Abstract: Abstract: Using near-ground remote sensing is significant to understanding the growth of crops and providing accurate and scientific data for precision agriculture. The vehicle-borne system is one of the most important tools for growth monitoring and management. It is an efficient, flexible and economical operation for the small region. However, the vehicle-borne growth monitoring system cannot maintain steady operations due to the row spacing of corn. The background interference on the reflectance will not be suppressed effectively, which will result in a deviation in the growth monitoring. In order to overcome this problem, a new method was developed in this paper, which contains matrix transformation and GIS analysis. 　　In order to obtain the experimental data, the tests were carried out by the vehicle-borne system on the cornfield. The vehicle-borne system collected the reflectance data of the corn canopy with the sensors at a sampling rate of 1 point per second. The GPS receiver obtained the location information as the same rate. All information was formulated in a matrix at each experiment. Then, each data set of the matrix was combined by the information of GPS and canopy reflectance. The spatial interpolation methods of Inverse Distance Weighted (IDW) and Kriging were utilized for comparison study on the matrix. It overcomes the shortcomings of the large deviation resulting for the background interference. By dealing with neural network analysis between reflectance and chlorophyll content, the results analysis from the matrix can show the corn growth in some specified region.　　The results indicated that: It has satisfactory forecasting accuracy on the chlorophyll content by using the BP neural network model and RBF neural network model, with average R2 of 0.8. By focusing on the optimization of the spatial data distribution obtained by vehicle, it was proposed that the matrix of results, which was predicted by RBF neural network, was transformed with inverse distance weighted (IDW). It was under 10% that the deviation rate between the predication and real value was the majority, which was about 85% of the entire data.　　The theoretical analysis and test results prove that the method of combining spatial analysis, neural network and matrix transformation has the characteristics of estimating the corn growth by the traverse measurement system. It also showed the good effect on solving the dynamic crop growth predication with severe background interference.