摘要
积雪中的污染物含量可以用来反映区域和全球范围内人类活动对环境的污染,但是迄今为止,对大范围或人类活动难以到达的地区进行积雪污染物含量时空监测的研究尚不多见。文章通过模拟大气沉降实验,应用光谱学技术分析了不同污染物含量对积雪反射光谱的影响,而后分别利用构建特征指数法、主成分分析法、BP神经网络以及RBF神经网络模型对积雪污染物含量预测,表明神经网络模型结合高光谱遥感数据方法能够较为准确地估算积雪污染物含量。
Contaminants in the snow can be used to reflect regional and global environmental pollution caused by human activities.However,so far,the research on space-time monitoring of snow contamination concentration for a wide range or areas difficult for human to reach is very scarce.In the present paper,based on the simulated atmospheric deposition experiments,the spectroscopy technique method was applied to analyze the effect of different contamination concentration on the snow reflectance spectra.Then an evaluation of snow contamination concentration（SCC） retrieval methods was conducted using characteristic index method（SDI）,principal component analysis（PCA）,BP neural network and RBF neural network method,and the estimate effects of four methods were compared.The results showed that the neural network model combined with hyperspectral remote sensing data could estimate the SCC well.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2011年第5期1318-1321,共4页
Spectroscopy and Spectral Analysis
基金
中国科学院知识创新工程项目(KZCX2-YW-QN305)
国家重点基础研究发展计划专题(2009CB421103)资助
关键词
高光谱遥感
积雪污染物含量
主成分分析
神经网络
Hyperspectral remote sensing
Snow contamination concentration
PCA
Neural network
