| 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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| 作者姓名： | CAO Yungang ;YANG Xiuchun ;ZHU Xiaohua |
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| 作者单位： | [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 |
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| 基金项目： | Under the auspices of Special Basic Research Fund for Central Public Scientific Research Institutes （No. 2007-03）.Acknowledgments The authors would like to thank Tibet Meteorological Bureau and CAMP-Tibet program for providing observed data. The authors would also like to thank the National Snow and Ice Data Center （NSIDC）, USA for supplying the AMSR-E data. |
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| 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.
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| 关 键 词： | 人工神经网络 贝叶斯规则 青藏高原 积雪厚度 温度 |
| 收稿时间： | 22 July 2006 |
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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| 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. |
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| Authors: | Yungang Cao Xiuchun Yang Xiaohua Zhu |
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| Institution: | (1) Department of Meteorology, Climate Snow and Hydrology Research Group (CSHRG), COMSATS Institute of Information Technology, Plot No. 30, Sector H-8/1, Islamabad, 44000, Pakistan |
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| 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 tempera-ture 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 damsels on model fitting can be decreased due to adopt-ing the Bayesian regularization approach. |
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| Keywords: | artificial neural network Bayesian regularization snow depth brightness temperature Qinghai-Tibet Pla-teau |
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