Observed data of temperature and precipitation are used to predict Normalization Difference Vegetation index (NDVI) in eight selected areas which is Dongbei, Huabei, Huazhong, Xinan, Huanan, Xibei, Xinjiang and Tibet in China by methods of artificial neural networks and stepwise regression. The climatic factors are 24 months ahead of NDVI. The testing forecast shows that the method of artificial neural networks is much better than that of stepwise regression in prediction of NDVI. Meanwhile above two methods are combined to predict NDVI, that is the factors selected by stepwise regression are used to be the input factors of artificial neural networks in predictions. The result shows that combining the two methods is helpful in improving forecast accuracy.