More and more attention has been paid to the best use of medium and high resolution images and statistical data, combined with low resolution images on crop area estimation. However, information abstraction with high and medium resolution images also has many uncertainties due to factors such as spectral difference within classes, spectral similarity between classes, and the mixed pixels. This paper presents a method for crop area estimation with high and medium resolution images based on statistical sampling and amount controlling. Firstly, sample units are obtained by stratified sampling. Then sampling units are interpreted, and the estimator of crop planting acreage is extrapolated. Finally the spatial distribution mapping is classified and refined under the restriction derived from sampling estimator. Moreover, we validate the method presented above by using a SPOT-5 subset image (with resolution of 10m, August 21, 2006) of Sanhe, Hebei province. The results indicate that the overall accuracy of the new method is 93.8%, with kappa 0.88, based on cluster samples, which is higher than that of MLC method. The new method has promising practicality and popularity in large-cover measurement of crop planting acreage.
