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摘要： 太阳辐射数据是农作物模型、水文模型及气候变化模型等的重要参数，但是，全国范围内太阳辐射站点的分布有限，太阳辐射数据的缺乏制约着相关模型的发展，而神经网络对太阳辐射的估算精度较高，现有的神经网络模拟太阳辐射的模型很少考虑气溶胶、云、水汽对太阳辐射的影响，基于MODIS提供的气溶胶、云、水汽高空大气遥感产品和常规气象站点资料，输入LM-BP神经网络模型模拟了华东地区90个常规气象站点2001~2014年的太阳辐射月均值。验证结果表明：该模型的拟合优度达到0.95，均方根误差基本控制在2 MJ·m-2以内，平均偏离误差基本在-1 MJ·m-2至1 MJ·m-2之间。最后，利用模型的模拟值，结合13个辐射站点的实测值，通过空间插值得到华东地区2001~2014年年均太阳辐射的精细化空间分布图。   关键词： 太阳辐射;  神经网络;  MODIS;  云;  气溶胶;  水汽;  时空变化     Abstract: Solar radiation data are important parameters of crop model，hydrological model and climate change model，however，the distribution of solar radiation sites is scarce and uneven throughout the country，and it is difficult to obtain spatial continuous solar radiation by using only rare radia.Therefore，the lack of solar radiation data restricts the development of the relevant model，and the neural network on the solar radiation has a good predictability，many Artificial Neural Network ensemble models were developed to estimate solar radiation using routinely measured meteorolological variables，but it did not consider cloud，aerosol，and precipitable water vapor influence on solar radiation.In this paper，we used cloud，aerosols，atmospheric precipitable water vapor from MODIS atmosphere remote sensing products and conventional meteorological data including air pressure，temperature，sunshine duration and latitude and elevation，based on the LM-BP neural network model to simulate the 90 conventional weather stations in Eastern China from 2001 to 2014.The results show that the model has a good fit of 0.95，and the root mean square error is controlled within 2 MJ·m-2.The average deviation error is between -1 MJ·m-2 and 1 MJ·m-2.Finally，using the simulated values of the model and the measured values of 13 radiation sites，the spatial distribution of the annual solar radiation in the East China region from 2001 to 2014 is obtained by spatial interpolation and the spatial variation trend is analyzed. 

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