Multi-layer perceptron neural network based algorithm for estimating precipitable water vapour from MODIS NIR data
文献类型：EI期刊论文
| 作者 | Li Z.; Li Z.; Wang W. |
| 发表日期 | 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
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| 卷 | 27期:3页:617-621 |
| 收录类别 | EI |
| 语种 | 英语 |
| 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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