Here, we try to realize the spatialization of the added values of the secondary and tertiary industries in Fujian province, by adding thermal infrared remote sensing data to land use data and night light remote sensing data, and considering surface temperature as well.The results show that compared with previous method of using land use data plus night light remote sensing data, the adding of thermal infrared remote sensing data and consideration of surface temperature improves the GDP spatialization model in coefficients of determination (R², 0.966 vs. 0.743, 0.870 vs. 0.776 for the secondary and tertiary industry, respectively) and simulation accuracy (MRE, 20.45% vs. 72.60%, 19.82% vs. 60.10%, for the secondary and tertiary industry, respectively).Further, we take Xiamen as an example to show the potential of thermal infrared remote sensing data in GDP spatialization model.It is found that the proposed method can greatly improve the spatialization of the added values of the secondary and tertiary industries, indicated by its high consistency with reality.The results of this paper can provide reference for regional economic development planning.