The chlorophyll content of crop could indicate the growth status of crop and the spatial model of the chlorophyll content in the field could help to make the decision on the field management. In order to develop a spatial distribution mapping system of winter wheat’s growth parameter, the research was conducted to find a method to establish the model with high accuracy and sparse sample data points. The chlorophyll content and the GPS data were measured in the field. The 67 samples were involved and divided into the calibration and validation samples randomly. Firstly, according to the calibration data, the distribution maps of chlorophyll content were drawn by using inverse distance weighting (IDW) method and ordinary Kriging(OK) method respectively. The fitting data could be extracted at the same GPS position where the validation data located. And then, the accuracy analysis was discussed between the fitting data and the measured validation data. The results show that there is a positive correlation between them based on both IDW and OK map. However, compared with the OK method, the correlation coefficient between IDW fitting and validation data is higher with r is 0.722 and covariance is 1.361.The possible reason caused error in the research was also discussed. It could help to improve the mapping accuracy when the sampling points are unevenly distributed and closed to each other.