The traditional ground area measurement is limited by accuracy and efficiency.Remote sensing technology helps improve both but alone still far from satisfaction.Therefore

we proposed a spatial stratified sampling method based on remote sensing.The basic idea is using up to date remote sensing images to guide the target stratified sampling by a general classification

other than by those obsolete background knowledge databases.In order to validate the improvement on accuracy of calculating the real quantity with actually it unknown（which is just our goal to estimate）

we took the early classification map from the remote sensing image（in this experiment

we used part of one scene of TM image covering Beijing area

a size of 4800×4800 pixels

and classified into 5 different types

3 of which were chosen）as the laboratorial quasi-real target（proportions vary from 7% to 32%）.The detail methods and operations are described in the following steps: Firstly

we played back simulated pre-classification result

which completely contains the target

by iteratively adding error class pixels around the outskirts of the target to a demanded proportion;secondly.Secondly

we arranged square boxes（with a size of 20×20 pixels）on the pre-classification image

excluded zero-target ones and divided the rest into 5 strata according to the proportion of in-box pre-classification target area（pixels）

randomly or systematically chose the samples.And then we estimated the gross by summing up the actual pixels in each sample pro rata.Finally

we analyzed and compared the quality and variation of estimation accuracy repetitiously with different land cover types

different pre-classification precision levels

and two methods of random and system in stratum.The results mainly presented the relationship between estimation accuracy and pre-classification accuracy in each target type

which showed that the estimation accuracy degraded when the stratified sampling method was aided with rough pre-classifications（accuracy less than 40%）

but remarkably reached higher accuracy and stabilization than those of unsupported random or systematic sampling methods with pre-classification above a certain accuracy level.For the former situation

the degradation is mainly caused by the extreme inconsistency of area distributing direction poorly classified

and does not take place in common classifications.In general

this method has its best cost-efficiency at 50% pre-classification accuracy

a case of which is that the accuracy of the estimators for each target class with all proportions at 0.5% sampling ratio level and 95% confidence level can be acquired above 92% when the accuracy of pre-classification reaches 60%.During the study

we innovated in the following aspects: first

we created a simulated classification map with an assumed given target

and this map worked properly to show up the real situation;second

with the help of pre-classification from remote sensing images

the stratified sampling method can be much more effective and precise

and less in need of prior knowledge.