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The main purpose of the current research is to investigate the nighttime LST climatology in Iran. The study of the seasonal regime and annual temperature differences along with the identification of the role of different temperature thresholds and the spatial analysis of Iran's cold spots are among the climatic features studied in the current research. For this purpose, the MYD11A1 product (version 6.1) of the MODIS which is a key instrument onboard the Aqua Satellite with a horizontal resolution of 1000 meters has been used for a period of 19 years (2003-2021). This product is related to the nighttime passage of the satellite at 1:30 local time. Satellite data were validated using daily soil temperature data from 174 meteorological stations. The accuracy of satellite data showed that the area average values of root mean square error (RMSE) and mean bias error (MBE) compared with station data are 1.7℃ and 1.39℃ respectively. Also, the PBias statistics results show that the underestimation prevails in the night LST data compared to the soil temperature. In total, the MODIS sensor estimates LST in most regions of the country with an error of less than 10%, which indicates the high efficiency of the MYD11A1 product in assessing Iran's nighttime LST. The findings showed that the combination of altitude and higher latitude has significant importance in the seasonal changes of nighttime minimum LST in Iran. So, the combination of these two factors plays an important role in the occurrence of the lowest night temperatures in the country in the middle parts of central Alborz and the highlands of Azerbaijan. Thus, the longest period of dominance of low temperatures in Iran is related to 38°N and 36°N latitudes, respectively. The results also indicate that the prevailing temperature zones in Iran fluctuate from the minimum temperature in January to the maximum temperature in July. In the transitional seasons, only April and October have distinct and independent temperature identities, and the rest of the months of the transitional seasons are either connected to winter or connected to summer according to the pattern. Examining the course of intra-annual temperature changes shows that the highest intra-annual temperature concentration and the highest homogeneity are observed in the summer season. On the other hand, the highest level of heterogeneity and the lowest level of temperature concentration are related to the autumn season. Examining the average nighttime temperature difference in Iran also shows that the highest values of the earth surface temperature difference occur in the winter season and the lowest values in the summer season. In a general view, the temperature difference curve fluctuates between a maximum of 56.6℃ in January and a minimum of 46.5℃ in July. Spatial analysis of nighttime temperature difference showed that the Lut desert experiences the highest average values of nighttime temperature difference throughout the year. In general, lack of moisture, very low percentage of cloud cover, lack of vegetation cover, and very low soil moisture seem to have led to high nighttime temperature fluctuations in the Lut desert. On the other hand, a narrow strip along the southern coastline of the Caspian Sea experiences the lowest nighttime temperature difference in the country. Clearly, the high humidity of the southern coastline of the Caspian Sea plays the main role in reducing the temperature fluctuations of this region. The geographical analysis of cold spots indicates that the coldest spots of Iran are observed in the high peaks, located in high latitudes. Also, the highest geographical concentration of cold spots in Iran is related to the summer season. Meanwhile, the largest spatial distribution of Iran's cold spots can be seen in the winter season, especially in December. The findings also indicate the existence of a regular annual cycle in the spatial arrangement of cold spots in Iran. In this way, in the warm period of the year, the cold spots of Iran are concentrated only in the middle part of the central Alborz. With the beginning of the cold period of the year, we are witnessing a northwest shift of cold spots toward the highlands of Azerbaijan. So that a bimodal pattern replaces the summer concentrated pattern. The bimodal pattern continues at the height of winter with the dispersion of cold spots in Alborz and Azerbaijan, but with the arrival of the warm period of the year, a southeastward shift of cold spots towards the Alborz Mountains begins, with the cold spots completely concentrated on the Damavand peak, this annual cycle ends in the summer season. [ABSTRACT FROM AUTHOR]

Human interventions in natural areas, such as changes in land use, have led to a series of anomalies and environmental hazards. These significant and cumulative changes in land cover and land use have resulted in various anomalies, including excessive runoff, soil erosion, desertification, and soil salinization. The primary objective of this study is to examine the temperature variations caused by the land cover structure in Lorestan province and analyze the impact of land use changes on the province's temperature structure. To accomplish this, data from the MCD12Q2 composite product, which provides information on land cover classes, and ground temperature data from the MOD11A2 product of the MODIS sensor were utilized. Additionally, the cross -analysis matrix (CTM) technique was employed to identify temperature variations for each land cover class during both hot and cold seasons. The findings indicated that Lorestan province had five distinct land cover classes: forest lands, pastures, agricultural lands, constructed lands, and barren lands. The cross -matrix analysis revealed that forest cover (IGBP code 5) exhibited the highest temperature, reaching 48°C, while urban and residential land cover (IGBP code 13) had a temperature of 16°C, making it the warmest land use. Furthermore, it was observed that the thermal variations of land cover were minimal during the warm season, with no significant difference between the temperature structures of land cover classes. However, during the cold season, the thermal variations became more pronounced. The analysis of variance test demonstrated that, unlike the warm season, different land cover classes significantly (Sig = 0.026) influenced the thermal variations in the province during the cold season. Scheffe's post hoc analysis indicated that this difference was particularly noticeable between rangeland cover classes and built-up cover. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) phenomenon caused by rapid urbanization has become an important global ecological and environmental problem that cannot be ignored. In this study, the effect of influencing factors on ground surface temperature in Karaj city was measured and by using ordinary least square (OLS) regression, geographic weighted regression (GWR) and multiscale geographic weighted regression (MGWR) models, spatial heterogeneities of the factors were measured. We checked the influencer and LST.The results indicated that, compared with traditional OLS models, GWR improved the model fit by considering spatial heterogeneity, whereas MGWR outperformed OLS and GWR in terms of goodness of fit by considering the effects of different bandwidths on LST. The results showed that compared to the traditional OLS models, GWR improved the fit of the model by considering spatial heterogeneity, while MGWR outperformed OLS and GWR by considering the effects of different bandwidths on LST. Building density Normalized Difference Impervious Surface Index (NDISI) and traffic density had the greatest effect on high LST by 0.323, 0.246 and 0.260 respectively, while NDVI index, MNDWI and population density were negatively correlated with temperature. These findings show the need to consider spatial heterogeneity in the analysis of impact factors. This study can be used to provide guidance on strategies to reduce temperature in different regions. [ABSTRACT FROM AUTHOR]

In this research, a synthesis approach to estimating land surface emissivity (LSE) using remote sensing data and a spectral library that can be used to any optical sensor is proposed. The suggested method not only estimates the LSE more accurately as a function of the reflection of various surface effects, but it also takes into account the spectral response functions (SRF) of the thermal and reflective bands when calculating the LSE. Moreover, by using reflection of all reflectance bands, the proposed method strengthens the prior methods' poor link between LSE and reflectance of only the red band. The suggested approach was applied to a Landsat 8 imagery, and the resulting LSE was compared to and verified using two LSE products from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The findings indicated that the LSE from the proposed methodology in Landsat 8 thermal band 10 has a root- mean-square error of 0.76 % and 0.75 %, respectively, when compared to the equivalent LSE product of the first and second ASTER Images. This error was also calculated in the 11 thermal band, with values of 1.49 and 1.06 %, respectively. This error was also calculated in the 11 thermal band, with values of 1.49 and 1.06 %, respectively. Additional source of uncertainty in thermal band 11 might be the relatively substantial discrepancy in the SRF, spectral range, and effective wavelength between Landsat 8 band 11 and the 14-ASTER band. The results of this study compute the surface emissivity as a function of the reflectance of the reflective bands, and each pixel associated with that reflectance has a unique emissivity value that differs from that of nearby pixels. The prior technique, on the other hand, assigned a constant emissivity coefficient to the value of the group of pixels, and the surface emissivity was computed as a constant discrete value in each part of the image. [ABSTRACT FROM AUTHOR]

Albedo is one of the key parameter in climatic studies. Albedo climatology investigation can be a tool to recognize environmental changes. The MODIS continuously produces the land surface albedo on a global scale and with the appropriate spatial resolution and makes it available to researchers. In this research, in order to analyze the climatology albedo of Iran, firstly, the data produced by the MODIS sensor MCD43A4 product in the range of Iran in the period from 1/1/2001 to 12/30/2021 with a spatial resolution of 500 meters and a daily temporal resolution was taken from the NASA website. After the necessary pre-processing, the long-term average monthly, seasonal and annual albedo of Iran was calculated. The findings on a monthly scale showed that in the months of Jan, Feb, and Mar which are known as Iran's snow-covered months, Iran's albedo is maximum and is decreased in the transition months, and then in the warm months of the year (June, July, and Aug) it is increased again due to the dryness of the land and the increase in the land surface temperature. This two-way behavior is also evident in the seasonal scale. These calculations are made in the worst conditions (July) over 98% of the area of Iran and in the best conditions (Jan) on 99.97% of the area of Iran. In other words, in the July, the albedo time series data was complete for about 98% of Iran's area, and there was a statistical gap in about 2% of Iran's area. In the research of Kefayat Motlagh et al. (2021), the albedo data gap values of the MODIS sensor have been investigated in different seasons and annually. Results show that the maximum distribution of albedo in winter and autumn seasons corresponds to the snow-covered heights of Alborz, Zagros and the northwest of the country. But in the spring and especially in the summer, with the increase in air temperature and surface temperature, most of the wetlands dry up. With the drying of the bottom of Jazmurian, Hamon, Shadgan, Maharlo and salt lakes and Urmia, salt flats appear. These salt marshes also show a high albedo due to their white color. On the coastal of the Caspian Sea, low albedo is seen due to the decrease in land surface temperature and increase in soil moisture. This part of the research is in harmony with the findings of other studies conducted on the land surface albedo of Iran (Soltani Akmal, 1397; Kefayat Motlagh, 1400; Karbalaee, 1399). Also, the findings showed that the long-term average albedo of Iran is 12.5%, which is about half of the average planetary albedo (24%) (Zhang et al., 2010). This part of the research is also in line with the research of Karbalaee et al. (2021). [ABSTRACT FROM AUTHOR]

Water stress is a major environmental issue and has a significant impact on the sustainability of urban areas worldwide. This study examines the changes in land surface temperature (LST) and their relationship with variations in vegetation cover during wet and dry periods in Zayandeh Rood. To accomplish this, Landsat TM, ETM, and OLI satellite images from three dry years (2001, 2009, and 2018) and three wet years (2005, 2006, and 2020) were used. The research findings indicate that, in 2006, when water was flowing in the river bed, vegetation cover decreased from 36% (201 km2) to 23% (km2) in 2018, when the river bed had no water. Over the last two decades, the Earth's surface temperature has increased. The highest average temperature was observed in 2018, at 40/4 degrees Celsius, and the maximum temperature in the dry years of 2001, 2009, and 2018 was higher than in the wet years of 2005, 2006, and 2020. The highest density of heat islands was observed in regions 2, 4, 5, 6, 7, 14, and east of region 15, which is concentrated on barren lands and then on urban areas. In the dry year of 2009, the total area of temperature classes 42-49 increased by approximately 12% compared to the dry year of 2001 and also increased by about 25% in 2018. In contrast, it was zero in the wet years of 2005 and 2006, and in 2020, it decreased by around 34% compared to 2018. Furthermore, urban development, which has grown by 7/3% over the past two decades, has contributed to the reduction of vegetation and intensified the urban heat island effect. Examining the average temperature changes at different distances from the Zayande Rood indicated that the temperature increased by about 1 degree Celsius with the distance from the river. [ABSTRACT FROM AUTHOR]

Introduction: Dust storm is one of the desertification and land gegradation consequences especially in arid and hyper-arid regions of the world. Fine particles released from the Earth's surface can be released several kilometers and subsequently over long distances, sometimes even across continents. These particles play a key role in the Earth system by affecting the radiation balance as well as forest and ocean ecosystems. Weather is often considered as the main factor influencing the occurrence of dust storms. Dust storms have increased in recent years mainly due to continued droughts and global warming. Temperature is an important variable in climatilogy in terms of its direct effect on weather variables such as pressure, wind movement, cloud formation and precipitation. Based on previous studies, the increase in temperature along with the decrease in rainfall and the increase in wind speed provide the conditions for the creation of dust storms. In general, the review of the studies conducted in the field of evaluating dust changes and the effect of temperature parameters on it shows that it is possible to conduct these studies from different perspectives and methods. Therefore, it is necessary to find a comprehensive view on dust and factors affecting it by reviewing and evaluating the results of other studies so that the phenomenon can be controlled and managed. The number of dusty days in Khuzestan province is significant and has increased in recent years. The reason for that is its proximity to the great deserts of the neighboring countries. Therefore, this province is one of the challenging areas of the country in the field of dust management and control. The purpose of this study is to evaluate the trend of dust changes and the effect of temperature on this phenomenon. The results of this study help to better understand the phenomenon of dust in Khuzestan province. Materials and methods: In this research, three data groups were used including the monthly average temperature of 13 meteorological stations in the study area, the monthly average land surface temperature (LST), and the monthly average aerosol optical depth (AOD). First, the trend of dust changes was evaluated using the Mann-Kendall test. Then, the relationship between temperature parameters and dust was investigated using the correlation between the AOD in a buffer of 5 km around the selected station with temperature parameters including monthly average temperature and average monthly LST in buffers of 5, 10 and 20 km around the selected station. Results and Discussion: The evaluation of the average changes in the buffer of 5 km around the selected stations showed that in the months of February, July, December and April, no increasing trend was observed in any of the stations and even in the month of April, there was a decreasing trend in two stations. The trend of AOD changes was increasing with a probability of more than 90% in the months of August, September and November in seven, March in five, October in four, January in three, May in two and June in one station. The correlation results of temperature parameters and AOD showed that a very strong correlation between average LST and AOD has occurred only in the three months of May, June and July in different buffers. There are the highest number of strong correlations in the months of May to August between temperature parameters and AOD. Also, the highest number of very weak correlations occurred in April, March, November, and December. The highest number of very weak and weak correlations are related to air temperature (85 and 47 numbers, respectively). This shows that LST is more important than temperature in evaluating changes in dust intensity in the study area. Conclusion: According to the results of this research, it can be said that air temperature has a weak correlation with dust, and most of the strong and very strong correlations occurred with the average LST in different buffers, which shows that LST is more important than temperature in evaluating dust intensity changes in the study area. In general, evaluation and monitoring of dust changes can be effective in identifying new dust sources as well as evaluating wind erosion, dust control, and management activities. Therefore, it can be suggested that an online and up-to-date system based on remote sensing and artificial intelligence be designed and presented to evaluate and monitor dust changes. In this case, the management of the dust phenomenon in Iran will be more targeted and the dust sources can be identified faster. On the other hand, one should pay attention to the factors affecting these changes and evaluate their effect on dust. [ABSTRACT FROM AUTHOR]

Land surface temperature, as one of the important and fundamental parameters in climatology, indicates the relationship between the atmosphere and the Earth. Considering the environmental issues of cities, including the intensification of urban heat islands, accurately estimating LST and identifying its influencing factors play a significant role in urban thermal management and adopting adaptive strategies for heat islands. In this regard, this study compares two regression methods: multiple linear regression and random forest in order to estimate the LST. Daily nighttime MODIS images were used to extract the LST of Tabriz city during the summer. These images were processed in the Google Earth Engine platform and averaged for the period from 2018 to 2022. According to the results, the random forest showed significantly better performance with a coefficient of determination of 0.924 (RMS = 0.009) compared to multiple linear regression. The random forest was also used to determine the importance of the indices. Based on the index importance results, night lights (51/06%), sky view factor (48/01%) and frontal area index (45/27%) were the most important factor affecting the nighttime summer LST in Tabriz city, respectively. The findings of this study, in addition to revealing the strength of the random forest regression in estimating LST, also highlight the importance of various indices in the LST. In this context, the study's results will be practical for managing the thermal environment of Tabriz city and adopting mitigation strategies for its heat islands. [ABSTRACT FROM AUTHOR]

Background and Objectives Water resources management, especially irrigation practices, is heavily reliant on reference evapotranspiration (ET0). ET0 is the rate of evaporation and transpiration from a standard reference surface with a presumed surface resistance of 70 s m-1, the height of 0.12 m and an albedo of 0.23. Penman-Monteith FAO-56 (P-M FAO-56) approach is the most commonly used method for calculating ET0. In spite of the fact that FAO-PM is achievable, its implementation remains inconvenient because it requires a large amount of meteorological data, which is derived from standard meteorological observation stations. In the absence of complete climate data, it is highly desirable to have a model with fewer input climatic dates. Therefore, remote sensing methods have been used and improved over time to estimate ET0 at various spatial scales. Alternatively, it has been observed that the research community has become increasingly interested in obtaining data from metaheuristic algorithms that are based on artificial intelligence (AI). Methodology In this research, it has been attempted to estimate the amount of daily reference evapotranspiration (ET0) using two data-driven models, using a combination of inputs from meteorological station data and satellite imagery data from MODIS sensor, by considering different inputs from these sources. The models include the random forest (RF) and hybridized RF with genetic algorithm optimization (GA-RF). Moreover, the correlation of input variables with ET0 is evaluated and the possibility of training a simple and accurate machine learning model in the conditions of lack or absence of meteorological data using satellite image data is investigated. So, this study aimed to determine ET0 in the time period of 2003-2021 using land surface temperature (LST) data and leaf area index (LAI) acquired from MODIS sensor and Tabriz meteorological station data including maximum and minimum air temperatures (Tmax, Tmin), average temperatures (T), wind speeds (U2), average relative humidity (RH), maximum and minimum relative humidity (RHmax, RHmin), and sunny hours (n). For the study area, daily LST were extracted from the Terra (MOD11A1) and Aqua (MYD11A1) satellites. Moreover, the LST of Terra and Aqua satellites were combined, since the LST values had missing data due to the presence of clouds. Furthermore, MODIS MCD15A3H version 6.1 using fourday data from Terra and Aqua satellites was used to determine the leaf area index (LAI). The standard P-M FAO-56 method for calculating daily reference evapotranspiration was considered as the base method. The set of input parameters was considered based on the cross-correlation of the parameters with reference evapotranspiration obtained from the FAO-Penman-Monteith equation. Findings The results of two data-driven models including standalone random forest (RF) and hybridized RF model with genetic algorithm (GA) to estimate ET0 values were compared with calculated ET0 by P-M FAO-56 equation. The results indicated that all of the studied input variables are highly correlated with the target variable. Based on the P-M FAO-56 method, the average air temperature with the highest value (R²=0.903) and the wind speed with the lowest value (R²=0.282) has a high and low correlation with reference evapotranspiration. Also, by comparing LAI and LST MODIS parameters, LST has the highest correlation coefficient with ET0 with R²=0.865. A total of twelve scenarios for estimating ET0 are evaluated, each with a different set of input parameters. Based on the correlation between the parameters and ET0, the first ten scenarios are categorized. Additionally, the eleventh scenario is based only on satellite images, and the twelfth scenario is based solely on weather station data. Based on the results, the GA-RF-10 (R²=0.976, RMSE=0.200, MAPE=11.373, and MBE=0.028), which includes all input parameters, outperforms the other models. There was a greater degree of accuracy with the RF-10 (R²=0.949, RMSE=0.293, MAPE=16.442, and MBE=0.017) when compared with the other random forest models. Based on the comparison of scenario 11 (satellite image data) and scenario 12 (meteorological station data), it appears that scenario 12 is more accurate for both RF (R²=0.922, RMSE=0.357, MAPE=20.712, and MBE=0.009) and GA-RF (R²=0.944, RMSE=0.306, MAPE=17.037, and MBE=0.013) models. Despite the fact that only satellite image parameters did not provide accurate estimation of ET0 compared to independent meteorological parameters, the inclusion of these parameters in the ET0 estimation resulted in more acceptable results, demonstrating the importance of satellite image parameters. Thus, satellite data may be useful and recommended for estimating ET0, particularly in areas without meteorological stations. Conclusion According to the results obtained from the present research, GA-RF-10 model (R²=0.976, RMSE=0.200, MAPE=11.373 and MBE=0.027), which includes all input parameters; It has the best performance among other models. According to the results, although the satellite parameters alone had weaker estimates than the independent meteorological parameters, but by including these parameters in the estimation of ET0, more accurate results are obtained, which shows the importance of satellite image parameters in estimation. [ABSTRACT FROM AUTHOR]

Introduction Accurate estimation of reference evapotranspiration (ET0) is essential in water management in the agricultural sector, especially for arid and semi-arid climates. ET0 plays a vital role in the water and energy cycle and is an essential link between ecological and hydrological processes. Therefore, accurately estimating ET0 is a major issue for understanding the water cycle in continuous soil-plant-atmosphere systems. The traditional ET0 estimation methods are mainly based on physical principles, such as Priestley-Taylor, Hargreaves, and Samani, which have many limitations in accurate ET0 estimation in cases of minimum meteorological parameters (such as radiation solar, wind speed, and air temperature). Numerous studies have focused on ET0 estimation using terrestrial data. However, in the case of a lack of meteorological stations, the conventional methods of estimating ET0 using ground data will be inefficient, so remote sensing (RS) provides the possibility to fill such a gap, in such conditions, satellite images are the most effectivefor evaluating ET0 in large areas. Because satellite images have a suitable spatial and temporal resolution, the time series of satellite images can be used to estimate ET0. The successful estimation of ET0 from satellite images paved the way for its prediction using artificial intelligence models. The primary satellite imagery sources can be obtained from Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS), and Global Land Surface Satellite (GLASS). Remote sensing data provides the possibility of recording more information through satellite images. Remote sensing methods can be used to extract vegetation information and different types of radiation, which help estimate ET0. Materials and MethodsIn the current research, two different agro-climatic locations including Ahvaz and Tabriz stations were selected. According to De Martonne classification method, Ahvaz was classified as dry climate and Tabriz as semi-arid climate. In this research, random forest (RF) and multi-layer perceptron (MLP) algorithms have been used to estimate monthly ET0 in Ahvaz and Tabriz stations. The input parameters were selected from Landsat 8 and MODIS satellite images in the time period of 2014 to 2021. The utilized parameters were the monthly average, Landsat Land Surface Temperature (LSTLand), MODIS Land Surface Temperature (LSTMOD), Landsat Satellite Normalized Difference Vegetation Index (NDVILand) and MODIS Normalized Difference Vegetation Index (NDVIMOD). To evaluate the accuracy of the input parameters and models, the estimated monthly ET0 was evaluated with the monthly ET0 of the FAO-Penman-Monteth equation. Results and DiscussionThe input parameters for implemented models were Landsat land surface temperature (LSTLand), MODIS land surface temperature (LSTMOD), Landsat Satellite Normalized Difference Vegetation Index (NDVILand), and MODIS Normalized Difference Vegetation Index (NDVIMOD). Six possible scenarios were defined to estimate monthly ET0. The first two scenarios were considered as a single parameter (scenarios 1 and 2) and other scenarios were evaluated with two input parameters. Scenarios 3 and 4 were evaluated based on the parameters of the Landsat satellite and MODIS sensor, respectively. In scenarios 5 and 6, monthly ET0 was estimated with Landsat and MODIS NDVI and Landsat and MODIS LST, respectively, to determine the effect of NDVI and LST values on ET0 estimation. According to the obtained results, for the MLP and RF models in Ahvaz station, the value of R2 ranges from 0.440 to 0.972 and 0.271 to 0.983, respectively. In Ahvaz station, the lowest and highest RMSE is 0.279 mm.month-1 (RF-5 model) and 1.396 mm.month-1 (RF-4 model), respectively. Additionally, in this station, the highest and lowest values of NS are 0.962 (RF-5 model) and 0.042 (RF-4 model), respectively. According to the obtained results, in estimating the monthly ET0, the best performance is related to MLP-6 (R2=0.972, RMSE=0.348, and NS=0.940) and RF-4 (R2=0.983, RMSE=0.279, and NS=0.962). The highest and lowest values of R2 in Tabriz station were 0.988 and 0.186, respectively. Moreover, MLP-4 and RF-5 models in this station have the lowest and highest RMSE, respectively. The results showed that in Tabriz station, the best performances were related to MLP-4 (R2=0.988, RMSE=0.299, and NS=0.935) and RF-4 (R2=0.979, RMSE=0.302, and NS=0.933). In addition, in this station, the RF-5 model has the weakest performance among all models with R2=0.186, RMSE=1.169, and NS=0.012. ConclusionThe results showed that 1) the accuracy of monthly ET0 estimation in Ahvaz (arid climate) and Tabriz stations (semi-arid climate) with scenario 4 including LSTMOD and NDVIMOD was better than other investigated scenarios; 2) in estimating monthly ET0 using a single input parameter including LSTLand (scenario 1) and LSTMOD (scenario 2), in both Ahvaz and Tabriz stations, scenario 2 had better performance with both MLP and RF models; 3) estimation of monthly ET0 in Ahvaz and Tabriz stations has performed best with RF-4 and MLP-4 models, respectively, with LSTMOD and NDVIMOD input parameters (scenario 4); 4) in the comparison of scenario 5 (NDVILand, NDVIMOD) and scenario 6 (LSTLand, LSTMOD) in both RF and MLP models, scenario 6 has the best performance in estimating monthly ET0; and 5) in the comparison of monthly ET0 estimation in both arid and semi-arid climates, the best performance with a high correlation coefficient was obtained with the MLP model in semi-arid climates.

Introduction Globally, the physical growth of cities is recognized as a major threat to natural and ecological resources, with a variety of effects including land use change, increased pollution, increased earth surface temperatures, and climate change in both urban and non-urban areas. Planning to minimize the negative environmental effects of urban growth can be aided by quantifying and monitoring the changes caused by urban development in the Tasseled cap of the surface. Urban management and planning can also be derived from the quantitative and qualitative effects of climatic conditions on the type and amount of changes in the Tasseled cap of the surface due to physical expansion of the cities. Furthermore, weather conditions are the primary and effective factor on the type and amount of changes in the Tasseled cap properties of the surface. Considering field measurement is a time-consuming and expensive technique, remote sensing technology will be helpful and effective to overcome this challenge because of its large and continuous coverage, immediate access, and availability of data at various local, regional, and global scales. Theoretical Framework: Urbanization leads to an increase in land surface temperature (LST). In general, at the patch scale, the more compact the urban growth, the more easily the surface warmed. In most temperature zones, it was found that edge expansion and infilling had significant and favorable correlations with LST. Positive correlations were found in the warm temperature and plateau climatic zones, while negative correlations were seen in the subtropical and intermediate temperature zones, indicating that the influence of outliers on LST had opposite effects in these regions. The findings also demonstrated that LST was significantly influenced in diverse ways by patch area, industrial firm density, population density, and road density. This study further verified the existence of a scale effect; moreover, the results of patch-scale research based on the microscopic perspective were deemed to be more accurate. Overall, understanding the quantitative relationships between UGP and LST is helpful for assessing the complexity of urban climates and for providing a scientific basis for planners and urban managers to optimize urban layouts, (Rao et al., 2021: 105314). Methodology: The physical development of cities and their impact on the surface’s Tasseled cap have been the subject of numerous studies. This research was conducted with the aim of investigating the effect of different climatic conditions on the changes in Tasseled cap by using remote sensing. The cities of Kashan, Bandar Anzali, and Sirjan were chosen as the study regions for this research because of their diversity in geographical features, climatic features, and land cover. So, between 1991 and 2021, the effects of various climatic conditions and changes in thermal temperature on these cities were examined. For this purpose, satellite images of Landsat 5 TM sensor and Landsat 8 OLI sensor used. Conclusion: This study investigated the effect of climatic conditions on the spatial and temporal changes in Tasseled cap, including LST, in three cities in Iran: Kashan, Sirjan, and Bandar Anzali. The results showed that urban expansion has led to an increase in LST in all three cities. However, the increase in LST was more pronounced in Kashan, which has a hot and dry climate, than in Sirjan and Bandar Anzali, which have more humid climates. This is because the reduction of vegetation cover in urban areas reduces evaporation and transpiration, which leads to an increase in surface temperature. The study also found that the changes in other Tasseled cap indices, such as brightness and wetness, were also affected by climatic conditions. In the humid cities of Bandar Anzali and Sirjan, the increase in LST was accompanied by an increase in brightness and wetness. This is because the presence of vegetation helps to reflect sunlight and retain moisture, which helps to cool the surface. In contrast, in the hot and dry city of Kashan, the increase in LST was accompanied by a decrease in brightness and wetness. The findings of this study suggest that the planning and management of urban areas should take into account the local climatic conditions. In hot and dry climates, it is important to preserve vegetation cover to help reduce surface temperatures. In humid climates, it is important to design urban spaces in a way that maximizes the benefits of vegetation, such as shading and moisture retention. The study also suggests that future studies should consider the effects of climatic conditions on the changes in Tasseled cap in different cities. This will help to improve our understanding of the relationship between urban development and climate change. [ABSTRACT FROM AUTHOR]

1-Introduction Wind erosion is affected by climate change and several droughts have caused environmental degradation in arid regions. This process causes sand and dust storm (SDS) phenomenon which is one of the most important challenges in fragile areas. This phenomenon is harmful for human health and causes socio-economic problems. Over the past two decades, SDS have been increasing in Iran, particularly in the south-east of the country. Therefore, in order to manage this environmental phenomenon in arid areas, it is an urgent need to identify the dust storm sources. The changes of factors such as vegetation, soil moisture, slope, land temperature, and geological units have effective roles in the occurrence of dust storms. So, investigating the changes in these factors is an effective solution to identify dust storm sources. In this regard, using remote sensing data is one of the effective methods for the detection and mapping of dust storms resource, which combined with field methods, provides a coherent approach to manage and control this phenomenon. This study aimed to identify dust sources and storms in the southeast of Iran with emphasis on the Sistan dust storm by using field work and remote sensing data. 2-Methodology The study area is located in the south east of Iran and contains some of the border areas of Iran and Afghanistan (including some parts of the south Khorasan and Sistan and Baluchestan province in Iran as well as Farah and Nimroz province in Afghanistan with coordinates of 60˚ 16′ - 61˚ 36′ east longitude and 29˚ 8′ - 32˚ 32′ north latitude). In north of the study area, the mountainous and lowaltitude areas are intertwined but the southern area is mostly flat and lacks topography and natural features. The most important characteristics of this area is 120-day winds, which sometimes reach speeds of more than 120 kilometers per hour and is accompanied by high intensity of sand and dust storms. In this study, to identify dust storm sources, the combination of remote sensing, field study and wind analysis methods are used. To achieve this purpose, at first effective factors in the occurrence of dust storms such as vegetation, soil moisture, slope, land temperature, and geological units were prepared. In this regard, by using Modis satellite images, map of vegetation, soil moisture and land temperature were prepared by applying Soil Adjusted Vegetation Index (SAVI), integrating temperature vegetation dryness index (TVDI) and Land surface temperature (LST), respectively. Also slope map (using Aster satellite) and geological units were prepared. In the next stage, the fuzzy logic method was employed to combine layers and prepare the map of dust storms source, which is assessed by an error matrix and available dust source map. Detection of sand dust storms were performed by using BTD dust detection indicator. For winds analysis, we used WRplot view 8 Software. In the field study, several dust storms sources were determined and their characteristics were recorded. 3-Results The results showed that the ranges of SAVI index are + 0.6 to -1 and the highest amount of this index coordinate with highest soil moisture (TVDI) and the lowest land surface temperature (LST) area. This area is mostly related to rangeland, combination of wetland-rangeland, hand-planted forests and agricultural lands. The amount of SAVI index and soil moisture reaches its minimum value in the salt marsh lands, abandoned agricultural lands, desert areas and degraded rangeland. By merging the layers and preparing the map of dust storm sources, it was found that the critical dust storms resources coordinate with high land surface temperature and low vegetation and soil moisture areas. In addition, the highest dust source area is in the slopes of 0 to 4 % with geological units such as playa deposits, eolian deposits, alluvium sediment and wet playa deposit. According to the results, the incoming dust storms into the study area come from the east and southeast of south Khorasan province in Iran and the southwest of Farah province in Afghanistan. 4-Discussion & Conclusions In this study by using effective factors including vegetation, soil moisture, land surface temperature, slope and geological units, the sources of dust production were identified. According to the results, poor vegetation and low soil moisture in flat areas with sensitive formations are the key factors to create dust storm resources in the study area. Also, the most important active hot-spot areas for dust storm is concentrated in the southwest of Afghanistan and north of sistan region (dry lake bed of Hamoon). Therefore, it can be concluded that using satellite images and the studied factors, the dust sources can be properly identified. Furthermore, by using Time series images of modis and the updated dust source maps, a coherent approach can be provided in order to manage and control this phenomenon. [ABSTRACT FROM AUTHOR]

Introduction LST (LST) is one of the important parameters that affect the physical, chemical, and biological processes of the earth as well as environmental science and urban planning. Human activities such as land use changes and the development of urban areas led to an increase in the LST and the appearance of thermal islands. The main source of climate data such as temperature are synoptic stations, however, it is impossible and time-consuming to use traditional methods to estimate the LST for all types of earth conditions, on the other hand, synoptic stations only measure temperature information for specific points, and the obtained values are only related to that specific point; while according to the land cover and other conditions, the temperature in different parts of a region is different compared to the temperature recorded for a specific point and can be several degrees celsius lower or higher, therefore, it is necessary to use scientific methods that provide the possibility of calculating the temperature of any point on the earth's surface. At present, remote sensing images due to features such as wide and continuous coverage, low cost, timeliness, and the ability to obtain information in reflective and thermal ranges, are suitable tools for extracting LST and land use maps. Spatial analysis is one of the important subjects in the temporal and spatial evaluation of land surface data, which can be used to examine the spatial and temporal changes of spatial data in a region. Given that the data that are examined in environmental studies are not independent of each other in most cases and their dependence is due to the location of the observations in the studied space, which are called spatial data; Due to the existence of a spatial correlation between the data, the usual statistical methods are not a suitable method for examining these data, and spatial statistics can be used as a suitable option for analyzing these data. The aim of this research is to extract LST and land use map of Yazd county using a remote sensing technique. In this study, the spatial autocorrelation of LST in Yazd city and the identification of hot thermal clusters have been investigated using the global Moran statistic and the Getis-Ord GI statistic. Materials and MethodsIn this research, Landsat 8 satellite’s multi-spectral and thermal images have been used to extract the land use and LST in the study area, After performing the necessary corrections in the preprocessing stage, the land use map of the study area was prepared in 5 classes (built-up, vegetation cover, water bodies, bare land, and rock) using the support vector machine method and the overall accuracy and kappa coefficient were used to evaluate the classification result. In the next step, LST was extracted by the split window method. The relationship between LST and Soil adjusted vegetation index (SaVI) was investigated using regression analysis. In order to identify the spatial pattern of the LST, the global Moran index was used and hot spots were identified by Getis-Ord GI statistics. Results and DiscussionOur findings show that the kappa coefficient and overall accuracy were equal to 0.96% and 98.99%, respectively, bare lands are the most, and water bodies have the least area, equal to 76.16 and 0.09%, respectively. The average LST was 50.83°C. The result showed that the type of land use had an effect on LST, the water bodies had the lowest, and barren lands had the highest mean LST, equal to 36.91 and 52.13 °C, respectively. Vegetation is one of the factors that regulate the LST, areas without vegetation have a maximum LST and areas with high density vegetation have minimum LST .Based on the results, the vegetation quality of the study area was poor and its average temperature was 45.61°C. The mean of SAVI index was equal to 0.09 and correlation analysis showed a negative correlation between SAVI index and LST (r = -051). The analysis of spatial correlation with global Moron indexes showed that the LST of Yazd has a spatial structure, in other words, LST is distributed in a cluster form, Based on the results of the Getis-Ord GI statistic, the area of hot and cold spots was equal to 66.86% and 27.4%, respectively. In general, parks, cultivated lands, tree and forest cover and water areas, formed the cold spot areas of yazd city, and the hot spot areas of yazd city were located in the industrial areas and surrounding urban lands, hospitals, passenger terminals, gas stations, places near busy roads and bare and uncovered lands. Conclusion The results showed a strong relationship between land use and LST. Based on the results, the LST data of Yazd has a spatial structure pattern, barren lands and industrial areas formed hot thermal islands, and vegetation and water bodies formed cold thermal islands in the study area; the wide area of barren lands, the lack and poor vegetation cover due to the lack of rainfall and drought are factors affecting the LST and the creation of hot thermal islands in the study area. The result showed a negative relationship between LST and SAVI, the vegetation of the study area is weak and its temperature is high. Considering the role of vegetation in adjusting LST, it is recommended to take necessary management measures in order to improve the quality of vegetation and reduce bare land in the study area, and also prevent the conversion of natural land uses into built-up land. The results of this research can be used by managers and planners for better urban management. The results of this research confirm the capability of remote sensing in environmental studies, it is suggested to identify thermal islands in other seasons and at night and compare the results with the results of this research.

Introduction Accurate estimation of reference evapotranspiration (ET0) is essential in water management in the agricultural sector, especially for arid and semi-arid climates. ET0 plays a vital role in the water and energy cycle and is an essential link between ecological and hydrological processes. Therefore, accurately estimating ET0 is a major issue for understanding the water cycle in continuous soil-plant-atmosphere systems. The traditional ET0 estimation methods are mainly based on physical principles, such as Priestley-Taylor, Hargreaves, and Samani, which have many limitations in accurate ET0 estimation in cases of minimum meteorological parameters (such as radiation solar, wind speed, and air temperature). Numerous studies have focused on ET0 estimation using terrestrial data. However, in the case of a lack of meteorological stations, the conventional methods of estimating ET0 using ground data will be inefficient, so remote sensing (RS) provides the possibility to fill such a gap, in such conditions, satellite images are the most effectivefor evaluating ET0 in large areas. Because satellite images have a suitable spatial and temporal resolution, the time series of satellite images can be used to estimate ET0. The successful estimation of ET0 from satellite images paved the way for its prediction using artificial intelligence models. The primary satellite imagery sources can be obtained from Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS), and Global Land Surface Satellite (GLASS). Remote sensing data provides the possibility of recording more information through satellite images. Remote sensing methods can be used to extract vegetation information and different types of radiation, which help estimate ET0. Materials and Methods In the current research, two different agro-climatic locations including Ahvaz and Tabriz stations were selected. According to De Martonne classification method, Ahvaz was classified as dry climate and Tabriz as semi-arid climate. In this research, random forest (RF) and multi-layer perceptron (MLP) algorithms have been used to estimate monthly ET0 in Ahvaz and Tabriz stations. The input parameters were selected from Landsat 8 and MODIS satellite images in the time period of 2014 to 2021. The utilized parameters were the monthly average, Landsat Land Surface Temperature (LSTLand), MODIS Land Surface Temperature (LSTMOD), Landsat Satellite Normalized Difference Vegetation Index (NDVILand) and MODIS Normalized Difference Vegetation Index (NDVIMOD). To evaluate the accuracy of the input parameters and models, the estimated monthly ET0 was evaluated with the monthly ET0 of the FAO-Penman-Monteth equation. Results and Discussion The input parameters for implemented models were Landsat land surface temperature (LSTLand), MODIS land surface temperature (LSTMOD), Landsat Satellite Normalized Difference Vegetation Index (NDVILand), and MODIS Normalized Difference Vegetation Index (NDVIMOD). Six possible scenarios were defined to estimate monthly ET0. The first two scenarios were considered as a single parameter (scenarios 1 and 2) and other scenarios were evaluated with two input parameters. Scenarios 3 and 4 were evaluated based on the parameters of the Landsat satellite and MODIS sensor, respectively. In scenarios 5 and 6, monthly ET0 was estimated with Landsat and MODIS NDVI and Landsat and MODIS LST, respectively, to determine the effect of NDVI and LST values on ET0 estimation. According to the obtained results, for the MLP and RF models in Ahvaz station, the value of R² ranges from 0.440 to 0.972 and 0.271 to 0.983, respectively. In Ahvaz station, the lowest and highest RMSE is 0.279 mm.month-1 (RF-5 model) and 1.396 mm.month-1 (RF-4 model), respectively. Additionally, in this station, the highest and lowest values of NS are 0.962 (RF-5 model) and 0.042 (RF-4 model), respectively. According to the obtained results, in estimating the monthly ET0, the best performance is related to MLP-6 (R²=0.972, RMSE=0.348, and NS=0.940) and RF-4 (R²=0.983, RMSE=0.279, and NS=0.962). The highest and lowest values of R² in Tabriz station were 0.988 and 0.186, respectively. Moreover, MLP-4 and RF-5 models in this station have the lowest and highest RMSE, respectively. The results showed that in Tabriz station, the best performances were related to MLP-4 (R²=0.988, RMSE=0.299, and NS=0.935) and RF-4 (R²=0.979, RMSE=0.302, and NS=0.933). In addition, in this station, the RF-5 model has the weakest performance among all models with R²=0.186, RMSE=1.169, and NS=0.012. The results showed that 1) the accuracy of monthly ET0 estimation in Ahvaz (arid climate) and Tabriz stations (semi-arid climate) with scenario 4 including LSTMOD and NDVIMOD was better than other investigated scenarios; 2) in estimating monthly ET0 using a single input parameter including LSTLand (scenario 1) and LSTMOD (scenario 2), in both Ahvaz and Tabriz stations, scenario 2 had better performance with both MLP and RF models; 3) estimation of monthly ET0 in Ahvaz and Tabriz stations has performed best with RF-4 and MLP-4 models, respectively, with LSTMOD and NDVIMOD input parameters (scenario 4); 4) in the comparison of scenario 5 (NDVILand, NDVIMOD) and scenario 6 (LSTLand, LSTMOD) in both RF and MLP models, scenario 6 has the best performance in estimating monthly ET0; and 5) in the comparison of monthly ET0 estimation in both arid and semi-arid climates, the best performance with a high correlation coefficient was obtained with the MLP model in semi-arid climates. [ABSTRACT FROM AUTHOR]

IntroductionAlthough the air layer adjacent to the earth's surface - the boundary layer - is a small fraction of the entire atmosphere, the processes that take place on a small scale are very important to human life and activites. Among living organisms, plants and especially trees have undeniable effects on surface temperature and especially in urban environments have several balancing effects. This research was carried out using Landsat 8 satellite imagery and with Arc GIS software to compare the surface temperature of the earth in two areas with vegetation of coniferous trees (Chitgar Park) and broadleaf trees (Shahid Chamran Park). The values ​​of Radiance, Reflectance, Brightness Temperature, Normalized Difference Vegetation Index, Proportion of Vegetation and Emissivity and then Land Surface Temperature were calculated and generated. A total of 1700 points were harvested from Chitgar Park and 800 points from Chamran Park. In SPSS software, Leven test (F) statistics was used to prove the homogeneity of variances of the samples and parametric tests (T with two independent samples) were used to prove the significant difference between the surface temperature of the earth in the mentioned areas. According to Leven test, the value was Sig = 0.409 (P_value), which confirms the homogeneity and equality of variance of the studied samples. Also, in the T test, the value was Sig = 0.000, which is less than 0.05, which means a significant difference. Therefore, the difference between the surface temperature data of the two parks was proved. Also, by comparing the graphs of LST values ​​in the two groups, we found that Chitgar Park has a higher surface temperature than Chamran Park. In the current dilemma of the century, global warming, knowing these local realities and providing logical solutions to reduce surface temperature at the regional and regional scales as a whole can effectively solve the problem of global warming on a global scale.Data and Method The data used in this study is a Landsat 8 satellite imagery with the acronym: 8 (LC08_L1TP_165035_20190706) is LANDSAT.Retrieved July 6, 2019 from the USGS website.Production of component images for Shahid Chamran Parks in Karaj and Chitgar in Tehran: The surface temperature image was generated step by step using the Landsat 8 satellite image using the Raster Calculator command in the ArcMap software environment. First, relevant and effective indicators in calculating the surface temperature of the earth, Top of atmospheric radiance, reflectance, Brightness Temperature, normalized difference vegetation index, proportion of vegetation, emission coefficient (emissivity), calculation and their images are produced and then the land surface temperature, It was calculated and produced according to the following mathematical formulas.Step 1: Produce a spectral radius image from above the atmosphere To obtain the brightness temperature, the image must first be converted to radius. Therefore, the gray DN values of bands 10 and 11 of the Landsat 8 satellite TIRS sensor should be converted to high atmospheric radius separately with the help of the MTL file, which is an extension of the Landsat image (Tables 1, 2 and 3).Formula (1) :Calculate the radius of the upper atmosphere TOA (Lλ) = ML * Qcal + ALLλ = (Watts / (m2 * srad * μm)) The radius of the atmosphere in terms ofML = Multi-band radius_ 10 bandStep 2: Produce an image of the light temperature above the atmosphere After converting the DN values of bands 10 and 11 to high atmospheric radii, we converted these two corrected bands to Brightness Temperature.BT = (K2 / (ln (K1 / L) + 1)) - 273.15 Formula (2): Calculation of Brightness Temperature BT = Atmospheric Brightness Temperature (° C)Lλ = (Watts / (m2 * srad * μm)) Radius of the atmosphere in terms ofBT = (1321.0789 / Ln ((774.8853 / “% TOA%”) + 1)) - 273.15K1 = K1 Constant Band (No.), K2 = K2 Constant Band (No.)Step 3: Produce vegetation index image formula (3): normalized difference vegetation index image was generated usingNDVI = (Band 5 - Band 4) / (Band 5 + Band 4)Step 4: Produce a proportion of vegetation imageThe proportion of vegetation image was generated using normalized difference vegetation index.formulas (4):Calculate the proportion of vegetation PV = (NDVI - NDVImin / NDVImax- NDVImin) 2PV = Square (("NDVI" - 0.216901) / (0.632267 - 0.216901))Step 5: Produce the Emissivity image Emissivity image was generated using formula (5)ε = 0.004 * PV + 0.986 Formula (5): Calculate the Emissivity coefficientStep 6: Produce an image of the earth's surface temperature Land surface temperature image was generated using formula (6).Formula (6) :Calculate ground land surface temperatureLST = (BT / (1 + (0.00115 * BT / 1.4388) * Ln (e)))Results and Discussion Text Comparison of surface temperature phenomena (LST) According to Table (6), the highest land surface temperature with 44.42 ° C belongs to Chitgar Park, which is covered with coniferous trees, and the lowest in Shahid Chamran Park, in Karaj with 28.09 ° C with broadleaf trees. Has been. According to Tables (7) and (8), the lowest temperature of Chamran Park is 28.09 ° C and the highest is 36.51 ° C and the lowest temperature of Chitgar Park is 34.74 ° C and the highest is 44.42 ° C. . According to Figure (22), Chitgar Park with an average surface temperature of 38.92 ° C is warmer than Shahid Chamran Park with an average land surface temperature of 31.39 ° C. Figure (23) shows a red graphic showing the surface temperature of the ground in Chitgar Park with coniferous species (pine) and the blue diagram shows the surface temperature of Shahid Chamran Park in Karaj with broadleaf species. It is clear that the temperature is significantly higher in Chitgarh Park. The range of temperature fluctuations in Shahid Chamran Park is between 36.51 - 28.09 ° C and in Chitgar Park is between 42 / 44-74 / 34 which is exactly shown in the diagram. The fact that the red chart is higher than the blue chart explains this correctly. This is due to the lower density of trees in Chitgarh Park as well as the predominant tree species (needle-shaped) due to less shading and more input radiation. T test with two independent samples: This test, which is a parametric test, was used to prove a significant difference between the earth's surface temperature in areas with coniferous and deciduous trees. Leven test (F) was used to prove the homogeneity of sample variances and t-test with two independent samples was used to examine the homogeneity of the means of the two statistical populations, which resulted in the following results. As can be seen in Table (12), the value = 0.409 Sig, which is the same value as P_value, is greater than 0.05, ie the variance of the communities is homogeneous and equal. 0.05 is less, which means that the difference is significant. Due to religion, the difference between the land surface temperature data of Shahid Chamran and Chitgar parks is proved.ConclusionAccording to all the findings, Chitgar Park has a higher land surface temperature than Chamran Park, which is due to the lower density of trees and also the type of dominant tree species (needle-shaped). Coniferous species that take up less space than broadleaf species and have less shading. They also make it possible for the sun to collide with the ground due to the fact that the leaves of the adjacent trees do not meet, and this is an important factor in raising the surface temperature in the mentioned park. Species compatible with the climate of the study areas are broadleaf species because they have more leaves shading and care than coniferous species and ultimately cause more climate adjustment. The difference in temperature between the two parks confirms this fact. In the current dilemma of the century, global warming, knowing these local realities and providing logical solutions to reduce surface temperature at the regional and regional scales as a whole can effectively solve the problem of global warming on a global scale.

Remote sensing data play an important role in environmental planning and monitoring. The current study aimed to investigate the land surface temperature (LST) and the effect of environmental factors on the LST, to identify the temporal-spatial patterns and determine the hot spots in the period of 2013 to 2019, using Landsat 8 images. The effect of spectral indices: Normalized Difference Build-up Index (NDBI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) on the surface temperature was investigated. Results indicated that the lowest average temperature has occurred in 2019 and the highest LST was in the 2017. The results of Moran's index correlation also showed that the most clustering pattern of LST, with the Moran value of 0.85 was obtained in 2019, the highest correlation between LST and NDBI, with the R value of 0.76 in the 2015, the highest correlation between LST and NDVI in the 2015 (R = -0.56), and the highest correlation between LST and NDWI in 2013 (R = -0.53). Rasht watershed in Guilan province is affected by human factors and land use changes. Therefore, it is recommended to increase the vegetation cover in urban areas, reduce the change of pasture to agricultural area, and reduce forest destruction.

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. [ABSTRACT FROM AUTHOR]

Urban Heat Island (UHI) is an important factor for heat change and balance in global studies and is considered as a proxy for climate change. Studying this phenomenon and investigating its mechanism are very important in urban planning. During the last two decades, the great need for earth surface temperature information for environmental studies and land resource management activities has turned earth surface temperature remote sensing into one of the most important scientific issues (Sobrino et al., 2004). In a research, Hay et al. (2018) studied the effect of the topographical factor on LST in mountainous areas. The results of their research showed that there was a high correlation between topography parameters and surface temperature. For the area studied in this research, the relationship between LST and altitude was inversely linear. Tran et al. (2017) used Landsat 5,7,8 in order to determine the relationship between land cover change and land surface temperature of the inner city of Hanoi (plain and flat area). The results revealed that: a) LST depends on the non-linear method of LULC types; b) Foci analysis by using the Getis Ord Gi* statistic allows analysis of LST pattern change over time; c) UHI is affected from both the urban perspective and type of urban development; d) investigation of LST pattern prediction and UHI effect can be done by the proposed model by using nonlinear regression and simulated LULC change scenarios. In a research in a city of China, the harmful effects of land cover and land use changes on the Earth's surface temperature were investigated through vegetation indicators based on three image sensors of TM and ETM+. For this purpose, the researchers obtained the Temperature Vegetation Index (TVX) from the images. Their results showed that land use change is an important factor for increasing the Earth's surface temperature. It also showed high temperature in areas with scattered vegetation and low temperature in areas with dense vegetation. It has been used in the statistical period of 2013-2015 in the city of Isfahan as well. The results of this research showed that there was a sharp thermal gradient due to the presence of Urban Cold Islands (UCI) between the center and suburbs. The largest UCI was identified in Region 6 (Ahmadi et al., 2016). [ABSTRACT FROM AUTHOR]

Introduction LST (LST) is one of the important parameters that affect the physical, chemical, and biological processes of the earth as well as environmental science and urban planning. Human activities such as land use changes and the development of urban areas led to an increase in the LST and the appearance of thermal islands. The main source of climate data such as temperature are synoptic stations, however, it is impossible and time-consuming to use traditional methods to estimate the LST for all types of earth conditions, on the other hand, synoptic stations only measure temperature information for specific points, and the obtained values are only related to that specific point; while according to the land cover and other conditions, the temperature in different parts of a region is different compared to the temperature recorded for a specific point and can be several degrees celsius lower or higher, therefore, it is necessary to use scientific methods that provide the possibility of calculating the temperature of any point on the earth's surface. At present, remote sensing images due to features such as wide and continuous coverage, low cost, timeliness, and the ability to obtain information in reflective and thermal ranges, are suitable tools for extracting LST and land use maps. Spatial analysis is one of the important subjects in the temporal and spatial evaluation of land surface data, which can be used to examine the spatial and temporal changes of spatial data in a region. Given that the data that are examined in environmental studies are not independent of each other in most cases and their dependence is due to the location of the observations in the studied space, which are called spatial data; Due to the existence of a spatial correlation between the data, the usual statistical methods are not a suitable method for examining these data, and spatial statistics can be used as a suitable option for analyzing these data. The aim of this research is to extract LST and land use map of Yazd county using a remote sensing technique. In this study, the spatial autocorrelation of LST in Yazd city and the identification of hot thermal clusters have been investigated using the global Moran statistic and the Getis-Ord GI statistic. Materials and Methods In this research, Landsat 8 satellite’s multi-spectral and thermal images have been used to extract the land use and LST in the study area, After performing the necessary corrections in the preprocessing stage, the land use map of the study area was prepared in 5 classes (built-up, vegetation cover, water bodies, bare land, and rock) using the support vector machine method and the overall accuracy and kappa coefficient were used to evaluate the classification result. In the next step, LST was extracted by the split window method. The relationship between LST and Soil adjusted vegetation index (SAVI) was investigated using regression analysis. In order to identify the spatial pattern of the LST, the global Moran index was used and hot spots were identified by Getis-Ord GI statistics. Results and Discussion Our findings show that the kappa coefficient and overall accuracy were equal to 0.96% and 98.99%, respectively, bare lands are the most, and water bodies have the least area, equal to 76.16 and 0.09%, respectively. The average LST was 50.83°C. The result showed that the type of land use had an effect on LST, the water bodies had the lowest, and barren lands had the highest mean LST, equal to 36.91 and 52.13 °C, respectively. Vegetation is one of the factors that regulate the LST, areas without vegetation have a maximum LST and areas with high density vegetation have minimum LST .Based on the results, the vegetation quality of the study area was poor and its average temperature was 45.61°C. The mean of SAVI index was equal to 0.09 and correlation analysis showed a negative correlation between SAVI index and LST (r = -051). The analysis of spatial correlation with global Moron indexes showed that the LST of Yazd has a spatial structure, in other words, LST is distributed in a cluster form, Based on the results of the Getis-Ord GI statistic, the area of hot and cold spots was equal to 66.86% and 27.4%, respectively. In general, parks, cultivated lands, tree and forest cover and water areas, formed the cold spot areas of yazd city, and the hot spot areas of yazd city were located in the industrial areas and surrounding urban lands, hospitals, passenger terminals, gas stations, places near busy roads and bare and uncovered lands. Conclusion The results showed a strong relationship between land use and LST. Based on the results, the LST data of Yazd has a spatial structure pattern, barren lands and industrial areas formed hot thermal islands, and vegetation and water bodies formed cold thermal islands in the study area; the wide area of barren lands, the lack and poor vegetation cover due to the lack of rainfall and drought are factors affecting the LST and the creation of hot thermal islands in the study area. The result showed a negative relationship between LST and SAVI, the vegetation of the study area is weak and its temperature is high. Considering the role of vegetation in adjusting LST, it is recommended to take necessary management measures in order to improve the quality of vegetation and reduce bare land in the study area, and also prevent the conversion of natural land uses into built-up land. The results of this research can be used by managers and planners for better urban management. The results of this research confirm the capability of remote sensing in environmental studies, it is suggested to identify thermal islands in other seasons and at night and compare the results with the results of this research. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is one of the most important parameters in agricultural water management which has spatial and temporal variations. Remote sensing-based algorithms of ET estimation have been applied in several studies. Using MODIS satellite images ET can be estimated on a daily basis at the pixel size of 1000 meters while using Landsat 8 images can be estimated every 16 days in the pixel size of 100 meters. Various methods are available for combining satellite images with low (such as MODIS) and medium (such as Landsat 8) spatial resolution to estimate evapotranspiration on a daily basis in the lower pixel size. Downscaling is one of these methods which was evaluated in this study is one of the plains in Kermanshah province. For this purpose, the actual ET over the study area was calculated during the summer of 2014 using the SEBAL algorithm based on six satellite images of Landsat 8 and MODIS. Although there are several methods for downscaling the estimated evapotranspiration based on MODIS images, in this study two simple downscaling methods (ratio and subtraction). were used to convert the Landsat 8 image scale. Results of downscaled images of MODIS were compared with the results of Landsat 8 images graphically and statistically. Given the fact of small size and mixed land use types in the study area results of downscaling methods were not acceptable.

Introduction Drought analysis in agriculture can not only be achieved by measuring precipitation changes but also by using other parameters such as soil moisture. Due to the fact that soil moisture affects plant growth and yield, it is often considered for monitoring agricultural drought. Remote sensing data are often provided from three sources: microwave, visible and thermal. Most satellite soil moisture-based algorithms rely on passive microwave images, active microwaves, or a combination of data from several different sensors. Among the various remote sensing methods, the microwave electromagnetic spectrum has fewer physical limitations than other spectrum in measuring soil moisture. However, microwave soil moisture data often have very large pixel dimensions (more than 10 km), making it difficult to use them on a small scale.Materials and Methods In this study, in order to calculate the agricultural drought index at the field-scale, AMSR2 Retrieval data were calibrated first using field moisture measurement data in the Neishabour plain during 2017 to 2019. During the research period, 560 soil samples (20 samples in 28 shifts) were collected and soil moisture was measured in the laboratory of the Department of Water Science and Engineering, Ferdowsi University of Mashhad. LPRM_AMSR2_ SOILM3_001 is one of the third level products of the AMSR2 sensor, which is produced on a daily basis with a spatial resolution of 25 × 25 km2. Land surface parameters including surface temperature, surface soil moisture and plant water availability were obtained by passive microwave data using the Land parameter Retrieval Method (LPRM). Then, by using Modis sensor images (NDVI and LST), linear downscaling equations were extracted. The dimensions of the AMSR2 images were reduced from 25 kilometers to 1000 meters using these equations. In next step, SMADI Agricultural Drought Index, which is a combination of vegetation characteristics, soil moisture and land surface temperature, was used to monitor agricultural drought at the field-scale. Statistical indicators such as coefficient of determination (R^2), mean absolute error (MAE) and root mean square error (RMSE) were also used to evaluate the statistical performance.Results and DiscussionBy visual analysis of the role of vegetation and land unevenness, it was found that these two factors affect the regression relationships extracted for calibration of remote sensing data. The RMSE and MAE values for the regression equations used in the calibration process were calculated in the range of 1.6 to 4%, which can be considered acceptable in comparison with the mean values of the soil moisture data (15 to 20). The results showed that changes in SMADI index in three land use zones including rainfed cultivation (R1), medium rangeland (R2) and poor rangeland (R3) have experienced a similar trend to precipitation changes, illustrating that precipitation is one of the most effective factors in major changes in SMADI agricultural drought index fluctuations. It was also observed that SMADI index changes with a delay of 1 to 8 days compared to the precipitation changes in all three zones. In all three zones, the SMADI index followed a similar trend to in-situ soil moisture changes. At mot 80% of the changes in SMADI-R1 index can be explained by in-situ SM-R1, and the rest of the changes were related to other environmental factors or measurement error. This decreases to 68% in the R3 zone. It should be noted that soil moisture monitoring can more accurately reflect the impact of environmental factors on the changes in agricultural drought index such as SMADI than other variables; because the rainfall recorded at the meteorological station does not necessarily occur uniformly throughout the study area. On the other hand, any amount of precipitation will not necessarily lead to an effective change in soil moisture storage. This also renders assessment of the performance of agricultural drought indicators difficult.Conclusion Examination of statistical indices of coefficient of determination (R2), mean absolute error value (MAE) and root mean square error (RMSE) showed that the algorithm used in downscaling as well as estimating SMADI agricultural drought index is well able to reflect the interactions between precipitation, soil moisture, vegetation and changes in canopy temperature profile. This feature justifies and strengthens its application in agrometeorological analysis.

The southern regions of Kerman Province have repeatedly encountered dust storms. Therefore, the objective of this study was to identify dust sources using effective parameters such as vegetation cover, land surface temperature, soil moisture, soil texture, and slope as well as to detect dust storms originating from these regions based on 31 MODIS images in 2016 and SRTM data. After normalizing parameters, the dust source map was prepared by fuzzy logic and assessed with an error matrix and available dust source map. Results showed that 30.5% of the study area was classified as a low source of dust, 39.55% as moderate, and 29.85% as severe-very severe. The overall accuracy of the produced map was about 70% and the producer and user accuracy of the severe-very severe class was more than 87%. The detection of dust storms originated from the identified dust sources also confirmed a crisis situation in the region. Due to the repeatability and continuity of obtained dust source map at pixel scale, it can be used to update available dust source maps and manage dust crisis in the region, properly.

Remote sensing data play an important role in environmental planning and monitoring. The current study aimed to investigate the land surface temperature (LST) and the effect of environmental factors on the LST, to identify the temporal-spatial patterns and determine the hot spots in the period of 2013 to 2019, using Landsat 8 images. The effect of spectral indices: Normalized Difference Build-up Index (NDBI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) on the surface temperature was investigated. Results indicated that the lowest average temperature has occurred in 2019 and the highest LST was in the 2017. The results of Moran's index correlation also showed that the most clustering pattern of LST, with the Moran value of 0.85 was obtained in 2019, the highest correlation between LST and NDBI, with the R value of 0.76 in the 2015, the highest correlation between LST and NDVI in the 2015 (R = -0.56), and the highest correlation between LST and NDWI in 2013 (R = -0.53). Rasht watershed in Guilan province is affected by human factors and land use changes. Therefore, it is recommended to increase the vegetation cover in urban areas, reduce the change of pasture to agricultural area, and reduce forest destruction. [ABSTRACT FROM AUTHOR]

Expansion of cities on the fringes has caused the loss of natural landscapes, agricultural lands and environmental problems. Restoring and improving these effects based on the principles of environmental design is a necessary matter. Considering the current state of development of Arak city on the eastern edge, it is very important to pay attention to this part due to the value of high-quality soil, the existence of rural areas, gardens and agricultural lands. In this paper, a part of the eastern fringe of Arak was studied by satellite data and remote sensing-based ecological Index (RSEI) that includes four sub-indices: built-up area, vegetation cover, land surface temperature and land surface moisture. Integrating them was done by principal component analysis. The results showed that the lower ecological quality in different areas was caused by the development of infrastructures, impervious urban surfaces, industrial lands and in general the spaces resulting from the pressure of human activities and the ecological quality was higher in gardens, active rural agricultural lands and scattered parks and green spaces. Then, the results were analyzed in the pressure-state-response framework, and a part of the land named "Qanat Naseri" was redesigned in order to maintain and expand the existing ecological values based on the results and analysis. [ABSTRACT FROM AUTHOR]

In the recent decades, the geometric layouts and morphological elements of cities have increasingly been recognised as influencing the formation and intensification of the urban heat island phenomenon, with many researches conducted on finding the indicators representing these issues in various scales. Although the spatio-temporal effects of these indicators, as well as models for their interpretation and classification has been the subject of some recent studies worldwide, these studies are almost entirely missing for Iranian cases. The present paper studies and assesses the relationship between the land surface temperature and the morphological parameters of the built environment according to the concept of local climate zones, which is a standard framework for describing the form and function of cities in urban climate studies. The local microclimate zones of the Tehran metropolitan area have been studied and assessed based on the method proposed by the World Urban Database and Access Portal Tools. They are classified under seventeen groups in terms of their building geometries and land cover, with a resolution of 100 meters, using Landsat 8 satellite imagery data over the course of a year, integrated by geographic information systems and earth monitoring tools such as Google Earth, ArcGIS and SAGA GIS. The results of spatial and statistical analyses showed that over a year, the highest intensity of surface urban heat islands was related to areas without structures, as well as large-scale, low-rise industrial zones, with high-density, high-rise, and low-density low-rise classes having the least differences with the average land surface temperature. Also, the high-density, low-rise and mid-rise structures have had the lowest average temperature in the whole city during the year.

Land Surface Temperature (LST) is an important parameter in weather and climate systems. Satellite remote sensing is a unique way to estimate this important parameter. However, satellite products have either low spatial resolution or low temporal resolution that limits their potential use in various studies. In recent years, the use of Spatio-temporal fusion techniques to produce high resolution simultaneous spatial and temporal images has been extensively investigated. In this study, a Flexible Spatio-temporal Data Fusion (FSDAF) was used to produce Landsat-like LST images with Landsat spatial resolution and MODIS temporal resolution. The quantitative and qualitative validation of the images was performed by comparing them with the Actual Landsat LST images. The results showed that the FSDAF algorithm has high accuracy in estimating daily LST data both qualitatively and quantitatively. The RMSE and MAE parameters of the images produced compared to the actual Landsat images were 1.18 to 1.71 and 0.88 to 1.29°C, respectively. The correlation coefficient above 0.87 and bias between -0.6 to 1.45°C also confirms the high accuracy of the algorithm in estimating Landsat-like land surface temperature on a daily time scale.

Temperature is one of the most important physical parameters that control the transfer and exchange of energy between different layers of the earth and the atmosphere. LST estimation methods based on satellite images require surface and atmospheric parameters such as surface emissivity, average air temperature, atmospheric transfer coefficient, and water vapor as input. Uncertainty in these parameters causes errors in the retrieval of land surface temperature. This study aimed to compare the accuracy of different methods for estimating atmospheric water vapor in estimating land surface temperature using Landsat 8 images. In this study, atmospheric water vapor was estimated using FLAASH atmospheric correction methods, MODIS sensor images, and SWCVR method. Then, the impact of atmospheric water vapor on land surface temperature accuracy was investigated using the split window and single-channel methods. Validation of Land surface temperature images was performed using cross-validation and ground measurement methods. Therefore, 20 Landsat 8 images related to 2018 and 2019 were used to estimate atmospheric water vapor by the FLAASH atmospheric correction and SWCVR methods, and land surface temperature estimation. MODIS radiance images were used to estimate atmospheric water vapor and the land surface temperature product of this sensor was used for cross-validation. The surface temperature was measured using a thermometer in places with homogeneous cover, for ground-based validation. Results showed that among water vapor estimation methods, the SWCVR method is more suitable for estimating land surface temperature and the split-window method based on the SWCVR method shows the lowest RMSE and MADE at 3.47 and 3.18. Results of RMSE image classification of split-window algorithm based on the SWCVR showed that 1.67% of the area has an error of more than 4 °C and 98% of the study area has less than 4 °C error.

Introduction: Climate change in cities has been in close focus in the past few decades. Heat stress in urban areas has also had adverse effects on human health and is expected to worsen in the future due to the global warming. In recent years, the mapping of urban biophysical and thermal conditions as well as their relation to land use and land cover (LULC) and air pollution has attracted increasing interest. In most cases, UHIs are a result of the use of fossil fuels which affect air-pollution in urban areas. It is a well-known fact that an increase in the population, particularly in developing countries, intensifies the pressure on natural resources. Rapid population growth, in conjunction with urbanization, expansion, and encroachment into limited agricultural and green areas lead to the destruction of vegetation coverage. It is obvious that such destruction, in combination with population growth, causes environmental impacts such as intensified land surface temperature (LST), UHIs and air-pollution. LST is considered as one of the important parameters in urban climate, which directly controls the UHI effects. LST is believed to be closely associated with LULC, resulting in heat islands. In general, thermal remote sensing is regarded as an efficient technology which provides a synoptic and uniform means of studying UHI effects on a regional scale. In the absence of a dense network of land-based meteorological stations, the spatiotemporal distribution of LSTs from thermal remote sensing imagery can be used as data to support UHI management and, potentially, countermeasures. Thermal satellite-measured LST has been utilized in various studies on heat balance, climate modelling, and global change. This is because LST is determined by the effective radiating temperature of the Earth’s surface to assess UHIs. There some methods devised in this regard, such as SplitWindow Algorithm (SWA) and Single Channel Method (SChM). In this line, the present research seeks to investigate the relationship between LST and LULC in Isfahan City, using Landsat thermal remote sensing satellite images. For this purpose, Landsat 8 (OLI) and Landsat 5(TM) satellite images were utilized, and the obtained images were received and preprocessed from 2000 to 2018. Methodology: The study area is Isfahan City located at the longitudes of 51° 50′ E and 51° 78′ E and the latitudes of 32° 50′ N and 32° 80′ N. The data used in this study were Landsat 8-sensor (TIRS -OLI) and Landsat 5-sensor (TM) satellite images. Land use classification was also done by the Ecognition and Envi 5.3 software programs. Then, Hot Spot analysis was done to determine the hot and cold clusters in Isfahan thermal islands. Finally, the ArcGIS 10.5 software was used to plot the corresponding maps. Results and Discussion: Land surface temperature was determined by the split window algorithm for images obtained via TIRS and the single-channel TM sensor of regional weather stations. The statistical analysis of the temperature data was done via Roots of Mean Square Errors (RMSE), and the values found for 2000 and 2018 were 1.64 and 0.93 respectively. The split window method proved to have a better performance than the single-channel method. The other achievement of this study is to identify the relationship of land surface temperature and land use. According to the results, the temperature of the desert areas was 42.76° C and 46.06° C in 2000 and 2018 respectively. It can be claimed that the high temperature of these area is due to the lack of vegetation. The temperature of the lands with water consumption was also 21.6°C and 32.09°C in 2000 and 2018 respectively. This phenomenon can be attributed to the development of urban areas and the recent droughts which have occurred in the Zayandeh Rood River. Consequently, Hot Spot analysis was done to determine the hot and cold clusters in Isfahan thermal islands. The analysis of the spatial correlation with global Moron indexes suggested that the surface temperature of the earth is distributed in a cluster form. Hot Spot analysis, indeed, provided evidence for the spatial concentration and clustering of the thermal islands in Isfahan over the time. Conclusion: The results of this study showed that the object-oriented classification method provides better results and has a better kappa coefficient and higher overall accuracy. There is also a close relationship between land use and surface temperature. The results indicated a negative correlation between LST on one hand and vegetation canopy and moisture, as already known from many other studies. LST was found to be very sensitive to vegetation and humidity. The study also rejected the hypothesis of irrelevance of spatial temperature of Isfahan (H0) and proved that the surface temperature data of Isfahan have a spatial structure and are distributed in clusters.

One of the factors affecting human activities is land use change that can lead to changes in surface albedo and land surface temperature and changes in these parameters affect the natural climate of the area. The present study investigates land use change trends and land surface temperature and surface albedo per land use in the Aharchai watershed using Landsat data for a period of 29 years. for this purpose, Landsat 5 (TM) satellite images in 1987 and Landsat 8 (OLI) satellite images were used for 2015 and after various stages of image processing, land use extraction was performed based on the Support Vector Machine (SVM) method. Classification accuracy according to kappa coefficient was 0.93 and 0.95 and 95.09 and 96.20 respectively. Most land use changes were observed in pastures and later in dry land agriculture. Converting pastures to dry land agriculture has reduced vegetation cover and altered the distribution pattern and trend of increasing land surface temperature and surface albedo levels. This was the case for dry land farming with an increase in temperature of approximately 8 ° C and a surface albedo of 0.03. It is therefore observed that changes in land use at the basin level have resulted in changes in ground surface temperature and surface albedo.

The population of Isfahan has increased ten times over the past six decades. The rapid urbanization of the historical city of Isfahan has had a great environmental impact. At the same time, drying of Zayandeh Rood, increasing the air temperature, decreasing rain has brought the city to a critical environmental situation. The emergence of the urban heat island is only one of the consequences of the environmental changes of the past decades. Urban heat island has consequences for the health of citizens and it affects the consumption of water and energy. In this study, MODIS Aqua/LST data was used for night and day from 1379 to 1395. By using this data, the background climate of the metropolis of Isfahan was identified with the distance-azimuth (DA) charts. Then the representative pixel within the city and the representative pixel of the background climate were identified. Based on the time series of LST over these two pixels SUHI index of Isfahan metropolis was calculated. Studies show that the Isfahan metropolitan area is colder than the suburbs during the day and it is warmer than its surroundings during the night. The magnitude of the SUHI is maximal in January and is weaker in the summer. Regarding the temporal and spatial behavior of the SUHI, it seems that the changes made by the city in humidity, albedo, and composition of the atmosphere have played an important role in the emergence of the SUHI. Zayandeh Rood has played a major role in modulating the land surface temperature in the metropolitan area of Isfahan, and its drying has had environmental consequences.

Land surface temperature (LST) is used as one of the key sources to study land surface processes such as evapotranspiration, development of indexes, air temperature modeling and climate change. Remote sensing data offer the possibility of estimating LST all over the world with high temporal and spatial resolution. Landsat-8, which has two thermal infrared channels, provides an opportunity for the retrieval of LST using the split- window method. The main objective of this research was to analyze the LST of land use/land cover types of the central part of Isfahan Province using the split- window algorithm. The obtained results demonstrated that the "other" class which had been mainly covered with bare lands exhibited the highest LST (50.9°C). Impervious surfaces including residential areas, roads and industries had the LST of 45°C. The lowest temperature was observed in the "water" class, which was followed by vegetation. Vegetation recorded a mean LST of 42.3°C. R2 was 0.63 when regression was carried out on LST and air temperature.

The southern regions of Kerman Province have repeatedly encountered dust storms. Therefore, the objective of this study was to identify dust sources using effective parameters such as vegetation cover, land surface temperature, soil moisture, soil texture, and slope as well as to detect dust storms originating from these regions based on 31 MODIS images in 2016 and SRTM data. After normalizing parameters, the dust source map was prepared by fuzzy logic and assessed with an error matrix and available dust source map. Results showed that 30.5% of the study area was classified as a low source of dust, 39.55% as moderate, and 29.85% as severe-very severe. The overall accuracy of the produced map was about 70% and the producer and user accuracy of the severe-very severe class was more than 87%. The detection of dust storms originated from the identified dust sources also confirmed a crisis situation in the region. Due to the repeatability and continuity of obtained dust source map at pixel scale, it can be used to update available dust source maps and manage dust crisis in the region, properly. [ABSTRACT FROM AUTHOR]

Land Surface Temperature (LST) is an important parameter in weather and climate systems. Satellite remote sensing is a unique way to estimate this important parameter. However, satellite products have either low spatial resolution or low temporal resolution that limits their potential use in various studies. In recent years, the use of Spatio-temporal fusion techniques to produce high resolution simultaneous spatial and temporal images has been extensively investigated. In this study, a Flexible Spatio-temporal Data Fusion (FSDAF) was used to produce Landsat-like LST images with Landsat spatial resolution and MODIS temporal resolution. The quantitative and qualitative validation of the images was performed by comparing them with the Actual Landsat LST images. The results showed that the FSDAF algorithm has high accuracy in estimating daily LST data both qualitatively and quantitatively. The RMSE and MAE parameters of the images produced compared to the actual Landsat images were 1.18 to 1.71 and 0.88 to 1.29°C, respectively. The correlation coefficient above 0.87 and bias between -0.6 to 1.45°C also confirms the high accuracy of the algorithm in estimating Landsat-like land surface temperature on a daily time scale. [ABSTRACT FROM AUTHOR]

Anthropogenic environmental changes can have serious consequences. Environmental changes affect the frequency distribution of the land surface temperature (LST) due to changes in the thermal properties of the land surface. Temperature changes, in turn, trigger a series of changes in various aspects of the environment. Such changes in Iran that suffer from a hot and dry climate may be matter of life and death. Analysis of 15 years of MODIS/Aqua LST data (2003-2017) shows that man-made land-use changes affect the frequency distribution of LST in some parts of Iran. Construction of dams has been altered the hydrologic cycle in many watersheds of the country. Overconsumption of groundwater resources, urbanization, and deforestation have affected the tempo-spatial behavior of LST in Iran during the past half-century. As a result, there is a huge imbalance between natural resources and expectations of society. Ecological footprints are beyond the capability of natural resources of the country. So there is an urgent need for abrupt changes in lifestyle and reducing the ecological footprints. Natural climatic changes along with these anthropogenic environmental changes would pose a serious threat to the survival of many metropolitans of Iran with several thousand years of history. Many civilization centers of the country survived severe droughts, but this time the changes are too fast.

Dam construction in a watershed causes changes in basin’s entire land cover and landuse. These changes affect basin’s local climate and governed natural process directly or indirectly. In this research, the trend of vegetation land cover and land surface temperature changes for the period of before and after Gavoshan dam construction in Gavehrood basin was studied using temporal Landsat images. To obtain vegetation cover changes, NDVI, EVI and SAVI indices were used. They all show an increase in vegetation cover in the south of dam. To obtain LST and LSE, Planck equation and NDVI thresh holding method used respectively. Analysis of spatial variations of temperature classes from land surface emissivity show that Downward the dam of very warm temperature category are eliminated and cool and very cool temperature categories replaced instead. Analysis of NDVI index show it has been changed averages from 0.13 to 0.165. So, changes in other vegetation indices have been incremental. With production of normalized classes of Temperature was determined that area of average class increased about 67 Km2 (7.3%) and area of very warm class about 11 Km2 (17.5%) decreased. Analysis the dispersion diagram of temperature normalized and vegetation (Fr/T) shows in the course of study decreased the dry edge length and increased the wet edge length. This means that the existence of water surface in this Basin in addition to changing vegetation coverage and soil moisture causes land surface temperature change which in turn evolves the microclimate of the region in the long term.

Land surface temperature (LST) is used as one of the key sources to study land surface processes such as evapotranspiration, development of indexes, air temperature modeling and climate change. Remote sensing data offer the possibility of estimating LST all over the world with high temporal and spatial resolution. Landsat-8, which has two thermal infrared channels, provides an opportunity for the retrieval of LST using the split- window method. The main objective of this research was to analyze the LST of land use/land cover types of the central part of Isfahan Province using the split- window algorithm. The obtained results demonstrated that the "other" class which had been mainly covered with bare lands exhibited the highest LST (50.9°C). Impervious surfaces including residential areas, roads and industries had the LST of 45°C. The lowest temperature was observed in the "water" class, which was followed by vegetation. Vegetation recorded a mean LST of 42.3°C. R2 was 0.63 when regression was carried out on LST and air temperature. [ABSTRACT FROM AUTHOR]

The aim of this study is to explore the role of land surface temperature on distribution of snow-covered days in Iran. Firstly, the land surface temperature data were obtained from NASA for 2003 to 2014 in the spatial resolution of 1×1 km. MODIS Terra and MODIS Aqua data were also downloaded from 2003 to 2014 in the spatial resolution of 500 * 500. After preparation of the data in MATLAB software, Digital Elevation Model of the country was applied to calculate the annual LST for each of the elevations grouped by 1 meter. The findings of this study revealed that in the extents of the country which annul LST is below 30 º C the environmental condition is suitable for the accumulation of snow cover. And it was also found that the LST is 30 º C for the elevations above 1700 m. findings also revealed that there is no snow-covered day for the extents that LST is 37 º C and the most number of snow-covered days can be seen in the regions that LST is 0 º C.

Soil moisture is one of the most important factors that can affect productivity in ecosystems in arid and semiarid regions. The aim of this study was to investigate soil moisture and vegetation changes in the Isfahan province at the seasonal scale. For this purpose, MODIS Land Surface Temperature (LST) and NDVI data were used to calculate the TVDI index, and the rate of soil moisture content was also measured at several soil depths including 5, 10, 20, 30 cm. in the growing season. Seasonal changes of LST and NDVI indices were also studied in different climate regions ranging from humid to hyperarid. The results showed that the changes in NDVI and LST in this region were different, depending on the climate type and soil conditions; the LST and its changes mostly depended on the amount of vegetation cover NDVI changes based on the plant phenology in humid regions, which was were greater than that in arid and semi-arid climates. Soil moisture monitoring indicated that the relationships between TDVI and different soil depths varied based on the seasonal conditions. In the early growing season, the soil moisture at the depth of 0-5 cm had a higher correlation with TVDI, but in the middle of growing season, the deeper soil moisture (10-30 cm) showed the highest correlation. Therefore, the findings of this research indicated the importance of the growing season, soil conditions and vegetation percentage and types in the soil moisture studies by using satellite data. [ABSTRACT FROM AUTHOR]

Monitoring the changes in land surface temperature (LST) caused by seasonal and non-seasonal fluctuations is essential due to its profound impact on the human and natural environment. The use of remote sensing satellite imagery has been extensively considered for the continuous monitoring of LST with its low cost and high speed. In this research, first, three conventional temperature extraction approaches based on Plank’s law were used to extract LST of surrounded areas of Lake Urmia. Then, using a linear regression and observations of the temperatutre in meteorological stations, LST maps extracted from conventional methods were modified. The results showed that conventional temperature extraction approaches for TM and TIRS sensors of Landsat Satellite had an accuracy of about 4 °C and 8 °C, respectively. After modifying LST maps using the linear regression, this accuracy reduced to 1 °C and 0.5 °C for TM and TIRS sensors, respectively. This indicated the proper performance of the regression approach presented in this study for temperature modification. [ABSTRACT FROM AUTHOR]

In this research, the relationship between land use and land surface temperature and spatial correlation in Ardebil city using Moran index has been investigated. For this purpose, Landsat 8 satellite imagery (OLI) was used from 2015 to 2018. Initially, the relevant images were taken and the necessary preprocessing was applied. Then the classification was made using the object-oriented method and the nearest neighboring algorithm and the surface temperature was extracted by the algorithm (SW). The results showed that the temperature in the urban areas was 43 ° C and 45 ° C in 2015 and 2018, respectively, due to the absorption of heat and urban pollution. The use of water was 35 ° C in 2015, and 37 ° C in 2018, that eliminates more heat from the water and has a lower temperature. The results also showed a strong relationship between land use and temperature. Finally, Hot Spot Analysis was used for hot and cold clusters of Ardebil thermal islands. The analysis of spatial correlation with global Moron indexes showed that the surface temperature of Ardabil has a spatial structure or, in other words, the surface temperature of the earth is distributed in cluster form. Hot Spot Analysis has been a clear confirmation of the concentration and clustering of the thermal islands of Ardabil in space with increasing period of time. [ABSTRACT FROM AUTHOR]

Human activities control the microclimate of the city and one of the most important effects of it is the urban heat island phenomenon. The heat island affects the quality of urban life, including energy consumption, air and water quality, as well as human health. Ahwaz, on the other hand, is one of the hottest cities in Iran. Therefore, the study of the heat island of Ahwaz and the knowledge of urban microclimate is worthy of consideration.e. For this purpose, in this study, the heat island of Ahwaz metropolitan area was investigated in terms of temporal and spatial variations by using the data of temporal temperature data of the MODIS-Terra and MODIS-Aqua observations from 2002 to 2017. The intensity of the heat island was measured based on the background climate. To this end, after the city boundary was determined by the latest Google Earth imagery. Then, a range, of the size of the city, was determined around the city and was called the non-urban. The temperature that was most common among all the non-urban cells was considered as the temperature of the non-urban. The temperature difference of all the non-urban cells of the city was obtained from the non-urban temperature. The average temperature difference of all cells from the temperature of non-urban was obtained and called the average intensity of the heat/cold island. The results showed that Ahwaz, during the day has a cold island (-2 degrees Centigrade), and at night was a heat island (2.2 degrees Centigrade). Due to the fact that the Karun river passes through the city, heat island is divided into two parts. The eastern and western parts of the city have more procedural temperatures than the city average. Also, the core of the nightly heat island and the weak parts of the daily cold island are in line with the industrial zone of Ahwaz. [ABSTRACT FROM AUTHOR]

Land surface temperature is one of the important parameters in environmental science and urban planning. Human activities, such as land use degradation and urban development, have the effect of increasing the land surface temperature and thus the appearance of thermal islands, and this phenomenon has adverse effects on human health over time. The aim of this study was to investigate the effects of land use change on spatiotemporal patterns of land surface temperature and thermal islands in Saqqez city. In this study, images from the years 1989, 1998, 2008, and 2018 were first categorized using the Maximum Likelihood Algorithm in ENVI software. Then, using a Split window algorithm, land surface temperature was extracted in the GIS software. Also NDVI, UHII, and UHIII indices were used to study the spatiotemporal land surface temperature and thermal islands. Also, land surface temperature of the low, medium and high vegetation classes was extracted during the period. The results of the classification accuracy with Kappa coefficient above 80% show the validity of the results. Also, the results of the evaluation of land use changes indicate the increasing trend of water, residential and bare lands areas and the decreasing trend of vegetation. In other words, 3.68, 38.43 and 514/02 km, respectively, are the area of water, residential and bare lands areas has been added, and a total of 550/06 km2 has been cut from the vegetation area to the 29-year period. Also, the results of maximum temperature of each land use indicate an increasing trend in temperature in residential and bare lands areas and vegetation during the studied period. According to the results of UHII and UHIII indices, the class with low vegetation has the highest temperature compared to the middle and high vegetation classes and also based on the results of these indicators, the thermal islands occurred in the northeastern parts of the city in 2008 and 2018. [ABSTRACT FROM AUTHOR]

MODIS LST high spatial and temporal resolution data can provide higher quality information about temperature variation in different regions of Iran. LST cluster analysis calculated to detect LST zones and LST spatiotemporal variation in every zone. Time consistent LST has been product from MODIS Terra and Aqua LSTs in any pixels of time series. Monthly long term mean temperature calculated from Time consistent LST and makes an array in 1765*2688*12 dimensions that is long term mean LST of Iran. Monthly data clustered using ward method. Tow temperature zone (warm & cold) separated in first step and each of them clustered to tow new sub clusters. The resulted zones include hot, warm, temperate, and cold. Four temperature zones is consistent with topographic and environmental characteristics of Iran. Temperate and cold zones located in higher areas in Zagros and Alborz mountains, while low altitudes in southern latitudes and deserts are the warm and hot zones. LST maximum values in warm and temperate zones are near to the values in hot zone. With regard to area of warm zone that cover the third of Iran area, it can be concluded that temperature increase in the large parts of the country has the ability to become hot areas

Estimation the amount of radiation reaching the Earth's surface (Rs) is an important factor in the energy balance models simulation of plant growth and evapotranspiration estimation. Most Estimation models to radiation reaching the Earth's surface use satellite data and they are based on land surface temperatures. In this study, the Accuracy of solar radiation estimation is investigated Using four different models of neural networks (with the names of ANN1,ANN2, ANN3, ANN4) with the inputs Including products land surface temperature MODIS sensor (models 1 and 2 , and models 3 and 4 are based on MOD11A1 MYD11A1 products, respectively), extraterrestrial radiation (Ra) and relative sunshine (n / N). The results show that four neural network models are able to estimate the amount of radiation reaching the Earth's surface with good correlation (R2>. 85). However, models based on MOD11A1 products have a higher accuracy than models based on MYD11A1 products. Neural network model of ANN1 (based on MOD11A1 products, relative sunshine and extraterrestrial radiation (Ra)) with the coefficient of determination (R2) equal to .9332 and the root mean square error (RMSE) equal to 1.4448 MJ per square meter per day is more accurate on the estimation of solar radiation than other models. The results also showed that the Neural network model ANN2, comparing with Hargreaves and Samani models based on air temperature and extraterrestrial radiation, is More accurate in estimating of solar radiation.

Today, urbanization and environmental impacts have been revealed to everyone. Among the harmful effects of urbanization, thermal islands have been considered by politicians and researchers in the field of environmental science more than any other factor. The aim of this research is to identify the critical environmental zones of Isfahan urban thermal islands. In order to achieve this goal, the first 9 images were downloaded from Landsat 8 satellite for the warm period of the year 2013 through 2015. Then, the required pre-processing schemes, land surface temperature (LST) and normalized difference vegetation index (NDVI) were calculated. Then, the Environmental Criticality Index (ECI) was used to identify sensitive areas. The results showed that there is a sharp thermal slope between the city center and the suburbs due to the existence of a cool thermal island in the city center. The largest urban thermal islands were identified in the 6th district. Focal thermal islands and urban thermal islands strip are two types of thermal islands after the peripheries accounted for most regions in this study. The highest environmental sensitivity was in the southern part of the city (6th district of the city) and the lowest environmental sensitivity was in the center of the city (urban areas 1 to 3) were identified. In order to solve this crisis, the upcoming proposal to develop green roof vegetation organized and tailored to the indigenous climate can be considered as the best practice for thermal island mitigation. [ABSTRACT FROM AUTHOR]