Leaf area index (LAI) is one of the important structural parameters of crop population, which could be used to monitor the variety of crop canopy structure and analyze photosynthesis rate. Mapping leaf area index of maize in a large scale by using remote sensing technology is very important for management of fertilizer and water, monitoring growth change and predicting yield. The Beer-Lambert law has been preliminarily applied to develop inversion model of crop LAI, and has achieved good application results. Most of current applications were concentrated in wheat and paddy rice, but less in maize. Because the population characteristics of maize are different from wheat and paddy, mapping maize LAI based on Beer-Lambert law will be helpful to improve the application ability of remote sensing in crop growth monitoring and management of fertilizer and water. In this paper, with the support of in-situ samples and Landsat 8 multispectral image in the tasseling stage, the Beer-Lambert law was used to analyze the influence of reducing solar radiation by the maize canopy structure. The light reduction coefficient was derived from the NDVI of soil samples and maize samples by using the least square method. The inversion model was developed to map the spatial distribution of maize LAI in the study area. The in-situ samples of maize LAI were used to evaluate the accuracy of the model with cross validation. Results showed that the relationship between NDVI of Landsat-8 image and LAI of maize was positively correlated. The determination coefficient of inversion model of maize LAI based on Beer-Lambert law could reach 0.97. The spatial distribution of maize LAI in the study area was consistent with the information of local agricultural management department. These indicated that the Beer-Lambert law could effectively reflect the influence of light reduction from maize canopy structure. It was feasible to map maize LAI by Beer-Lambert law and multispectral image.