Multi-layer perceptron neural network based algorithm for estimating precipitable water vapour from MODIS NIR data

	Multi-layer perceptron neural network based algorithm for estimating precipitable water vapour from MODIS NIR data
	Li Z.; Li Z.; Wang W.
	2006
关键词	Algorithms Estimation Neural networks Precipitation (chemical) Vapors Water
英文摘要	This Letter presents a multi-layer perceptron neural network (MLP-NN) based algorithm to quantitatively determine precipitable water vapour (PWV) directly from near infrared (NIR) radiance measured by the Moderate Resolution Imaging Spectroradiometer (MODIS). First, the background of the MLP-NN based algorithm is discussed briefly. Then, the radiance of MODIS NIR channels simulated through a radiative transfer model with a set of input variables covering a broad range of surface reflectance and water vapour content are used to train MLP-NN. Finally, PWV values derived by the MLP-NN based algorithm are compared with radiosonde observations and a root mean squared error of 5.2 kg m^-2 is found from this comparison. © 2006 Taylor & Francis.
出处	International Journal of Remote Sensing
卷	27 期:3 页:617-621
收录类别	EI
语种	英语
内容类型	EI期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/24534]
专题	地理科学与资源研究所_历年回溯文献
推荐引用方式 GB/T 7714	Li Z.,Li Z.,Wang W.. Multi-layer perceptron neural network based algorithm for estimating precipitable water vapour from MODIS NIR data. 2006.