期刊
ATMOSPHERIC MEASUREMENT TECHNIQUES
卷 12, 期 11, 页码 6017-6036出版社
COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/amt-12-6017-2019
关键词
-
资金
- NASA [80NSSC17K0569]
We retrieve aerosol optical thickness (AOT) independently for brown carbon, dust and sulfate from hyperspectral image data. The model, a neural network, is trained on atmospheric radiative transfer calculations from MOD-TRAN 6.0 with varying aerosol concentration and type, surface albedo, water vapor, and viewing geometries. From a set of test radiative transfer calculations, we are able to retrieve AOT with a standard error of better than +/- 0:05. No a priori information on the surface albedo or atmospheric state is necessary for our model. We apply the model to AVIRIS-NG imagery from a recent campaign over India and demonstrate its performance under high and low aerosol loadings and different aerosol types.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据