Abstract: 【Objective】 Light temperature/climate productive potentiality as one of the important index for farmland classification, which directly affects the accuracy of the classification results. In theory, light and temperature conditions should vary in different terrain regions, but existing productive potentiality value that one county, one crop just owns one value can’t accurately reflect the differences of productive potentiality when the terrain differences apparent in the county, which leads to the classification results can’t accurately describe the differences of the cultivated land quality. The objective of this study is to solve this problem.【Method】 Based on terrain had serious relationship with the light, temperature and precipitation which were closely related to productive potentiality, this paper proposes to find the relationship between terrain factor and productive potentiality using the relationship to correct the value of productive potentiality. As productive potentiality was calculated based on a national scale data, in order to ensure the comparability of revised productive potentiality value, this paper carried out correction in national scale and used 900 m × 900 m DEM data as data source of calculating terrain factors. Firstly, by SPSS software, regression analysis was done between altitude, gradient, aspect and productive potentiality respectively, then the highest correlation regression model was screened to reflect their relationships. Secondly, the regression equation, county average terrain values, average productive potentiality and the terrain values of correcting area were used to get correction formula for productive potentiality. Finally, the correlation coefficients of different terrain factors and productive potentiality were used as weights to weight the values of each corrected productive potentiality value by single factor to get the comprehensive correction productive potentiality value.【Result】 This paper did regression analysis using the data productive potentiality value at the current farmland classification and the DEM data. There were 3 779 samples participated in correcting light temperature productive potentiality and 2 765 samples participated in correcting climate productive potentiality. Regression analysis results showed that the correlation coefficient between light temperature productive potentiality and gradient was 0.0008 and the correlation coefficient between light temperature productive potentiality and aspect was 0.0002. This proved that when 900 m × 900 m DEM data were used as data source to calculate gradient and aspect in national scale, both gradient and aspect almost had no correlation with productive potentiality. On the other hand, correlation coefficient was 0.835 between altitude and light temperature productive potentiality, and the value of correlation coefficient between climate productive potentiality and altitude was 0.721, which meant there was high correlation between altitude and productive potentiality. According to the regression equation between altitude and productive potentiality, the influence coefficient of altitude to the light temperature productive potentiality was 1.479, to the climate productive potentiality was 1.095. Changning County in Sichuan Province was used as a case example verification. The results showed that the revised production potential value had the same trend as elevation of the trend, which reflected that terrain impacted light and temperature conditions, and the more elevation deviating from average elevation was, the greater the revised productive potentiality was different from average productive potentiality.【Conclusion】In national scale, altitude has an important effect for productive potentiality, and the impact for light temperature productive potentiality is greater than climate productive potentiality. On the contrary, both gradient and aspect have no obvious correlation with productive potentiality in national scale. Based on the data limitation, the purpose of this paper is to focus on discussion of mathematical model method and the fixed thought, there is still a certain distance with the practical application, in the future study, we can use more detailed data to analyze the impacts of gradient and aspect to productive potentiality in local area on the premise using the national control of comparability.

Key words: light temperature productive potentiality, climate productive potentiality, altitude, gradient, aspect