Abstract: Abstract: Acquiring high-throughput phenotypic information of crop height and leaf area index (LAI) in the fields rapidly and non-destructively is of great significance for monitoring growth of maize breeding material and predicting maize yield. Currently, phenotypic information of maize breeding material in the fields is obtained by traditional manual investigation, which is an inefficient, time-consuming work, as there are plenty of breeding material plots and there exists a certain degree of human subjectivity. Ultra-low altitude remote sensing data acquisition system based on unmanned aerial vehicle (UAV) platform with different remote sensing micro-sensors can acquire high-throughput crop phenotypic information fastly and non-destructively, overcoming the shortcomings of traditional field phenotypic information acquisition techniques, so it is becoming a research focus in crop phenotypic information technology. In this study, a low-cost UAV remote sensing data acquisition system equipped with a high-resolution digital camera was employed. Field phenotypic data of maize breeding material were acquired at the National Precision Agriculture Research and Demonstration Base in Xiaotangshan Town, Changping District, Beijing City from May to September in 2017. Three-dimensional coordinates of 16 ground control points (GCPs) evenly arranged on the ground were measured by a high-precision differential GPS (global positioning system). The high-resolution digital images of the digital camera were obtained at seedling, jointing, trumpet and anthesis-silking stages of maize. The average heights and LAI of maize in 72 randomly selected breeding plots were acquired almost synchronously with the flight campaigns. High-precision digital surface model (DSM) was produced based on high-resolution digital images of UAV and ground GCPs. Canopy heights of maize breeding material at each growth stage were obtained by calculating the differences of DSM between different growth stages. The maize heights derived from DSM and GCPs were verified in terms of R2, RMSE (root mean square error) and nRMSE, which turned out to be 0.93, 28.69 cm and 17.90% respectively and in high precision. High-resolution digital orthophoto map (DOM) was generated from high-resolution digital images. Average digital number (DN) of R (red), G (green) and B (blue) channels and a total of 15 indices derived from the DOM were calculated such as r, g, b, g/r, g/b, and r/b. The original dataset was composed of digital image variables, maize heights and corresponding LAI. Seventy percentage of the original dataset randomly chosen was used as the modeling dataset and the remaining 30% of the original dataset was used for the model validation. A stepwise regression model was constructed and the precision of it was analyzed taking the combination of maize heights with image-derived indices as the independent variables and merely taking the image-derived indices as the independent variables, separately. The R2, RMSE and nRMSE of estimate model and validation model for LAI were 0.63, 0.40, 26.47% and 0.68, 0.38, 25.51% using r and r/b, respectively, and were 0.69, 0.37, 24.34% and 0.73, 0.35, 23.49% using the combination of maize heights and g and g/b, respectively. Compared with solely using image-derived indices, the result showed that the accuracy of estimate model and validation model can be significantly improved by combining maize heights and image-derived indices. The results show that low-cost UAV remote sensing data acquisition system that includes a UAV remote sensing platform and a high-resolution digital camera can provide a feasible way to monitor canopy height and LAI of maize breeding material, and it proves to be a promising method to rapidly and non-destructively acquire high-throughput phenotypic information of maize breeding material.