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

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.

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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