Retrieval Snow Depth by Artificial Neural Network Methodology from Integrated AMSR-E and In-situ Dat

• 作 者: CAO Yungang YANG Xiuchun ZHU X
• 机 构: Department of Survey Engineering,Institute of Agricultural Resources and Regional Planning,Institute
• 出 处: 中国地理科学（英文版）
• 发表日期: 2004年期
• 关键词: artificial neural network Bayesian regularization snow depth brightness temperat

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.