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Soil background has an impact on the accurate estimation of maize leaf area index(LAI), and the traditional soil background removal method eliminates the area information of soil pixels thus resulting in a lower correlation between the target area spectrum and maize LAI. Therefore, a soil background removal method was proposed, which removed the spectral reflectance of soil pixels while retaining the area information of soil pixels. Based on this method, the multispectral image was preprocessed and 26 vegetation indices such as normalized difference vegetation index (NDVI) were extracted along with eight texture features such as Mean. Combined with crop growth covariates such as plant height/chlorophyll content, the above three different types of features were arranged and combined to form multiple input feature sets, and eight modeling algorithms were used to build multiple LAI estimation models, which were compared with those based on traditional soil background removal methods. The results showed that the soil background removal method proposed effectively eliminated the effect of soil spectral reflectance on vegetation spectral reflectance under the premise of retaining the area information of soil pixels and vegetation pixels, and the LAI estimation models based on this method were better than the traditional methods; the fusion of multiple types of features can improve the model estimation accuracy of LAI from multispectral images, and the estimation effect of texture features on LAI was better than that of the vegetation index; the machine learning model was better than the traditional statistical regression algorithm for LAI estimation, and the optimal model was the one-dimensional convolutional neural network (1D-CNN) model with vegetation index + texture features + plant height/chlorophyll content as inputs, which was pre-processed with the soil background processing method proposed. 1D-CNN model with testing set adjust coefficient of determination R2Adj, root mean square error （RMSE）, and mean absolute error (MAE） of 0.9515, 0.2421, and 0.1795, respectively. The research result may provide a method for rapid and accurate estimation of maize LAI.