Chlorophyll is an important indicator for the evaluation of plant photosynthesis ability and growth status. In order to obtain the spatial distribution of chlorophyll content in field crops quickly and nondestructively, the chlorophyll content detection and distribution map drawing method of maize canopy were carried out based on UAV remote sensing technology. Firstly, the aerial images of 150 maize plots were collected by UAV mounted camera and spliced by Pix4dmapper software. Totally 80 maize leaves were sampled in the experimental field. They were processed following chemical extraction and spectrophotometer measurement to obtain the chlorophyll content value. The images and chlorophyll data were used to form the underlying data source. In the aspects of data processing, the position and orientation system (POS) data of the sample points were matched with the images of the UAV using ArcGIS software. For the RGB images captured by the drone, the threechannel component values of R, G and B were firstly extracted. The color feature parameters were calculated such as greenred difference, normalized redgreen difference, super green, and so on. In addition, six kinds of texture features were calculated, including mean, standard deviation, smoothness and thirdorder moment. The error back propagation neural network was used to build chlorophyll detection model for maize canopy leaves. The experimental results were as follows: the root mean square error (RMSE) of the maize canopy chlorophyll content detecting model based on BP neural network was 4.4659mg/L, and the coefficient of determination R2 was 0.7246. The chlorophyll content of each pixel in the field canopy image was calculated. The visual distribution map of chlorophyll content in field maize canopy was drawn based on pseudocolor technique. The chlorophyll content distribution map of field maize canopy could be used to visually distinguish the field road and canopy area, showing the difference in chlorophyll distribution of the plot. By nondestructively detecting the chlorophyll content and chlorophyll distribution of canopy corn canopy, it could provide a support for field crop growth evaluation and precision management.