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Abstract:
Land surface temperature (LST) is the main factor in the energy balance of the Earth, and is used as input for climate and environmental changes models. In the past and even now, due to the lack or insufficient number meteorological stations, temperature has not been measured and recorded in many parts of the country, therefore, there is no accurate information on temperature changes in these regions. So there is a need for a method to measure this parameter that is fast and accurate as well as being the low cost and covering a wide area. Using remote sensing data due to its wide coverage, the availability of many images and rich archives is a good option for estimating this parameter. In this research, various methods for extracting land surface temperature including Mono-window, Artis and Stefan-Boltzman were investigated. In each method, emissivity was calculated from different ways, including NDVI, Classification and MODIS Product, and then entered into their equations. Landsat series including TM, ETM +, OLI and MODIS emissivity products were also used. To convert the surface temperature to  one and half meters height temperature, a precise environmental thermometer with a precision of 0.2 ° C and a linear relationship (air temperature= 0.44 × LST + 8.8) were used. Then, the results were tested using weather station data  via RMSE and t-test. The final results showed that the best method with RMSE 1.09 is using the ETM + image and the Stefan-Boltzman method, and the worst method with RMSE 2.64 is belonging to the use the MODIS image and the Mono-Window method. Therefore, it is recommended to use Stefan-Boltzman method for other regions which are geographically similar to this studied area.

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Earthquake is one of the most important natural disasters that annually causes a lot of damage to human and natural communities. These damages have always led researchers to look for a way to predict this phenomenon. These include using satellite images and analyzing them. Since technology has not yet been invented to accurately predict the location and time of an earthquake, it is very useful to examine the information and analysis of past earthquakes to better understand earthquake-related phenomena. In this study, thermal investigation of Bam fault, using ASTER and MODIS images in December 2003 earthquake Singel-channel algorithm and web coding in Google Earth engine, the heat trend was calculated for six months before and after the earthquake. This process was also monitored using hourly weather data from the Meteorological Organization near the study site to obtain a better view of the thermal status of the area. The results showed that contrary to existing beliefs, the earthquake had no detectable thermal effect with the ASTER and MODIS satellite images on the Earthchr('39')s surface.