieval Snow Depth by Artificial Neural Network Methodology from Integrated AMSR-E and In-situ Data——A Case Study in Qinghai-Tibet Plateau

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通讯作者:
CAO Yungang. E-mail: yungang78@163.com