Abstract: Abstract: Crop growth diagnosis or evaluation mainly relies on field survey, manual sampling and biochemical analysis in the laboratory. It is difficult to master the real spatial variance characteristics for the whole crop field because sample collection is restricted to the manpower and material resources, as well as the time consumption of data analysis in the laboratory. Remote sensing technology provides an opportunity for spatial variance monitoring of crop growth with its rapid development in recently years. In this study, the remote sensing image of the QuickBird with a high spatial resolution acquired on May 2nd, 2006 was used to analyze the spatial heterogeneity characteristics of winter wheat from different fields. Firstly, the coarse and precise geometric corrections were carried out by ground control points (GCP) and difference global positioning system (DGPS), respectively. Then, atmospheric correction was processed using the 'empirical line method' (ELM) based on ground spectral measurements. After the geometric and atmospheric corrections, a pan-sharpening process was applied to the QuickBird's four multi-spectral bands by using the pan band. Then the normalized difference vegetation index (NDVI) image was calculated based on the QuickBird images in Band 3 and Band 4. Six winter wheat fields were selected for the spatial heterogeneity analysis through the geo-statistics method. The empirical semi-variance function was established based on the NDVI values of the pairs of pixels within the range of 0.6 meter to 27 meters in the directions vertical to ridge and parallel to ridge in all six fields. Then semi-variograms were fitted with Spherical model, Exponential model and Gaussian model, respectively. The optimization model was then selected after evaluated by the SSE (sum of squares due to error) and R2 (determination coefficient) through the method of maximum likelihood. Three parameters for semi-variogram model, sill, range and nugget were calculated for all six fields in two directions using the least squares algorithm. Meanwhile, the statistical parameters for winter wheat's NDVI image, including the values of minimum, maximum, mean, standard deviation and coefficient of variance (CV), as well as the image texture parameters, including the data range, data variance and data entropy were calculated for all the fields. The NDVI coverage information with different value range was also used in this study. After that, the relationships between NDVI semi-variogram parameters and NDVI statistical information, texture information, and NDVI coverage information were analyzed, respectively. The results indicated that NDVI's spatial semi-variogram showed an obvious sill pattern for wheat field. The value of sill in the direction vertical to ridge was higher than that in the direction parallel to ridge for the same field. And the range and nugget values in the two directions were also different for the same field. It can be concluded that the wheat growth shows the zonal anisotropy. The results revealed that the sill values in the directions vertical to ridge and parallel to ridge were both related to the NDVI texture range, texture variance and the NDVI coverage value. While the NDVI range was related to the NDVI mean value, CV value and the coverage of pixels with NDVI value less than 0.30 and 0.40 in the field in the direction vertical to ridge. The NDVI nugget was related to the NDVI mean value, CV value and the coverage of pixels with NDVI value less than 0.30 and 0.40 in the field in the direction vertical to ridge. But the range and nugget were irrelevant to any factor in the direction parallel to ridge. This study indicates that remote sensing technique can provide an effective new method for the study on spatial heterogeneity of crop growth.