关键词: DMSP/OLS, GDP, 长时间序列, 空间关系模型

Abstract: In this study, the DMSP/OLS nighttime satellite data of China from 1992 to 2013 were used to find the relationships between GDP and nighttime satellite data. The nighttime satellite imageries were corrected by mutual correction, saturation correction, and continuity correction based on the invariant target region method. Then the lighting information of Mainland China and 31 provincial regions were extracted and models between GDP and light information, including linear, quadratic polynomial, power function, and exponential regression models, were tested to find the optimal ones. The results are showed as follows. 1) The corrected DMSP/OLS nighttime satellite data are more stable and continuous than the uncorrected data. 2) There is a strong correlation between the corrected DMSP/OLS night light dataset and GDP. 3) Exponential model was the most suitable one for predicting GDP of Mainland China, with the R² value of 0.97 and MARE of 11.32%.4)Provincial models of long time series are better than the annual provincial administrative region models. The exponential function models were optimal for the four municipalities and the top six provincial economic entities, and the quadratic polynomial models were optimal for the other administrative regions, whose R² values are above 0.95 and MARE's are about 10%.

Key words: DMSP/OLS, GDP, long time series, spatial correlation model