Abstract: Leaf area index (LAI) is the main parameter that reflects the status of crop growth. Retrieval of LAI is among the main focuses of quantitative remote sensing in agriculture. Crop spectral information with fine spatial resolution obtained by an Unmanned Aerial Vehicle (UAV) remote sensing monitoring system is used for estimating leaf area, which is important for precision agricultural production and management. In our study, an agricultural UAV remote sensing monitoring system was established based on a multi-rotor UAV with both Canon PowerShot G16 digital camera and ADC-Lite multispectral sensor mounted on the same platform. Based on this system, imageries were acquired over a soybean experimental field in Jiaxiang County of Shandong Province at podding and seed-filling stages. Five vegetation indices ratio vegetation index (RVI), normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), difference vegetation index (DVI) and triangle vegetation index (TVI) were calculated from the data. Together with measured LAI, both the univariate and multivariate empirical models were calibrated for estimating LAI of soybean. The best LAI retrieving models were identified based on best combinations of coefficient of determination (R₂), root mean square error (RMSE) and estimated accuracy (E_A). It was noted that there was the need to choose the best crop growth period for retrieving LAI. LAI was estimable at higher accuracy at seed-filling than at podding stage. Linear regression model of NDVI most accurately explained retrieval of LAI of soybean, with R₂ = 0.829, RMSE = 0.301 and E_A = 85.4%. NDVI linear regression model was therefore recommended as the most legible model for estimating LAI of soybean at seed-filling stage in this study area. The model was also recommended for application in mapping the LAI of soybean at seed-filling stage. According to our validation data, LAI map well reflected real-world spatial distribution pattern of LAI in soybean fields. The established agricultural UAV remote sensing monitoring system provided novel insights in guiding precision agriculture applications and the corresponding retrieval models for studying the feasibility of retrieving LAI.