split window algorithm

Land surface temperature (LST) is an important factor in the study of global warming and climate change. Natural and man-made activities, especially land use and land cover changes, have a significant effect on land surface temperature. The purpose of this study is to estimate the surface temperature using Split window algorithm in Tabriz and determine its relationship with land use changes. For this purpose, OLI and TIRS images of Landsat 8 in 2013 and 2019 were used and after performing various pre-processing stages, vegetation index (NDVI), water extraction index (MNDWI), urban development index (NDBI) and land surface temperature were estimated using multispectral and thermal bands. Land use maps were prepared using object-oriented classification method in eCognition software and then accuracy assessment coefficients were extracted. The results of this study showed that the most land use changes are observed in rangeland use, which in the 7-year period, most of it has changed to road and urban land use. The results also show a negative correlation between NDVI and surface temperature and the highest temperature occurs in areas with poor vegetation. Therefore, considering the role of vegetation in modulating temperature conditions, the need for vegetation protection seems necessary.

The purpose of the present study is to assess the land surface temperature in relation to landuse for the city of Tabriz using remote sensing technology and GIS. Landsat 8 satellite image was used to map the land surface temperature for the study area. Atmospheric correction was applied to the desired image using the FLAASH method and the land surface temperature was estimated using the split-window algorithm for the study area with an accuracy of 1.51 degrees. Landuse map of Tabriz city in 6 classes was obtained using the object-based approach in eCognition software with an accuracy of 90/03. The results of studying the relationship between land surface temperature and landuse indicate that agricultural lands with a temperature of 18.22 °C have the highest land surface temperature. Also, water areas (rivers) have the lowest (10.30 °C) land surface temperature, because of their radiant power close to one. The research results also indicate that the split-window algorithm provides reliable results for land surface temperature estimation that can be used in environmental studies and earth sciences.

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.

 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.

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.

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.

The land surface temperature is the highest layer of the earth's surface and depends on the level of surface emission, vegetation and the types of ground cover. Earth's surface temperature provides important information about the physical characteristics of the Earth's surface from local and global scales, and plays an important role in many applications. From Earth's surface temperature to study water resources management, agriculture, resource management, drought, environmental geochemical processing, meteorological research, global changes in land surface temperature, weather forecasting, hydrology, ecology, plant status survey, urban climate, studies Environmental and geophysical variables such as evaporation-transpiration and soil moisture are used. In general, temperature measurements at ground level are performed by meteorological stations, including synoptic and climatological. It should be noted that meteorological stations are only able to measure the temperature at specific points that have already been installed there. What is considered to be a major flaw in ground temperature monitoring is the lack of sufficient meteorological stations to know the temperature values in stations without stations, which have been partially remedied today by remote sensing technology. Earth's surface temperature is one of the most important components in global studies, which is used as one of the important factors in controlling the biological, chemical and physical processes of the earth.

Moghan plain is located in the northwest of Iran and is subject to Ardabil province and is located in the northern part of the province. ParsAbad Moghan city is a relatively large plain with an area of 143494 hectares, which occupies about 14% of Ardabil area and is the northernmost city of the province. The data used in this study included the Landsat 8 satellite image, which used its OLI sensor to extract land use maps and its TIRS gauge used to extract the Earth's surface temperature for 2018, as well as the ETM + Landsat imager to prepare land use maps. Using visible and infrared bands and surface temperature using thermal bands for 2002. The city's meteorological data were also used to check the temperature recorded by the stations. The ENVI 5.3 software was used for atmospheric and radiometric corrections, and the ARC GIS 10.5 software was used to extract the relevant maps. In order to classify land use, object-oriented classification method was used in eCognition Developer64 software. In object-oriented classification, spectral information is integrated with spatial information, and pixels are segmented based on the shape, texture, and gray tone of the image at a specific scale, and image classification is based on these components.

The largest area in 2002 belongs to the rangeland class with an area of 58,138 hectares. The second area belongs to the dryland agricultural class, which has the largest area with 52369 hectares. The smallest area belongs to the use of water with 543 hectares. For 2018, rainfed agriculture had the highest area with 41906 and then pasture with 30943 had the highest area. Looking at the uses of 2018, the results show a significant difference that the use of soil has increased from 11143 in 2002 to 30943 in 2018, as well as the use of irrigated agriculture and residential areas and forests. However, the use of irrigated areas has decreased significantly from 543 hectares to 262 hectares, and the use of rainfed and rangeland agriculture has also decreased compared to 2002. The water temperature during 2018 was almost constant and did not differ significantly, and the lowest temperature in both years is 31 degrees. Due to the fact that water has a high heat capacity, water has a lower surface temperature to the soil has the highest temperature in both years with 40.80 for 2002 and 42.92 for 2018. Aquaculture in 2003 was 33.12. C, which in 2018 increased to 34.41. C. The rangeland use has had a high temperature in both years of study and has increased from 38.22 in 2002 to 39.26 in 2018.

In this research, in the first step, in order to classify and then examine the changes that occurred in a certain period of time in the city of ParsAbad, action was taken. For this purpose, in the first stage of this research, in order to classify and record changes in a 16-year period, object-oriented images were classified in eCognition software and in ArcGIS10.5 software, extraction maps were extracted. Was. The classification accuracy in 2000 has a total accuracy of 0.90 and a coefficient of 0.87. While the classification in 2018 with an overall accuracy of 92% and a Cape coefficient of 0.90 has provided a relatively higher accuracy. After classification, an attempt was made to examine the changes that occurred in the region over a 16-year period, and a map of land use changes was drawn up for the area under study. Land surface temperature is one of the main parameters in the study of cities. Because it is almost the same as the air temperature in the lower layers of the city, which is the energy balance of the surface and determines the climate between the buildings and affects the life and comfort of the urban residents. Soil use has the highest temperature in both years with 40.80 for 2002 and 42.92 for 2018. Also noteworthy about residential areas is that residential use has increased from 34/51 in 2002 to 40/09 in 2018, which indicates that in 2018, with the expansion of the city compared to 16 years ago, the heat concentration will also increase. Increased. This use is due to the presence of man-made and heat-absorbing factors such as asphalt, concrete, the existence of various machinery and factories, as well as the creation of tall buildings that prevent heat from escaping around and prevent wind from moving into the city.

Urban heat islands in hot and dry climates have adverse effects on the environment and human health. In this study, a method has been proposed to investigate the factors affecting the heat islands of Iran's central plateau climate. In the first step, after applying geometric, radiometric, atmospheric corrections and preparing Landsat 8 satellite images, including OLI-TIRS sensors, Tasseled Cap transformation is created. In the second step, the surface temperature of the earth is extracted using Split window algorithm. In the third step, in order to evaluate the heat islands, the Urban Thermal Field Variance Index is classified into six levels. Finally, using the correlation coefficient between TCT and Urban Thermal Field Variance Index indicators, the relationship of heat islands with the desert, urban areas, vegetation, and humidity is evaluated. In order to evaluate the proposed method, the city of Qom has been studied. The results of the proposed method show that heat islands are inversely related to the amount of vegetation (-0.613), water and humidity (-0.535) and directly related to the amount of soil and desert areas (0.709). Examining the Urban Thermal Field Variance Index, it was shown that the rate of this index in the core of the studied city is less than the outskirts of the city which can be due to the expansion and dispersion of the city, insulation of the roofs of residential houses, increasing the density of vegetation in the suburbs, river crossing through the city center, the presence of barren areas, ring roads, factories and industrial towns in the suburbs cited. The results reveal that the proposed method is an efficient method to analyze the factors affecting the phenomenon of heat islands.

UHI (Urban Heat Island) describes the phenomenon of temperature change in urban areas than their surroundings such as bare lands, gardens. Its effects are: increasing energy and water consuming, escalation of air pollution and intrusion on thermal welfare (Hashemi et al., 2019). Different researches about UHI, have assessed the effect of one or several factors, mainly land use, on the escalation of surface and sensible temperature. There are many researches about this issue. In one case in Macedonia, Skopje, heat island was assessed by using of NDVI index. The results indicated NDVI index was effective in weakening heat island and NDBI index had positive correlation with surface temperature which shows manmade areas have effects on heat island intensification (Kaplan et al., 2018). RS (remote sensing) makes it possible to assess all aspects of UHI as a hazard through preparing high quality satellite images. GIS (geographical information system) does too by preparing database, uniform methods, analysis and producing maps. The objective of this paper is temporal-spatial analysis of heat island of this hazard to recognize the UHI places in relation to land uses. This analysis helps urban managers to know more about spatial requirements in city and the importance of proper places (green lands and parks) to moderate urban temperature.2. Study Area Urmia city is the central district of Urmia county in the center of west Azerbaijan province in Iran. The city has been located in the distance of 18km from Urmia Lake. The city is located in 37 ºN latitude and 45 ºE longitude. Its climatehas beenaffected by latitude, winds, Urmia Lake, Mediterranean wet climate, Siberian cold air masses, topography, altitude, and the direction of altitudes (Javan, 2013).

In this study Landsat images for August period of 1989, 1998, 2011 and 2018 were used. Preprocessing, atmospheric and radiometric correction were performed for the images. To prepare heat island maps, land surface temperature (LST) was calculated by the threshold limit of NDVI and Plank principle method for TM images and Split Window algorithm for TIRS image. The algorithms were run in ENVI 5/3. LST formula factors for TM images included Brightness Temperature and Land Surface Emissivity (LSE). To calculate LST for TIRS bands, Split Window algorithm was run by using some important factors including brightness temperature, split window coefficient values, mean LSE, difference in LSE and atmospheric water vapor content. Using of NDVI and land-use images, the relationship of LST, vegetation cover and different land uses was analyzed. For assessment of heat island intensity changes in Urmia, the index of heat island proportion was used for the images. See the following: temperature of place       Average surface temperature

According to land use maps, during 29 years from 1989 to 2018, Urmia city has had significant growth and expansion. Change detection results indicated  that about 82%, equal to 2475 hectare of bare lands and 72%, equal to 1833 hectare of surrounding farmlands and gardens of the city have been changed to urban land use and related constructions. Using Zonal statistics in Arc GIS the temperature of land uses was assessed .According to the results in 1989 the green cover had the lowest temperature, and the bare lands had the highest average temperature. In August 2018, the maximum temperature is related to urban areas. On August 2018 some new UHIs have been made which are related to producing and industrial workshops, buildings and bare lands in the north east, east and south east of city. The assessment of UHIs changes process showed the escalation of UHI index in Urmia.

Urban Heat Islands have destructive effects for metropolises and the residents. Urmia as a metropolis has had rapid industrial and population growth in the recent decade. In this research assessments have been done by Landsat images from 1989 to 2018 with four years with temporal distances. The results indicated that in 2018 the area of cold and very cold temperature classes have been decreased. It is because of destroying of large areas of farmlands and gardens and changing to urban land use during studied years. In 1989 and 1998 very high temperature class included bare lands. Passing years and constructing new buildings, industrial and production workshops, new UHIs have been created in east, north east and south east in 2018. The results indicated that during the studied period, according to UHI index results, UHI has been intensified. The UHI index with the amount of 0/2 in 1989, has been reached to 0/37 in 2018. According to these results, in addition to extending of UHI in Urmia, UHI has been intensified. Another research which has been done on UHI in Urmia in July 2015 (Asadi et al., 2019) had the same results of this paper. It proved the effect of industrial and administrative land uses on high temperatures. This research represented the negative relation between LST and green lands and vegetables. Maleki et al. (2018) using synoptic stations information and statistics to assess the UHIs in Urmia and recognized the UHIs in same places like this article. This shows that satellite images have high accuracy and efficiency in analyzing natural or manmade phenomena.