Normalizing land surface temperature for environmental parameters in mountainous and urban areas of a cold semi-arid climate.

Normalization of land surface temperature (LST) relative to environmental factors is of great importance in many scientific studies and applications. The purpose of this study was to develop physical models based on energy balance equations for normalization of satellite derived LST relative to environmental parameters. For this purpose, a set of remote sensing imagery, meteorological and climatic data recorded in synoptic stations, and soil temperatures measured by data loggers were used. For modeling and normalization of LST, a dual-source energy balance model (dual-EB), taking into account two fractions of vegetation and soil, and a triple -source energy balance model (triple-EB), taking into account three fractions of vegetation, soil and built-up land, were proposed with either regional or local optimization strategies. To evaluate and compare the accuracy of different modeling results, correlation coefficients and root mean square difference (RMSE) were computed between modeled LST and LST obtained from satellite imagery, as well as between modeled LST and soil temperature measured by data loggers. Further, the variance of normalized LST values was calculated and analyzed. The results suggested that the use of local optimization strategy increased the accuracy of the normalization of LST, compared to the regional optimization strategy. In addition, no matter the regional or local optimization strategy was employed, the triple-EB model out-performed consistently the dual-EB model for LST normalization. The results show the efficiency of the local triple-EB model to normalize LST relative to environmental parameters. The correlation coefficients were close to zero between all of the environmental parameters and the normalized LST. In other words, normalized LST was completely independent of the environmental parameters considered by this research.