Abstract: Abstract: Crop identification with high resolution satellite imagery relates to four key factors: 1) crop phenologies, which lead to the similarity of plant reflectance of different crops; 2) the high resolution, which leads to field-to-field variability of plant reflectance of the same crops; 3) performances of various classifiers, which directly restrict crop identification accuracy; 4) feature variables, which reflect spatial and spectral variability within fields. Considering restrictions by these factors, a geostatistical approach to bias calibration of crop identification is proposed, in order to make full use of spatial structure information in high resolution satellite imagery to further improve classification accuracy of some stable crops like single-cropping rice and late rice.Taking into account spectral characteristics and the spatial structure information of crops, the proposal method is based on the techniques of variogram and Kriging algorithms from geostatistics. First, the differences between indicator vectors and posterior category probability vectors for the training samples were calculated to quantify the biases of category memberships before and after the spectral classification. Then, the generated biases were regarded as regionalized variables, and some kind of experimental variogram models were selected to biases modeling, and the method of simple Kriging with local means was used to predict the biases for all pixels. Finally, the predicted biases were added to the posterior category probabilities derived from the initial spectral classification to obtain the calibration results, and the process of bias calibration of crop identification was then found.A SPOT 5 image acquired in September 2012 with four spectral bands and 10-m pixel size covering intensively cropped areas in south Anhui province was used for crop identification. Two subset images that covered Congyang county and Guichi county with the same area of 100km2 were also generated from the original image as the study area and verification area, respectively. A support vector machine classifier was used to get the spectral classification and posterior category probabilities. Ground truth data were collected and used to evaluate the calibration effects, and the transformations between category memberships before and after calibration were analyzed, with respect to the two major crops. Comparing with the direct spectral classification results generated from methods of maximum likelihood classification, fuzzy classification and support vector machine classification, the overall accuracy of the calibration method increased by nearly 14%, and was always able to achieve above 90%. Moreover, there were some substantial increases in the producer's accuracy and the user's accuracy of single-cropping rice and late rice, with the precision of increase more than 30%, effectively improved the identification accuracy of rice in the research region. Therefore, it is illustrated that the proposal method overcomes the limitations due to spectrum characteristics and a similar operation can usually be implemented for crop identification.Based on the direct spectral classification, the proposal method focused on biases of the category memberships of several major crops, and considered the structural and the random characteristics of the posterior category probabilities due to the spatial distribution of categories in the local regions, and thus is independent of the limitation of spectral similarities of some crops. With regard to the operation processes, further improvement upon the calculation of parameters of variogram models will be a future concern, and the optimal sampling strategy will be studied more, considering the spatial distribution of the samples.