A vegetation index of corn canopy chlorophyll by vehicleborne dynamic prediction at seedling stage

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 small regions, but the effect of monitoring is not steady. The background interference on the reflectance will not be suppressed, especially at seeding stage. In order to overcome this problem, a novel spectral index named MPRI (mobil photo chemical reflectance index) was developed in this paper. The results indicated that it had satisfactory prediction accuracy on the corn canopy chlorophyll content at seeding stage by using MPRI, with the average R2 of 0.81. The MPRI showed great potential application in the spatial distribution with inverse distance weighted as well. The amount of data with deviation rate between predication and real value under 7% was about 85% of the entire data. The theoretical analysis and test results proved that the spectral index -MPRI had the characteristics of estimating corn growth by traverse measurement system at seeding stage. It also showed the good effect on solving the dynamic crop growth predication with severe background interference