Remote Sensing (RS)
Narges Arab; Abdolrassoul Salmanmahiny; Alireza Mikaeili Tabrizi; Thomas Houet
Abstract
Extended Abstract Introduction:The Land surface temperature is one of the most important factors in controlling the biological, chemical and physical processes of the earth.Land surface temperature data provide information about the spatial and temporal changes of the Land's surface on a global ...
Read More
Extended Abstract Introduction:The Land surface temperature is one of the most important factors in controlling the biological, chemical and physical processes of the earth.Land surface temperature data provide information about the spatial and temporal changes of the Land's surface on a global scale.Land surface temperature is used as the main parameter in many studies, including energy stock estimation, humidity and evapotranspiration, climate change, urban heat islands and environmental studies. Therefore, it is necessary to measure the temperature of the Land surface in order to plan its use. In general, LST investigation is important and necessary to deal with interdisciplinary issues in earth sciences, urban climatology, environmental changes and human-environment interactions. LST can provide important information about surface physical properties as well as climate, which plays a vital role in many environmental processes. In such a situation, LST maps, which are prepared from satellite images, are a desirable option because they provide a permanent data collection.Materials and methods: Many algorithms have been used by researchers to estimate LST using satellite images, especially thermal bands. In this research, Split window and Mono window algorithms are used from Landsat 8 satellite images to obtain land surface temperature (LST) in Mashhad city.The purpose of this study was to investigate the spatial distribution of the Land surface temperature and also to determine an accurate method for preparing the Land surface temperature map.In the present study, using the Split window algorithm, the land surface temperature (LST) data was used from the TIRS sensor in Landsat 8. Also, in addition to TIRS, OLI sensor data are also needed to estimate LST when using the Split window algorithm. In the first stage, the OLI bands of Landsat 8, bands 3, 4 and 5 are layered on top of each other and the NDVI image is produced using bands 4 and 5. The FVC image is obtained using the NDVI image. FVC is calculated by considering the fraction of vegetation in the area. The split window algorithm uses the FVC image to generate the land surface emissivity (LSE) image. The LSE image measures the internal characteristics of the Land surface, which shows the ability to convert thermal energy into radiant energy. Estimating land surface emissivity (LSE) requires soil and vegetation emissivity for bands 10 and 11. LSE images from bands 10 and 11 are obtained separately and then the average and difference of LSE are calculated. The NDVI image is classified into soil and vegetation and is obtained separately for soil and vegetation. Landsat 8 has two TIRS bands. TB, or brightness temperature, is estimated for bands 10 and 11. The thermal calibration process is done by converting thermal digital numerical values (DN) of thermal bands 10 and 11 of the TIRS meter to spectral radiance of the atmosphere (TOA) and then to TB. Finally, LST is estimated using SW, TB, average LSE, LSE difference and water vapor constant.Results and discussion: The results showed that the temperature of the Land surface calculated by the Mono-window and Split-window method compared to the air temperature calculated in the desired weather station showed a difference of 5.1 and 1.7 degrees Celsius respectively. Therefore, it can be said that the Split window method has higher accuracy and the temperature obtained from it is more consistent with the actual temperature.The regression analysis between the results obtained from these two algorithms for LST shows the value of R2 equal to 0.96, as shown in Figure 8.The close correlation between the LST retrieved using the Split window algorithm, with the LST retrieved from the Mono window algorithm, shows that they can be transferred with a small accuracy error.The difference in LST estimation from Mono-window and Split-window algorithms can be attributed to the spectral bands and atmospheric water vapor content used in LST retrieval. The SW algorithm uses two spectral bands (band 10 and 11) with wavelengths of approximately 11 and 12 μm, while the Mono-window algorithm uses one spectral band (band 10) with a wavelength of approximately 11.5 μm to retrieve LST. In addition, the SW Split window algorithm uses the water vapor content of the atmosphere, which represents the true value of the prevailing conditions at the site. On the other hand, the water vapor content of the atmosphere is not used in the Mono window algorithm. The water vapor content of the atmosphere is a sensitive parameter that affects the climate and the temperature of the Land surface. Since two spectral bands are used in the SW algorithm to determine the emission rate and brightness temperature, and these values are used together with the atmospheric water vapor content in LST retrieval, the SW algorithm is able to record the conditions in the region more accurately. and provide better results compared to the Mono window algorithm.Conclusion: The results showed that the air temperature calculated by the Mono window and Split window method compared to the air temperature calculated in the desired weather station shows a difference of 1.7 and 5.1 degrees centigrade on average, respectively. Therefore, it can be said that the Split window method has a higher accuracy and the obtained temperature is more consistent with the actual temperature. The calculated LST values can differ by up to 5 degrees Celsius with the observed air temperature measurement at the station. In the parts covered with greenery, there are low LST values, while in the southeast with barren lands, non-cultivable lands and urban areas, there are high LST values. The results of this research can provide planners and experts with useful information about the temperature status of different regions where the possibility of building weather stations is impossible, and identifying regions with the potential to create thermal islands and its relationship with land use. and provide protection of natural resources.
