Based on the national large scale land use/land cover vector data （1∶100000）

we produced 1 km resolution grid cell data set in which each type of land use/land cover is represented as area percentage. From the 1 km grid cell data set

1 km grid data with one unique attribute in each grid is built and then a series of grid data sets with 4 km

8 km and 10 km spatial resolution are derived using maximum area attribution decision law and the centric attribute decision law separately. As to the derived data sets with different scale and scaling up method

we analyzed the distribution of error in area of each type of land use/land cover type. In addition

we explored the relationship between the error in area and physiognomy types. The result shows that: 1) 1 km grid data decrease the real residential area and constructive land （more than 60% of the total real area is lost）

while the forestry land and dry farming land are increased; 2) the difference between real area and the computed area from grid data is compatible with the land use/land cover complicacy; 3) the errors from scaling up data with maximum area law increase when scale increases

while the errors with centric attribute law randomly distributes when scale changes; 4) physiognomy types can impact on the errors distribution significantly when using the maximum attribution law in scaling up. For instance

the area of paddy land and dry farming land in mountain area and hill area will decrease obviously when it is scaled up

which differs significantly from that in other area with other types of physiognomy. However

physiognomy has little effect on the error distribution with centric attribution law when the grid data is scaled up.