Background and Objectives: Soil moisture is one of the key variables which by controlling evapotranspiration processes influences the water cycle and heat exchange between the earth and the atmosphere. The amount of soil moisture is also important for hydrological, biological and biochemical cycles. With the help of soil moisture information in regular intervals, the degree of drought development can be determined in regions with dry climates. Furthermore, continuous monitoring of soil moisture in agricultural areas can help to plan irrigation of crops effectively. Soil moisture is also used to identify areas susceptible to fire in forest areas. Therefore, monitoring of soil moisture is important in any regions and different time periods. Due to factors such as lack of uniformity in physical properties of soil, topography, Land cover, evapotranspiration and rainfall, soil moisture is known as a variable factor in spatial and temporal intervals. Therefore, the use of conventional and traditional methods for soil moisture determination (such as gravimetric and neutron probe) is not appropriate to understand the spatial and temporal variation of this parameter in large scales. To resolve this problem in past two decades, remote sensing technology (especially in visible/infrared spectrum) widely used to estimate of soil moisture indirectly. The objective of this study was to estimate Surface soil moisture using Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature ((LST)). Materials and Methods: For this purpose, Landsat 8 satellite imagery was downloaded at the same time as ground sampling. The samples were transferred to the laboratory and soil moisture was measured by weighted method. Then, using the expert software such as ArcGIS, the indices were estimated and the values of these indicators were transferred to SPSS software for statistical regression. In this study, a PTF were obtained to predict soil moisture condition using (LST) and NDVI and NDMI derived from Landsat 8 data. Multiple linear regression method was used to derive the PTF. After derivation of the pedotransfer function, the accuracy of the derived PTF was evaluated. This research was carried out in the Dehzad area of Izeh city of Khuzestan province. Results: Comparison between measured and predicted soil moisture values indicated that the PTF had good prediction (R2=0. 78), Coefficient of Residual Mass (CRM), Mean Absolute Error (MAE), Modified Coefficient Efficiency (E), Modified Index of agreement (d) also showed that the model had good performance (CRM=0. 001, MAE=0. 0013, E=0. 9998 and d=0. 9999). Furthermore, a soil moisture map was obtained for the study area. The result indicated that Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature ((LST)) can be used to predict soil Surface moisture content successfully. Conclusion: The result of this research has been presented by a PTF and in the form of soil moisture map. The soil moisture map simulated by this model can predict 78% of soil moisture variation in the region.

Forecasting changes in the environment has now become one of the important branches of environmental studies. One of these predictions is to investigate the possible effects of Temperature changes on plant and animal species. In this research, the aim is used the Land Surface Temperature ((LST)) of the MODIS satellite, separately for spring, summer, autumn and winter seasons, to investigate the changes in the average (LST) in the protected areas and the habitat of focal species. Focal species of the province include Panthra pardus, Acinonyx jubatus, Capra aegagrus, Ovis orientalis, Gazella subgutturosa and Gazella bennettii. In a field study, the presence points were collected between 1399 and 1402 from all over the province and protected areas, and the seasonal average of (LST) from 2003 to 2033 was prepared using MODIS satellite products (MYD11A1) in the Google Earth Engine system. Species habitat was modeled by using SDMs and combining regression models and machine learning. Also, the TSS threshold was used to convert the habitat desirability probability map into a binary map. Man-Kendall (MK) trend analysis test was used to analyze the trend of increasing and decreasing Temperature changes at a significance level of 95%. The findings show that the decreasing changes in the earth's Surface Temperature have occurred in a scattered and non-continuous manner on the Surface, but the increasing Temperature changes are associated with more spatial continuity. The highest decrease in Temperature occurred in the spring season with 15,212.69 hectares in the Iranian deer habitat, while there was no decrease in Temperature in the winter season. Protected areas have experienced the least increasing and decreasing (LST) trends in spring.

Urbanization which has been considered the main contributor to climatic problems in cities, not only changed the physical environment in cities, but also has affect the characteristics of local climate zone. This study aims to investigate the relationship between Surface properties of Local Climate Zones (LCZ) extracted from satellite resolution images and Land Surface Temperatures ((LST)) in a semi-arid region. By employing the LCZ approach, the study seeks to understand how LCZs influence (LST)s in this specific environmental context. This study consisted of four main steps: Image preprocessing, (LST) retrieval, LCZ map preparation and spatial analysis. In this way, the LCZ scheme was used to classify the study area based on two sets of built-up types and Land-cover types. Google Earth was used to specify training areas under study and to define different LCZ types. The Split Window Algorithm (SWA) was used to retrieve (LST) from Landsat-8 TIRS. The results showed that the built type and the Land-cover type behave like two independent sets of patterns in the study area. The build type set fluctuated in a higher (LST) range than the cover Land set (forest, farmLands and bushes). By shedding light on the relationship between the Surface properties of LCZs and (LST)s, the study provides valuable insights into the factors influencing thermal dynamics in semi-arid environments. Armed with this knowledge, urban planners can develop more informed strategies and interventions aimed at mitigating heat-related challenges and improving overall urban livability.

One of the two-way consequences of global warming and climate change is its effects on water and air pollution, which can affect rural livelihoods or the destruction of rural areas. The current research examines the effect of human presence and his heat-generating devices and activities on the Land Surface Temperature ((LST)) of the summer residences of Taft city, Yazd province. For this purpose, the images of two comparative periods of April (April 10 with April 2) and summer (Friday with Thursday and Saturday) of Landsat 8 and 9 satellites were used. Then the Surface Temperature map and hot-spot analysis (G-i-star) of the area were prepared and the changes were evaluated. The results showed that in April period, 64% and in summer period, 60.1% of rural areas experienced an increase in (LST). Also, 1.43% to 43.5% of the rural area of the region had experienced an increase in Temperature in April and 31.2% to 31.8% in summer. An increase in Temperature variance was also observed in these areas, which shows an increase in Temperature variation in these areas. The number of hot spots in these areas also increased by 111.4% in April and 48% and 21.1% in summer. The results also showed that 65.1% of rural vegetation in April and 49.8% in summer faced an increase in (LST), of which there was a 19% increase in April and 49.9 and 8.6% in summer in Temperature variance and 3. 118 percent of April and 9.5 and 0.2 percent of summer in the number of hot spots, the share of areas with vegetation was. According to the results, all the villages had experienced an increase in (LST), and the fluctuations of this increase were greater in the villages with less vegetation and less area. The current research can be considered as a warning for the creation of rural thermal isLands (like cities) on holidays with many tourists and serious damage to vegetation and rural climate.

Introduction Land Surface Temperature ((LST)) is one of the key parameters in environmental studies on local to global scales. Considering the limitations of local meteorological stations, remote sensing has opened a new horizon in collection of suchinformation. Recently, successful launch of Landsat 8 with two thermal bands has provided a good opportunity for retrieving Land Surface Temperature usingthermal remote sensing technology. Many studies had been performedwith the aim of retrieving Land Surface Temperature, but available evidencesshow a significant calibration uncertainty inThermal Infrared Sensor (TIRS) of Landsat 8 band 11 and thus development of new studies based on onethermal band seems to be necessary. However, calibration documents issued by the United States Geological Survey (USGS) indicated uncertainty ofdata received from Band 11 Thermal Infrared Sensor (TIRS) of Landsat 8 and suggested using Band 10 data as a single spectral band for (LST) estimation. Materials & Methods In this study, mono-window algorithm with its three essential parameters (ground emissivity, atmospheric transmittance and effective mean atmospheric Temperature)has been developedunderan automated algorithmin MATLABand was used for Landsat 8 data. Thermal band 10 was used to estimate brightness Temperature. Bands 4 and 5 were also used to calculate the NDVI. Retrieval of (LST) from Landsat 8 TIRS data is performed based on the premise that brightness Temperature (Ti)can be computed for any pixel of Band 10 using the mono-window algorithm. Since the observed thermal radiance for Band 10 of Landsat 8 TIRS is stored and transferredasa digital number (DNs) with 16 digits between 0 and 65, 535, it is possible toconvertthe DN value into thermal radiance and then convert radiance into brightness Temperature. Ground emissivity is calculatedusing Land cover patterns received from other bands of Landsat 8, and the other two parameters are estimated based on the local meteorologicaldata. Usually, obtaining an accurate estimate of ground emissivity is very difficult, and the atmospheric water vapor content is considered to be a sensitive parameter in traditional (LST) retrieval methods. Results & Discussion The algorithm has been successfully applied to Tabriz city in north west of Iran with the aim of analyzing spatial distribution of (LST). After running the algorithm on the satellite images of the study area on July 18, 2016, a lower Land Surface Temperature was observed in green spaces with 1. 2° C accuracy as compared to urban areas and wasteLands. The lowest Temperature in the study area was 20° C and the highest Temperature was 53° C and mean Temperature was 38. 78° C. Results indicate that the algorithm candiscover natural urban heat isLands accurately. Moreover, spatial distribution of (LST) in the region is quite well matched with the Land covers. Successful application of the algorithm proves the efficiency of improved mono-window algorithm as a method used for retrieving (LST) from Landsat 8 data. Conclusion Compared to common methods, the proposed algorithm estimates Land Surface Temperature with minimum requirement for user intervention, least possible time and an acceptable accuracy. Itgives researches an opportunity to easily compute (LST) and apply it in other studies, and thus it is a significant tool.

Land Surface Temperature ((LST)) is an important criterion for regional planning and management. (LST) can be used in many practical programs of environment, agriculture, meteorology and relevant surveys. Due to the limitations of meteorological stations, remote sensing can be used as base of meteorological data. One the practical approach of remote sensing is this important that it can be used for monitoring of (LST). For this goal, the split window algorithm is well known technique among the remote sensing based methods for evaluating of (LST). According to relevant literature this method has offered minimum error for evaluating the (LST). In this survey (LST) of Mahabad’s catchment was derived, by using multi-spectral and thermal bands of Landsat 8. For this goal, after the atmospheric/ radio-metrical correction, image analysis process was performed.Normalized difference vegetation index, fraction of vegetation cover, satellite brightness Temperature, Land Surface emissivity and column water vapor (CWV), are of the vital criteria in Land Surface Temperature in split Window algorithm. For the task of (LST) modeling, the Temperature was calculated based on relevant mathematical equations. Eventually, we managed to evaluate the (LST) being derived with an error of 1.4 Centigrade. And regions with high vegetation index and covered with water low Temperature and regions with low vegetation index, and bare shows a high Temperature which are all effective in Temperature changes in researching area. Results of this research indicated, the method of split window algorithm is presenting exact, and reliable results for the scientific environmental researches and geoscience.

The limitations of satellite sensors make it impossible to access thermal bands with high spatial and temporal resolution simultaneously. Therefore, downscaling methods are essential because they provide simultaneous access to thermal data with high spatio-temporal resolution. (LST) parameter is critical in agriculture, it is one of the most important in estimating the amount of evapotranspiration and significantly impacts crop growth. (LST) images of the MODIS with spatial resolution of 1000 meters are available daily. Still, the low spatial resolution in these images is a limitation that makes it impossible to use for agricultural management. On the other hand, high humidity changes in irrigated regions cause errors in (LST) downscaling process. this research was carried out with the aim of downscaling the (LST) of the MODIS sensor from 1000 meters to OLI resolution from Landsat 8 satellite (30 meters) in irrigated regions. In the first stage, the DisTRAD model was implemented for the downscaling of the (LST) of MODIS in Amirkabir and Mirzakoochak Khan Farms. The results show the poor performance of the DisTRAD model in the downscaling of (LST) from 1000 meters to 30 meters. Next, to check the results of (LST) downscaling of the MODIS by TOTRAM and OPTRAM soil moisture estimation models, the root mean square error (RMSE) statistic was used. The results indicate that the average value of RMSE in the downscaled images by the OPTRAM-TOTRAM model shows a decrease of about 2.53°C compared to the DisTRAD model. Also, the average value of RMSE in the first six months of the year when irrigation has been done, compared to the DisTRAD model, shows a decrease of about 4.11 °C. As a result, the use of the OPTRAM-TOTRAM model offers much better performance than the DisTRAD model in downscaling the (LST) of the MODIS in irrigated regions.

Land Surface Temperature ((LST)) is a crucial parameter in investigating environmental، ecological processes and climate change at various scales، and is also valuable in the studies of evapotranspiration، soil moisture conditions، Surface energy balance، urban heat isLands، fire detection and earthquake thermal precursors. There is a shortage of daily high spatial Land Surface Temperature data for using in high spatial and temporal resolution environmental process monitoring. Due to the technical and budget limitations، remote sensing instruments trade spatial resolution and swath width. As a result one sensor doesn’ t provide both high spatial resolution and high temporal resolution. The 16-day revisit cycle of ASTER leads to a disadvantage in studying the global biophysical processes، which evolve rapidly during the growing season. In cloudy areas of the Earth، the problem is compounded، and researchers are fortunate to get two to three clear images per year. However، the ability to monitor seasonal Landscape changes at fine resolution is urgently needed for global change science. At the same time، the coarse resolution of sensors such as the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) limits the sensors’ ability to quantify biophysical processes in heterogeneous Landscapes. The development of data fusion techniques has helped to improve the temporal resolution of fine spatial resolution data by blending observations from sensors with differing spatial and temporal characteristics. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is the widely-used data fusion algorithm for Landsat and MODIS imagery to produce Landsat-like Surface reflectance. In order to extend the STARFM application over heterogeneous areas، an enhanced STARFM (ESTARFM) approach was proposed by introducing a conversion coefficient and the spectral unmixing theory. Since ASTER and MODIS sensors are onboard a platform (Terra or Aqua)، therefore، this study has used an enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) based on the existing STARFM algorithm to blend ASTER and MODIS (LST) product. Using this approach، high-frequency temporal information from MODIS and high-resolution spatial information from ASTER can be blended for applications that require high resolution in both time and space. The MODIS daily 1-km (LST) product and the 16-day repeat cycle ASTER 90-m (LST) product are used to produce a synthetic “ daily” (LST) product at ASTER spatial resolution. The (LST) products of ASTER and MODIS sensors were fused for a part of Tehran city and finally، a virtual image was obtained with a spatial resolution equal to that of the ASTER sensor and a temporal resolution equal to that of the MODIS sensor. The results show that the accuracy of ESTARFM algorithm is better than the accuracy of the STARFM algorithm in the studied area— with an average difference of 1. 77 Kelvin from the real observation data. The STARFM algorithm couldn’ t preserve the spatial details in the predicted virtual image as well as two other algorithms. The results showed that the algorithm can produce high-resolution temporal synthetic ASTER data that were similar to the actual observations with a high correlation coefficient (r) of 0. 87 between synthetic imageries and the actual observations.