Study on the Coupling Relationship between Land Surface Composition and Land Surface Temperature Based on 3S Technology

With rapid urban growth in recent years, the understanding of urban biophysical composition and dynamics has become an important research topic. Land surface temperature (LST) is a more important parameter in studying the material exchange and energy exchange between the land and the atmosphere, a key parameter of physical course of the earth top layer in the area or global scale, an important index of monitoring the earth resource and studying the ecological environment system of the land surface, it has very important function in many aspects, such as climate, hydrology, ecology, environment and military affairs. The new space technology with 3S (GIS, GPS, RS) has offered a technological measure in evaluating and monitoring the land surface composition and LST. GIS technologies can offer analysis tools; GPS technologies can provide accurate location information; RS technologies can provide accurate spectral information. RS technologies constitute a potentially scientific basis for examining urban composition and monitoring its changes over the time. The vegetation-impervious surface-soil-water (V-I-SW) model in particular provides a foundation for describing urban/suburban environments and also serves as a basis for further urban analyses comprising urban growth modeling, environmental impact analysis and socioeconomic factor estimation. This paper developed a normalized spectral mixture analysis(NSMA) method for examining urban composition in Zhengzhou area using Landsat ETM+ data. In particular brightness normalization method was applied to reduce brightness variation. Through this normalization brightness variability within each V-I-S-W component was reduced or eliminated, thus allowing a single end-member to represent each component. Furthermore, with the normalized image four endmembers (namely vegetation, impervious surface, soil and water) were chosen to model heterogeneous urban composition using a constrained spectral mixture analysis(SMA) model. The accuracy of impervious surface estimation was assessed and compared with the other existing models. The results indicate that the proposed model is a better alternative to existing models with a lower root mean square error for impervious surface estimation in the study area. The transformed ETM+ images were unmixed into four fraction images (vegetation, impervious surface, water and soil) with a constrained least-square solution. This paper managed mono-window algorithm to retrieve land surface temperature based on the Landsat ETM+ hot infrared image and the meteorological data, thus gained the land surface temperature image. Finally, it received the conclusion by analyzing the coupling relationship between land surface composition and LST: the vegetation-LST relationship was middle negative correlation. The relationship of water and LST was also middle negative correlation. The relationship of impervious surface and LST was the positive correlation, but the correlation coefficient was quite low. The relationship of soil and LST was high negative correlation.