文章基本信息

标题：Estimation of Atmospheric Temperature and Humidity Profiles from MODIS and Radiosond Data Using Artificial Neural Network
本地全文：下载
作者：V. Akbari ; J. Amini ; M. R. Saradjian 等
期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B7
页码：35-40
出版社：Copernicus Publications
摘要：The aim of this study is to test the quality of the neural network for retrieving the temperature and humidity by comparison with the radiosond values and a linear regression method. Remote sensed images give useful information about the atmosphere. In this article, MODIS data is used to retrieve temperature and humidity profiles of the atmosphere. Two methods of linear regression and artificial neural network are used to retrieve the temperature and humidity profiles. A multilayer feed-forward neural network is tested to estimate the desired geophysical profiles. Retrievals are validated by comparison with coincident radiosond profiles
关键词：Neural Network; Humidity; Temperature; Linear Regression; Radiosond