جستجوی مقالات مرتبط با کلیدواژه « lst » در نشریات گروه « جغرافیا »

Land use change as a result of the rapid growth of urban areas causes an increase in the temperature of the ground surface in the central areas of the city and its suburbs. Therefore, checking the temperature of the earth's surface is vital in analyzing temperature fluctuations and minimizing its effects. The purpose of this research is to investigate the effects of land use change (LULC) on the LST index in Abbar region and Kohkan village using the combined techniques of remote sensing and GIS. The results of this research show that from 2013 to 2023, agricultural land has increased by 72.21%, while about 89.15% of barren land areas have decreased and turned into agricultural land use. Also, the temperature of the ground surface in the central areas of Abbar city and Kohkan village shows high values compared to the agricultural areas. LST values have an inverse relationship with NDVI and NDLI index and have a direct relationship with built-up areas or barren lands. In other words, there is a positive correlation between NDBI index and LST, and a negative correlation has been observed between LST and NDVI and NDLI. The highest correlation of 0.75 between LST index and NDBI was observed in 2013. The lowest correlation of -0.71 was observed between LST and NDVI in 2013. The lowest R2 values of 0.28 belonged to two LST and NDVI indices in 2023.

Soil moisture can be considered in the control of desertification, agricultural activities, watershed management and optimal management of water resources.   Since the country of Iran is facing many problems in these fields, the expansion of studies in the field of accurate estimation of soil moisture becomes important (Mehrabi et al., 2019). The GLDAS system may have a high error compared to the measured data in some areas. Therefore, it is necessary before the data and results of this product are used as a decision-making tool in the region.  The quality of these data should be evaluated locally using ground-measured data (Polo et al, 2016 & Sanchez-Lorenzo et al, 2013 & Zhang, 2019).In this study, Terra MODIS data was used to estimate soil moisture due to higher spatial resolution (1 km). Due to the fact that it is difficult to estimate the humidity time series in the field, and radar remote sensing methods produce humidity maps with low spatial resolution. Therefore, in this study, a new method was introduced to prepare a soil moisture map with higher spatial resolution based on NDVI and LST MODIS products.The purpose of this study is to estimate soil moisture in Jiroft city using the products of the Morris sensor and NDVI and LST indices. Considering that Jiroft plain is one of the agricultural poles of Iran. Estimating the time series of soil moisture and then providing a drought index based on soil moisture is a useful method for investigating agricultural drought in the study area.

LST and NDVI have high relative importance in arid regions (Park et al, 2016). In the study area, an algorithm based on remote sensing was used for soil moisture time series due to the lack of access to soil moisture time series. Moody's LST and NDVI products with a resolution of 1 square kilometer were used during 2007-2022. Then multiple linear regression was created using OLS method between soil moisture observations (2021 and 2022) and NDVI and LST time series data. Based on the results, both independent variables NDVI and LST were significant at the 99% level) p-value<0.01) .VIF values were less than 10. Therefore, there is no linearity between the independent variables. The standard error is small, which indicates that the estimated value is exactly the true value. The value of t statistic of two variables is higher than 2, which is considered statistically significant. In general, R2 = 0.78 and it shows the accuracy of soil moisture estimation method. R2 was obtained as 0.74 and 0.8 in the years 2021 and 2022, respectively, which indicates the accuracy of the predicted values.

Estimation of spatial and temporal changes of soil moisture is an important issue in low data areas such as Iran.  We proposed a multiple regression model based on Modis NDVI and LST to obtain surface soil moisture at a regional scale. The results showed that the multivariate linear regression method can be used to estimate soil moisture products with high resolution in areas with little data in the surface layers of the soil. Park et al. (2016) stated that LST and NDVI have high relative importance in arid regions. In the studied area, according to previous studies (Bai et al, 2020 & Wang et al, 2007), an algorithm based on remote sensing was used for soil moisture time series due to the lack of access to soil moisture time series became. Moody's LST and NDVI products with a resolution of 1 square kilometer were used during 2007-2022. Then multiple linear regression was created between soil moisture observations (2021 and 2022) and NDVI and LST time series data using OLS method.  Based on the results of simulated soil moisture changes and the OLS method in estimating soil moisture (0-30 cm), both independent variables NDVI and LST were significant at the 99% level (p-value <0.01)). VIF values are less than 10 Therefore, there is no linearity between the independent variables. The standard error is small, which indicates that the estimated value is exactly the true value. The value of t statistic of two variables is higher than 2, which is considered statistically significant. Overall, R2 was 0.7  And it showed the accuracy of the soil moisture estimation method, which is consistent with the results of (Khanmohammadi et al, 2008 & Lin et al, 2015).

Land surface temperature (LST) is one of the important criteria in applications, and its accurate time and place monitoring is considered essential for environmental studies and management as well as planning. Considering the limitations that exist in meteorological stations to determine this necessary parameter, with the help of different algorithms and sensors containing thermal infrared bands, this parameter can be determined on a wide scale. The accuracy of different algorithms for determining the LST using remote sensing images varies in different regions, using different sensors, and so far, no specific algorithm with high accuracy has been considered for all regions.
In this article, the aim is to determine the temperature of the LST by using Single channel, Split window, Planck, Mono Window and RTE algorithms, as well as using the fusion method of LST determination algorithms in a weighted and simple way. In the weighted method, the weight of each method is determined with the help of TOPSIS and SAW multi-criteria decision-making algorithms.
At the same time as the Landsat 8 satellite passes through the study area, the LST is taken for 25 points. To evaluate the performance of the proposed method of fusion of the LST determination algorithms, the root mean square error (RMSE) statistical criterion is used to make a comparison between the ground measurements and the values calculated by the algorithms. The results show that the algorithm fusion method, where the coefficient of each algorithm is calculated using the TOPSIS multi-criteria decision-making method, has the highest accuracy (RMSE=0.552oK). By using this combined algorithm, more weight is given to more accurate methods. Among the five algorithms separately, the single-channel algorithm has the best accuracy (RMSE=0.5623oK) and the single-window algorithm has the lowest accuracy (RMSE=1.0046ok).

Land surface temperature (LST) is an important parameter related to surface energy; as a result, researchers intend to find an accurate Algorithm to estimate it. In addition, researchers in recent decades have used various methods to determine LST, including the methods of Split window algorithm, Single channel algorithm, Mono window algorithm, Radiative transfer equation and Planck equation and etc. The performance of these methods is different when compared to each other. While Radiative transfer equation and Single channel algorithm are more sensitive to atmospheric water vapor content than Mono window algorithm and Split window algorithm especially in hot and humid conditions, Split window algorithm has the highest sensitivity to Land surface emissivity  and does not require accurate atmospheric profiles. On the other hand, high quality atmospheric transmission/radiation is required in accurate determination of LST using Single channel algorithm and is not required in Mono window algorithm. Satellite-based LST retrieval is still a challenging process due to the great variability of the Earth surfaces and a priori knowledge about input parameters such as the atmospheric transmittance, LSE, the meteorological conditions, and the sensor specifications are necessary.  Data fusion methods can be used to take advantage of multiple data. In this method, fused data will be produced in which more comprehensive information can be obtained. This study focuses on developing two reliable multi-criteria decision analysis method to LST determination, based on the weighted average of several LST determination approaches called TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and SAW (Simple Additive Weighting) methods. The main idea of these techniques, the preferred alternative which is the closest to the positive ideal solution and the farthest to the negative ideal solution. During 2019, data collection experiments were performed on the Iranian plateau in semi-arid region of Marvdasht (N 29°49′0.26"_29°50′44.5", E 52°42′51"_52°46′14") located in Fars province. On November 13, 2019, coinciding with the passage of the Landsat8 satellite through the region, the surface temperature of 25 points was measured by a mercury thermometer and the position of those lands was recorded by a manual GPS devise. These points were used in this research to determine the RMSE of different methods of LST retrieval.In this study, an attempt has been made to estimate the LST, by using Landsat-8 TIRS, OLI satellite data.

Different algorithms have been defined to determine LST. Each of these algorithms has advantages and disadvantages, and one method is not considered as the most accurate algorithm for determining LST in all regions. Therefore, in this article, in addition to the five methods of LST determination, simple average and weighted average methods are also used. The weight of each method in the weighted average has been calculated using multi criteria decision making TOPSIS and SAW methods. 

In this research, several methods in LST determination have been used. Due to the calculation of RMSE in the desired area, the most accurate algorithm among these algorithms is Single channel algorithm. The amount of RMSE in the Weighted mean method by TOPSIS approach has the lowest number of 0.552oK which is more accurate than the most precise methods in this region and image (Single channel algrithm). The least accurate method of retrieval of the LST in this region is Mono window algorithm, and the Weighted mean method by TOPSIS approach is 0.450oK more accurate than this method. By using the weighted mean, more weight is granted to the less-error methods.

Estimation of LST is an important topic of research. Because, these days, global climate is changing fast. Therefore, it is vital to investigate ways to predict change in LST change. In the study area, among the 5 algorithms of determining LST, Single channel algorithm has the most accuracy, and the weighted average method, which calculates the weight of the methods or the use of TOPSIS method, has the most accuracy.In a weighted mean methods, the weight of each method is calculated by using the TOPSIS & SAW methods and entering the RMSE of different LST determination algorithms (results of comparing the output of each algorithm with ground measurements) and simple mean is obtained from the average of the methods.

Earth's surface temperature is considered an important parameter in biosphere, ice globe and climate change studies. In this research, LST, NDVI, NDMI and NDWI values were calculated for the Anzali wetland area using the OLI and TIRS measurements of the Landsat 8 satellite. Investigations showed that the minimum LST temperature for the years 2013, 2018 and 2023 was equal to 13.94, 22.36 and 14.6, respectively, and its maximum values for these years were equal to 35.7, 40.58 and 31.6. 31.6 degrees Celsius is estimated respectively. Vegetation status, access to water resources and water stress for the study area were estimated with NDVI, NDWI and NDMI indices. Bands 3, 4, 5, 6 and 10 of Landsat 8 satellite were used to estimate these indicators. The obtained values were compared with LST values. The distribution charts show that the highest negative correlation between LST and NDMI is established at the rate of -0.65 and the highest positive correlation between the NDWI and LST indices is established at the rate of 0.23. In general, the investigations have shown that there is a negative correlation between the NDMI and NDVI indices with the LST index. The Support Vector Machine (SVM) method was also used to investigate land use changes (LULC). The results showed that in the studied area, which has an area of 686.81 square kilometers, agricultural lands have faced significant expansion and reached 487.7 square kilometers from 329 square kilometers in 2013. In the meantime, forest areas have faced a sharp decrease and have decreased from 34.8 square kilometers to 1.73 square kilometers.

Evapotranspiration is one of the crucial parts of the water cycle balance. In Iran, the total annual rainfall is estimated at 413 billion cubic meters. According to an analysis, 296 billion cubic meters, or 72% of this amount, became out of reach due to evapotranspiration. Accurate estimation of evapotranspiration plays a crucial role in studies on the issues such as global climate changes, environmental evolution, and control of water resources. Due to the limited number of meteorological stations and the high costs and time of collecting ground data, using remote sensing techniques and satellite images to have accurate and appropriate outputs can be a suitable tool to determine the actual evapotranspiration rate. One remote sensing algorithm for estmating evapotranspiration is the Surface Energy Balance (SEBAL).

The SEBAL is a model based on image processing that includes twenty-five models for calculating the evapotranspiration (ET) rate as the remainder of the Earth's surface energy balance. This model was introduced by Bastiansen in the Netherlands and also developed for the Idaho Highlands based on measured evapotranspiration at ground level. The SEBAL model uses digital image information captured by the Landsat satellite or other remote sensing sensors capable of recording thermal infrared and visible and near-infrared radiations. The ET value per pixel (e.g., in 30 by 30 square meters of TM and ETM Landsat images) is calculated for the specific moment at which the photo is taken.The ET value will equal the net radiation minus the heat entering the soil minus the heat entering the air. Further details of this model have been provided by Bastiansen et al., but the general equation used by the SEBAL is as follows:LE = Rn – H – GWhere LE is the latent heat flux (Wm-2), which can be easily converted to ET; Rn is the net solar radiation (Wm-2); H is the sensible heat flux (Wm-2), and G is the ground or soil heat flux (Wm-2). From this formula, the formula can be inferred that the radiation that reaches the Earth's surface from the atmosphere is separated into three parts: a part of the Earth or soil is heated, another part of it near the surface of the Earth is heated, and the rest of the remaining energy is evaporated. The SEBAL aims to calculate the latent heat flux (ET), considering the actual ET. It should be noted that the essential accuracy of the results is for the LE or ET. It is affected by the accuracy of the shortwave band as well as the thermal band of the satellite. In the following equation, the net radiation from the surface energy equilibrium equation is calculated as:Rn= (1-α) Rs + (Lin-Lout)Where a is the surface albedo; Rs is the solar radiation (Wm-2); e is the reflection of the Earth's surface (emission), and Lin-Lout is the radiations entering and leaving the Earth in the form of long waves. A value is obtained by mixing spectral reflections from six shortwave bands on the Landsat satellite. Lin-Lout is also considered a function of the surface temperature, which can be extracted from the satellite image. The value of e is obtained by plant indices created from two short-wavelength bands. The potential importance of Rs per pixel with a definite slope can be determined using the precise sky theory curves. The soil heat flux or G can be obtained empirically using Bastiansen's et al. (1998).

According to the results obtained from analyzing the data and output maps captured by the Landsat satellite, and considering the LST map, in which green color indicates a deficiency and red color represents very high, there was an oscillating trend from 2000 to 2008. Still, according to LST maps, since 2010, there has been a sharp incremental trend, the peak of which has been in 2020, and the LST has reached its highest level. Such a trend has also been seen concerning the soil temperature map of Mazandaran province. A remarkable point about the increase in soil temperature is that it was significant and instantaneous in the fifth month of 2010, and the soil surface temperature has increased, just as in the LST, since 2010. Regarding the NDVI map of Mazandaran province, significant and impressive changes have occurred since 2012, and this trend has risen from this year until 2020. According to the maps obtained from the soil moisture in the province, the data show that oscillating changes occurred until 2012, and since the fourth month of 2012, the region's soil moisture has also increased. All the factors mentioned have a direct relationship with evapotranspiration. According to the results obtained and the increasing trends, especially from 2010 to 2020, there is expected to be an increasing trend for evapotranspiration using the SEBAL algorithm. The primary outcome of this research, which studies the changes in the evapotranspiration rates in Mazandaran province, is that: as expected, due to the increase in all the factors affecting the evapotranspiration increase, the results show that since 2010 the evapotranspiration trend has dramatically increased; Of course, due to the geographical location and proximity to the Caspian Sea, the evapotranspiration has always been relatively high, but there have been significant changes and a sharp increase from 2010 to 2020.

Temperature is the most important parameter for studying spatiotemporal phenological changes in plants. Thus, the current study was aimed to investigate the potential of MODIS land surface temperature (LST) data for mapping growing degree days (GDD) and different phenological stages of Bromus tomentllus and Astragalus effusus in Chaharmahal and Bakhtiari Province. MODIS extracted maps of maximum, minimum and mean temperature, GDD index and phenological stages from 2018 to 2019 during growing season were assessed against weather station data and also field-based phenilogical data using Pearson analysis in three regions with different altitudes. Results showed that MODIS LST and GDD maps had more than 91 and 99% correlations with field-based air temperature and GDD data, respectively (p<0.001). In early growing season, GDD values were less than 16 degree-days and they were more than 5200 degree-days in the late growing season which explained one and all the phenological stages of the studied species in the study area, respectively. The study findings indicated that MODIS data have high capability in spatiotemporal stratification of phenological stages of the Bromus tomentllus and Astragalus effuses plant species. The knowledge of different phenological stages is essential in species conservation and rangeland sustainable utilization, therefore, species phenology map can be used as an effective tool in rangeland management in the related organizations.

The surface of the earth or the top of the earth at the level of cities and urban areas becomes significantly higher than the surrounding environment, which is known as the thermal component. If this phenomenon is higher than a certain limit, it will cause problems for the city residents. But one of the most important factors that can play a very important role in controlling this phenomenon is the presence of vegetation, which is mainly a significant part of this coverage in urban areas. In this study, the effect of vegetation cover on the amount of urban heat island in Isfahan City was investigated. For this purpose, the surface temperature of the city and its relationship with the surface cover were investigated using the land surface temperature (LST) products of the MODIS sensor in places with heat islands by applying the Normalized Difference Vegetation Index (NDVI) of Landsat satellite data during the period of 2001-2020. Examining the annual changes in temperature in Isfahan shows that during this period, the temperature is generally increasing, so 2011 with an average annual temperature of 37.21 degrees Celsius is the hottest year and 2012 is the coldest year with an average annual temperature of 33.7 degrees Celsius. The most apparent thermal islands were observed in areas with industrial use, high-traffic places with severe air pollution and population density, and places with weak vegetation, areas with dense and worn-out textures.

Desertification manifests itself in its initial stages with less production in the ecosystem. In this situation, the balance of water and nutrients for the growth of plants becomes unfavorable compared to the past. In natural conditions, the ecosystem of dry areas is in a stable state in terms of water and energy conversion, but due to human intervention, this balance is subject to change. Obviously, when the vegetation is destroyed, the land becomes bare, its organic matter is destroyed and the structure of the soil is destroyed. In this situation, direct rain on the soil causes further destruction of the soil structure. The natural consequence of such conditions is the lack of rainwater storage and the drop in the level of underground water tables. Usually, severe reduction of vegetation, water and wind erosion, salinity, soil compaction, sedimentation and emptying of nutrients are considered as the main signs of desertification. According to the definition of the Convention to Combat Desertification (UNCED), desertification is the destruction of land in dry, semi-arid and dry and semi-humid areas due to human and climatic factors, which as a result reduces the productive power of the land. Water and soil are among the most valuable natural resources, and human societies, both farmers and ranchers, are active and dynamic agents of changing natural landscapes, and improper management, neglect, and excessive exploitation can lead to soil destruction, and as a result, human life is seriously threatened. be placed.

To achieve the goals of the research; Modis sensor images were obtained from the NASA database during the years 2000-2020. In order to calculate the land surface temperature (LST) from the MOD11A1 product, to extract the Normalized Difference Vegetation Index (NDVI), from the MOD13Q1 product, to extract the Net Primary Production (NPP) index, from the MOD17A2H product with a spatial resolution of 500 meters in a time frame of 8 The MOD43A1 product of the Terra satellite was used to extract the albedo index. After preparing the MODIS sensor images, the indices of land surface temperature, normalized difference of vegetation cover, net primary production and albedo were estimated from the images and their correlation was checked.

Based on the results, LST is shown in three classes: low, medium and high. So, low to south moderate in the northwest and west and higher temperatures in the east of the study prevail. And based on the results, NDVI is classified into four areas with very poor, poor, moderate and dense coverage. Thus, the very weak layer is located in the north and northeast side, the weak layer is located in the northwest to south side, and the medium and dense layer is located in the northwest to southwest side. with increasing temperature; The normalized difference index of vegetation decreases and there is an inverse relationship between LST index and NDVI. with increasing temperature; Primary net production decreases and there is an inverse relationship between LST index and NPP. Based on the obtained results, it can be acknowledged that there is an inverse relationship between LST index and Albedo.

Based on the results obtained from the fourth chapter; The LST index during 2000-2020 was divided into three classes of low temperature, medium temperature and high temperature. So that; The largest area of the studied area had an average temperature, which was observed in the south and southwest. In the eastern and northeastern regions, the temperature was higher than in other regions, which corresponds to Namak Lake and Qom. Also, areas with low temperature were covered only in small parts from the northwest to the southwest of the region, which corresponds to the uneven heights of Karks. The NDVI index during 2000-2020 was divided into four classes: very weak, weak, medium and dense. So that; The largest area of the studied area had weak to weak silt cover, which was observed in the northwest to east range, which corresponds to Namak Lake and Qom. Also, it covered areas with medium to dense coverage only in small parts from the northwest to the southwest of the region, which corresponds to the uneven heights of Karks. Based on the results of NPP index changes during 2000-2020, the highest amount of primary net production was observed in the northwest to southeast of the studied area. This shows that the amount of primary net production is higher in this range. Also, the minimum amount of primary net production in the range from northwest to south corresponds to the heights of Vulture to Tanam, which indicates low production. Based on the results of changes in the Albedo index during the years 2000-2020, the highest amount of albedo was observed in the northeast to the east, so that these areas correspond to the salt lake and areas with weak vegetation, low net primary production and high temperature, because albedo and surface temperature The earth is affected by the amount of vegetation and relatively small changes in the characteristics of the vegetation cause changes in the albedo and temperature of the earth's surface. Between the normalized difference index data of vegetation and land surface temperature, net primary production and land surface temperature and albedo and land surface temperature, there was a significant relationship at the one percent level so that the correlation between vegetation and temperature, net primary production and temperature and albedo and the temperature was reversed and equal to 0.80, -0.84 and -0.85 respectively. The results of the study made it possible to create a desertification program by presenting a practical program for the Syr Regressive Road Dam and took an effective step towards sustainable development.

Due to the vast changes it brings about in the land use and land cover, the rapid expansion of cities has had many negative effects on the environmental quality at global level. Examples include air quality, increased temperature, changes in perspective, and the alteration of agricultural lands into barren lands that leads to the loss of biodiversity. Since land use changes happen at extensively, remote sensing technology is a necessary, efficient, and valuable means to monitor changes. In this study, the effect of land use changes on the temporal-spatial patterns of land surface temperature in the urban lands of Ilam city in a 30-year period (1990-2020) was examined using Landsat satellite images and simulation of changes using fuzzy ARTMAP neural network model. Landsat satellite sensors (TM, OLI, ETM) were used to investigate the longitudinal and spatial changes (LST) in Ilam city.  To provide the land use map, the pixel-based classification for all periods (1990-1995-2000-2005-2010-2015-2020) was applied using ENVI and Ecognitio software packs and then estimations were made using NDVI and LST models. The results showed that Ilam city ecosystem has moderate levels of NDVI. The average temperature of residential land use level in June is 35.8 degrees centigrade, and the temperatures of three land uses of horticulture, agriculture, and other uses (uncovered lands) were 32.22, 37.25, and 38.46, respectively, with the main lands with the minimum temperature being green highlands. In city, existence of high rise buildings and building shading, use of materials with less heat absorption such as Isogam, urban green space and furniture, and air pollution lead to lesser absorption of energy. On the other hand, the spatial distribution of NDVI was aligned with LSP values. The results regarding land use areas using fuzzy-neural network show that residential land uses has risen from 19.18 percent in 1990 to 39.35 percent in 2020, which shows the city expansion and development. On the other hand, the horticultural land use has declined from 8.64 in 1990 to 3.49 in 2020, which can be attributed to the development of urban space.

In some metropolises of Iran, Thermal Islands is somewhat generalization and observable. Existence of tall buildings, traffic, population density in the hub centers and especially in the central part of the city cause the creation of a Thermal island in the city. In this research, the time-spatial identification of the thermal islands of the cold season during the years 1985 to 2019 and the analysis of the factors influencing their formation were done with an emphasis on changes in land use, texture, high-rise construction, land cover and climatic variables in the Tehran city. The current research, which is based on the extraction of Landsat satellite images, produced maps of the mentioned indices using NDVI, LSE, ISA and LST indices, which became the basis of the analyses. The results of the research show that high LST values are spread in areas of Tehran where the NDVI index is relatively low and the ISA index is high. Although the formation of Thermal islands in winter is a little far from expected, small parts of the north, northwest, south and southwest of the city (zones 1, 5 and 18) are permanently exposed to Thermal islands, which are in step with dense land use and areas free of vegetation. The scattered hot spots in the mentioned areas with a temperature cover above 20 degrees Celsius in the middle of the average temperature of the city, which is lower than 17 degrees Celsius during the cold period of the year, is indicative of this fact.

With the development of urbanization and excessive interference of humans in the environment, large amounts of agricultural and forest areas have been replaced by houses, industrial areas, and other infrastructures. One of the negative effects of industrialization is the sharp increase in the temperature of cities and the creation of temperature differences between urban areas and the surrounding areas, which is known as the heat island. The main goal of this research is to estimate the temperature of the earth''s surface and investigate the effects of urbanization on the temperature of the earth''s surface in the city of Tabriz. For this purpose, satellite data and remote sensing because of their wide coverage; are Used. In this method, the temperature of the earth''s surface has been calculated using the information of a thermal band (Landsat 8 satellite) and after conforming to the land use map, the temperature of different uses of Tabriz city has been estimated. The results of the research show that there is a direct relationship between the spatial-temporal changes in the cities (including the change in land use and the reduction of vegetation mass) in the city of Tabriz and the increase in the temperature of the earth''s surface, and satellite images (remote sensing) are a very suitable tool for evaluating heat and temperature. They are the surface of the earth.

Global warming and the heat islands of cities are one of the biggest challenges in the world today. Cold islands is a word that stands in front of heat islands and refers to areas of the city that have lower temperatures than the surrounding areas. In this study, in order to investigate the factors affecting the formation of cool and heat islands of the city, it was first obtained by using Landsat image processing and using the single-channel surface temperature algorithm. Then to investigate the parameters affecting the land surface temperature changes; Criteria for changes in particulate matter and changes in vegetation were considered. The NDVI index was used for vegetation and the algorithm proposed by Saraswat et al. was used for the amount of particulate matter. According to the results, the highest ranking neighborhood  for heat islands were in Bustan, Shahid Bagheri township and the airport, respectively, and the lowest amount of cool islands were in Baharan, Niavaran and Darband, respectively. Pearson coefficient obtained from the relationship between surface temperature and vegetation was -21.29%, which indicates the inverse relationship between temperature and vegetation, as well as the amount of vegetation index in hot and cold regions. Regarding the relationship between land surface temperature and air pollution, the correlation between these two parameters was equal to 19.31% and comparing the pollution index in areas with cold and warm islands showed that there is a significant relationship between reducing air pollutants and cold islands but the opposite is not true.

Cities became one of the main centers for research because they are the main gathering place for human beings. One of the problems that plagues the city today, especially large cities, is the phenomenon of the city's thermal island. There are many factors involved in the spatial changes of a thermal island, including human activities and changes in land cover, which often lead to a reduction in green space. Numerous vegetation indices have been developed and in this study, NDVI and EVI indices were examined. Land surface temperature for August 18, 2021 was retrieved from Landsat images using one-channel algorithm and the above indicators for the above date were extracted. The results showed that most of the city is covered by a temperature class of 305-310 degrees Kelvin. By comparing the indices and the land surface temperature, it was found that the land surface temperature and the vegetation have an inverse relationship, so that in the south and east the temperature is lower and the vegetation is higher; but for the South and the West, the opposite is true. Also, the relationship between land surface temperature and EVI index was -23.31% and its relationship with NDVI index was about -24%; this indicates that the two indicators are similar with a slight difference.

Drought is a phenomenon that occurs at different times unpredictably with different intensity and has severe effects on human society and ecosystems. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) and PERSIANN Dynamic Infrared Rain Rate (PDIR Now) sensors were applied to examine the drought effects on vegetation in Northern River Basin of Afghanistan from 2001 to 2020. Therefore, MODIS data include Enhanced Vegetation Index (EVI), Vegetation Condition Index (VCI) with a time period of 16 days and a spatial resolution of 250 meters, Land Surface Temperature (LST) with spatial resolution of 1 km and period of 8 days, and monthly precipitation data with a resolution of (4*4) km. The relationship between drought and vegetation in spring was investigated using time series analysis, regression analysis and calculation of anomalies.  The results showed that the average vegetation coverage in the whole statistical period was 45.21%. In this study, vegetation area in 2001, 2008 and 2011 have reached the lowest rate (9.9%, 9.9% and 19.3%), respectively. According to VCI, 83.5%, 81.39% and 74.9% of the basin in these years are under drought conditions, respectively. Rainfall data confirm that these years have had the lowest rainfall 96.7, 133 and 117 mm, respectively. The years 2003, 2009 and 2010 with the highest vegetation in this season were recorded mainly due to the lower LST and higher rainfall then their average period. The correlation between EVI and LST is (r=-0.87; p<0.05), EVI and Precipitation (r=0.60; p<0.05). However, in Northern River Basin of Afghanistan, LST and rainfall must be considered together to determine the relationship between drought and vegetation dynamics properly.

The expansion of industrialization and migration of rural population to urban areas has led to the uncontrolled growth of urban population in recent years. Rapid population growth and infrastructure development of the city will cause changes in the urban pattern, and with the destruction of the environment and the replacement of buildings will cause environmental pollution and climate change. The satellite image studied in this research is related to Landsat 8 satellite, which was taken for the study area. Atmospheric correction is performed on the data and digital number (DN) is converted to spectral radiation and spectral radius is converted to black body temperature and its temperature is converted from Kelvin to Celsius, then the radiation power correction and finally the vegetation index (NDVI) is extracted. And ground surface temperature index (LST) has been extracted. Vegetation index (NDVI) with a maximum of 0.64 and a minimum of -0.36 and surface temperature index (LST) with a maximum temperature of 65 degrees and a minimum of 34 degrees, which high temperatures in some places indicate existing industrial activities In the city of Ahvaz, including the steel industry, oil companies and the lack of adequate vegetation in the bare lands of the city.

Due to the kind of its usage in a relatively long period, the analysis of temperature levels of modern cities is among the most important subjects that can be considered in the field of geography and environment, and its results can be used in promoting the science and solving the problems of today’s societies. The effect of temperature on climate, especially in metropolises, is one of the crucial indexes of this procedure. The rise in the land surface temperature, which is an indicator of the heat intensity, is among the important elements for identifying the changes in weather. The emergence of heat in cities is one of the most known forms of such changes. Urban heat islands are indicated by a temperature inversion and annoying temperatures throughout winters and summers. The temperature of some cities or urban areas has remarkably grown compared to the suburbs or rural areas around them. This phenomenon, called urban heat island, has caused numerous problems. The term “heat island” was proposed by Havard for the first time almost a century ago in 1833 (Sook, 2004: 10). Afterward, numerous studies were carried out in the large and industrialized cities of the world, whose results demonstrated that civilization has exerted significant changes in the meteorological parameters and properties of the ground surface, and consequently, remarkable variations in local climate (Mousavi Baygi et al., 2012: 36).

The present research is aimed at identifying the places with high heat, which have created the known thermal patterns in the city of Arak in Iran. Assessment of the spatial-temporal variations of the urban heat islands can be used as a critical component in the management strategies of natural resources and environmental changes, whose results can be useful for environmental, regional, and urban planners.

The studied area, Arak, is the capital of the Markazi Province in Iran, with an area of 304.8 km2 at 1755 m above mean sea level. The city has temperate weather tending to cold and semi-arid. According to enactment in 2011, Arak has five municipal districts.
The research method was analytical-statistical, and an effort was made to evaluate the relationship between land surface temperature and land cover of the city. In order to evaluate the development of hot places in Arak and determine its urban thermal patterns and heat islands in the long term, the data of the satellite images of the Landsat scanners 4, 5, 7, and 8, including the data of the TM scanners of Landsat’s 4 and 5, Landsat 7 (+ETM), and Landsat 8 (OLI/TIRS), during the period 1985-2017 were used. These images include two sets of reflective spectral and thermal bands. The thermal bands were used to identify the surface temperature and thermal islands, and the reflective bands were employed to apply the indexes of image processing. The data of the TM, +ETM, and OLI/TIRS scanners were provided in the bands 6, 8, and 11, respectively. The data of the thermal band 6 of Landsats 5 and 7 with wavelengths of 10.40-12.5 micrometers and the band 10 of Landsat 8 with wavelengths of 10.60-11.19 micrometers were used to calculate the surface temperature distribution patterns of Arak. The bands 3 and 4 of Landsats 5 and 7, along with bands 4 and 5 of Landsat 8, were also utilized to calculate the NDVI index (NASA, 2014). In the global imaging system, the images of the area of Arak exist in the 165th And 36th row.
Generally, the following steps were taken to analyze the urban heat islands of Arak:
Calculation of LST and spectral radiance
Conversion of the calculated radiation to Kelvin temperature
Calculation of the temperature levels of five districts of Arak
Calculation of the density percentage of the fourth level of temperature (hot points of the city)
The minimum, maximum, and average temperatures of Arak
Calculation of normalized difference vegetation index (NDVI)
Calculation of the urban thermal field variance index (UTFVI)

Evaluation of the land surface temperature changes and patterns
The analysis of the vegetation variations demonstrated that depending on different uses of urban lands, vegetation is in accordance with the temperature level. Generally, the low temperature in the southwest of the city, which was observed in the land surface temperature maps, is caused by the gardens of the cities of Senejan and Karahroud. The eastern and southeastern parts of district 1, which has industrial uses, streets with heavy traffic, and accumulation of uses, and the north of the city, i.e., the north of district 3 with the accumulation of residential uses and heavy traffics, have higher temperatures. Generally, during the studied period on vegetation, all areas having this use, except for the northwestern part, had considerable changes. Most of the vegetation in the studied period was concentrated in districts 4 and 5, which included the gardens of Senejan and Karahroud. However, other parts of the city, including the northwest and, to some extent, the city center and district 1, whose vegetation includes several parks and green spaces, show decreasing changes in temperature. Based on the results obtained from evaluating the urban thermal field variance index (UTFV) of Arak, using 20 land surface temperature (LST) maps and normalized difference vegetation index (NDVI), obtained from Landsat satellite, (TM), (ETM+), (OLI/TRS), the very hot temperature level of Arak was widely observed mostly in the north, northeast, east, and southeast of district 1, north and northwest of district 3, west and southwest of district 2, and district 5.

The evaluation of the LST maps to identify the hot points and urban thermal patterns revealed that most of the hot points are located in the areas with idle lands in the suburbs. These lands are mostly observed in the recently developed areas of the suburbs, including districts 1 and 3. Inside the city, most of the hot places conform to the formation of thermal patterns close to industrial towns, streets with heavy traffic and high pollution, and residential areas with dense and urban decay. The largest area of the third temperature level is observed in district 1 due to the presence of industrial towns, dense residential towns, cultural and governmental organizations, heavy traffics in the streets, the northern and southern belts in the district, and idle lands in the north and east of the district. The presence of the industrial towns and factories in the city of Arak, especially in district 1, is one of the effective factors in increasing the heat and creating thermal patterns. The thermal patterns in district 1 had the highest intensity in 1988/09/08 and 2017/01/08 during the studied period.

Studies have shown that the role of thermal temperature measurement in studying and estimating surface temperature is very important. Earth surface temperature is an important indicator in the study of equilibrium energy models on the ground at the regional and global scale. Due to the limitation of meteorological stations, remote sensing can be a good alternative to earth surface temperature estimation. The main objective of this study is to monitor the surface temperature of the Earth using satellite imagery and a relationship that can have a surface temperature with land use. For this purpose, the relevant images were first obtained and the necessary pre-processes were applied to each one. Then it was compared to modeling and classification of images. Firstly, in order to study land use change, land use classification map was extracted for each two years using a controlled classification method. Then, to study the land use change, the land use change map was extracted for a period of 28 years (1987-2015). Became finally, in order to monitor the surface temperature, the surface temperature map of Meshginshahr was extracted. The results showed that there is a strong relationship between land use and surface temperature. High-vegetation areas and low-temperature blue areas. Also, rainfed farming has the highest average temperature relative to adjacent areas, which indicates the dryness of agricultural products in the Meshginshahr city.

The high spatial and temporal limitations of TIR images for use in urban climatology have been identified as a current scientific challenge. Therefore, the use of Data Fusion Algorithms in Remote Sensing has been considered. In the old methods, two bands of one sensor were used for Data Fusion. In these methods, a panchromatic band was used to increase spatial accuracy, so only spatial resolution was increased. To solve this problem, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was used to integrate the images of two Landsat and Modis gauges to increase the spatial and temporal resolution of the reflection. but, this algorithm is designed for pixels and unmixing areas that are the same in Modis and Landsat pixels. The use of this model was not suitable for urban areas with a different of landuse. Therefore, the Enhanced STARFM model (ESTARFM) was developed. The ESTARFM model was improved in 2014 to predict thermal radiation and LST, taking into account the annual temperature cycle and the unevenness of the earthchr('39')s surface, and the SADFAT model was introduced.
In this study, the performance of SADFAT model in the use of OLI spatial resolution and MODIS temporal resolution in LST forecast in urban areas was examined. The metropolis of Tehran has different surface covers and multiple microclimates. So if the algorithm works successfully, This model can be used in other cities to improve urban heat island studies. The inputs for the algorithm are thermal radiance of Modis and Landsat   images, the red and near infrared band of Landsat for daily production of LST in 2017 in the city of Tehran. The algorithm uses two pairs of Modis and Landsat images at the same time and sets of Modis images at the time of prediction and then calculate the conversion coefficient for relating the thermal radiance change of a mixed pixel at the coarse resolution to that of a fine resolution. In this way, LST is generated in areas with a variety of landuse.
All the estimated pixels were compared to the base image pixels in that range to evaluate the results of the model. The comparison results for the autumn days with the average correlation coefficient of 0.86 and RMSE equal to 0.122, showed that the model has the highest accuracy in this season and in other seasons with the average correlation coefficient of 0.76 and RMSE about 0.4, has provided good accuracy.
Visual interpretation of the results of SADFAT showed that this model is able to accurately predict the LST of the land cover in different surface coatings and even in areas where one or more urban land uses are mixed in one MODIS pixel.
However, the borders are well separated and the features are not combined. Although the boundaries are clearly defined, in some land uses, the predicted LST is somewhat higher than the observational image.
Landsat and Modis satellites pass through an area with a small time difference, so they are suitable for combining with each other. But in predicting reflectance with the SADFAT algorithm, there are systematic and variable errors that we need to be aware of in order to increase the output accuracy. One of the systematic and unavoidable errors is the instability of the Terra and Aqua satellites passing through at any point, ie at each satellite pass, the location of the study area in Swath and the size of the pixel changes. Due to the distance of the study area from the vertical center of measurement on the ground (Nadir), the amount of this error varies on different days and should be checked for each day. The preventable error is the sudden change in one or more images used (16 days of the same pass time interval for Landsat) is high for estimating surface reflectance with spatial and temporal resolution. These changes may be due to human factors such as air pollution or natural factors. Natural factors such as clouds and dust storms are the main sources of error in using the SADFAT model because they are sudden and temporary and cover a wide area. The occurrence of these two factors has a great impact on reflectance. Therefore, a sudden change in these factors, in one or more images, causes a large error in the calculations.
The study also found minor spatial errors in the prediction, so that even on days when the results were better, points were observed where the values ​​in the predicted LST images did not match exactly with the OLI sensor. The reason for this may be due to changes in vegetation. Although there are some systematic and variable errors in the images and the implementation of the algorithm The results of this study showed that the performance of this model is reliable for predicting the daily LST with a spatial resolution of 30 meters in Tehran.
This method is able to support urban planning activities related to climate change in cities, so it is recommended that its performance be examined separately for different land cover in the city and the efficiency of this algorithm be evaluated with other sensors such as Copernicus Sentinels.

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.

Atmospheric column water vapor, which is the total atmospheric precipitable water vapor contained in a vertical air column, is one of the most important factors in all surface-atmosphere interactions (such as energy fluxes between the earth and the atmosphere) and plays a key role in wide variety of environmental studies, ecological and agricultural applications. However, measuring this parameter at meteorological stations requires the use of radiosonde instruments, which being pointwise and costly are limitations of these observations. Therefore, remote sensing is used as an alternative to estimate this important atmospheric parameter. Compared to other atmospheric parameters, atmospheric water vapor which attenuates remotely sensed radiance is of great importance. Although this atmospheric parameter is measured by AIRS (Atmospheric Infrared Sounder) sensor, its low resolution (about 40 km) is not acceptable for many applications. Therefore, developing an algorithm to downscale the AIRS-derived column water vapor is the main goal of this study, so that its spatial resolution can be improved. To do this, using the ratio method, the AIRS-derived column water vapor is fused with the MODIS (Moderate Resolution Imaging spectroradiometer) data. Then, due to the major influence of this parameter on Land Surface Temperature (LST) estimation, the role of improved resolution atmospheric column water vapor in the estimation of LST is investigated as a secondary goal. In order to validate the estimated parameters and evaluate their accuracy, independent datasets were used. Results of the implementation indicate that proposed downscaling method has high potential to enhance the spatial resolution of AIRS-derived atmospheric column water vapor, without significant degradation of the RMSE. It was also found that the atmospheric column water vapor when moving into higher spatial resolution can dramatically increase the accuracy of the LST estimation.