The ability of plant communities in natural ecosystems to modify temperature has become increasingly important due to the profound impacts of global climate change, particularly in arid regions. However, previous studies have provided limited information on the long-term temperature feedback of these plant communities and the biotic drivers behind these changes. This study aimed to determine the functional traits and types of plant communities as biotic drivers of land surface temperature (LST) at the plant community scale, with a focus on identifying co-functioning communities in the Sirjan region of Kerman Province. To achieve this, we utilized the MODIS-LST 8-day composite product at the plant community scale and measured functional traits of dominant species through field operations. The results revealed that leaf dry matter content (LDMC), maximum height (MH), and leaf width (LW) traits significantly reduce LST. Additionally, cluster analysis indicated that the plant communities in the study area can be classified into five functional groups, which fall into two co-function categories. The S-strategized co-function (e.g., 26 communities), characterized by high LDMC values and a combination of abrupt and trend feedback in LST, was found to be more effective than the R-strategized co-function (e.g., 13 communities), which exhibited only trend feedback. Therefore, it can be argued that extreme temperatures, as a global concern, can be mitigated through careful selection of vegetation based on functional traits and strategies. This approach, particularly through rangeland improvement practices using species such as Astragalus spachianus, Cornulaca monacantha, and Launaea acanthodes, could play a significant role in addressing this challenge.

Soil temperature is one of the main physical parameters of soil. Plant growth and soil generation processes are subject to temperature changes. The study of soil temperature in different depth is of utmost importance in meteorology specially, in micro scale, in climatology, agriculture and industry. Soil temperature change is subject to weather temperature of earth surface. Relationship of soil and weather temperature is one of the most important relationships which can be applied in forecasting cold weather and estimation of damages incurred on agricultural products. Boroujen area is one of agricultural areas of chaharmahal province. This study has been in this area for decreasing the frost bite damages incurred on farmer's products of that area and proper information providing for them. In this study, the minimum weather temperature, minimum earth surface temperature and the temperature of different depths of earth (5-10-20-30-50 and 100 cm of earth) have been analyzed in definite standard hours, 6: 30 in the morning, 12: 30 of noon, and 6: 30 in the afternoon after collecting the data and doing different tests (data qualification and homogeneity). Observations show that the temperature changes range in 24 hours is decreased as the earth depth is increased and that a time delay is occurred in lower depths before the temperature reaches its maximum point. The annual temperature cycle shows a time delay and a temperature changes range decrease by depth increase. The annual average temperature of earth is more than the annual average temperature of air because the energy absorbed in earth is more then the energy absorbed in air and a part of heating energy absorbed in earth is transferred to lower depths. The depth of a frost bitten layer of earth has been determined in different seasons especially in the spring and fall and finally the predication of earth minimum temperature has been offered for the prognosis of the occurrence of these phenomena. The study shows that the reason of sudden temperature decrease in the spring has mainly been radiation temperature decrease. The temperature change of earth in 50 cm. depth has been slow in such a way that the total temperature changes have been about 19.3oC. The most temperature changes range in this area has been in 5 cm.depth and about 29.1oC. This study has shown that the correlation of the minimum weather temperature and minimum earth temperature in all months is linear. The most correlation has been 0.98 in June and the least correlation has been 0.52 in May. The change of earth temperature has been wavy and has been decreased from surface to depth. The maximum temperature has been in 50 cm depth of earth in August with one month delay.

Oceanographic images obtained from environmental satellites by a wide range of sensors allow characterizing natural phenomena through different physical measurements. For instance Sea Surface Temperature (SST) images, altimetry data and ocean color data can be used for characterizing currents and vortex structures in the ocean. The purpose of this thesis is to derive a relatively complete framework for processing large dynamic oceanographic image sequences in order to detect global displacements such as oceanographic streams and to localize particular structures like motion current and vortices and fronts. These characterizations will help in initializing particular processes in a global monitoring system. Using area-based algorithms, two least squares methods have been used to solve the apparent motion which involves Least Squares Matching (LSM) and Hierarchical least squares Lucas and Kanade (HLK). SST images of Caspian Sea taken by MODIS sensor on board Terra satellite have been used in this study. Three daily SST images with 24 hours time interval are used as input data. The LSM technique, as a flexible technique for most data matching problems, offers an optimum spatial solution for the motion estimation. The algorithm allows for simultaneous local (i.e. template) radiometric correction and local geometrical image orientation estimation. Actually, the correspondence between two image templates is modeled both geometrically and radiometrically. In order to implement weighted least squares fit of local first-order optical flow constraints in each spatial neighborhood, the HLK method has been used. This method locates water current using coarse-to-fine strategy to track motion in Gaussian pyramids of SST images. This method allows the detection of large motion in coarse resolution layer and guides to correct result in finer layers. The method used in this study has presented more efficient and robust solution compared to the traditional motion estimation schemes to extract water currents.

Information on a variation of impervious surface is useful for understanding urbanization and its impacts on the hydrological cycle, water management, surface energy balances, urban heat island, and biodiversity. This research attempts to detect impervious surfaces and its changes by satellite imagery in Qaemshahr. The relationship between impervious surfaces and changes in land surface temperature in the city was investigated. For this purpose, after obtaining three images in 1978, 2000, and 2017, and performing the necessary preprocessing, the reflection values of the infrared spectrum and ground surface temperature in the study area were calculated. The reflectance of this spectrum was investigated in various land uses vegetation, asphalt and building areas in two parts of the urban and the suburb. Using the results of ANOVA and Tukey these properties compared to different land uses. By the difference between Permeable surfaces and impervious surfaces, the impervious surface index was calculated. The results of the detection and comparison of the three surveyed images showed that the impervious surfaces in Qaemshahr were significantly increased from 1978 to 2017. In the next step, by calculating the land surface temperature, it was determined that the temperature of the impervious surfaces is higher than the other parts of the study area. An increase in the population of the city followed by an increase in urban construction has led to an increase in impervious surfaces and a reduction in green space and this has caused a rise in city temperatures. The results of this study showed that increasing impervious surfaces has led to an increase of around 4 degrees in the citychr('39')s temperature. Finally, any increase in the impervious surface at the city will lead to unsustainability in the urban environment, if not accompanied by proper planning.

Earth surface temperature is an important indicator in the study of energy equilibrium models at the ground level on a regional and global scale. Due to the limitation of meteorological stations, remote sensing can be an appropriate alternative to the Earth's surface temperature. The main objective of this study is to monitor the surface temperature and its relationship with land use, which is monitored using satellite imagery. For this purpose, the images were first obtained and the necessary pre-processing was applied to each one. Then it was compared to modeling and classification of images.  Firstly, in order to investigate the changes in user-orientation, a user-defined classification map for each object was extracted using the object-oriented method. Then, to investigate the land use change, a map of user-landing changes map was extracted in an 18-year time period (2000-2017). Finally, in order to monitor the surface temperature, the surface temperature map of Ardebil was extracted. The results showed that there is a strong relationship between land use and surface temperature. As a user, urban users have a temperature of about 41oC (2017), which is also due to heat-absorbing urban temperatures.  This is despite the fact that the use of hydrocarbons is due to a lower heat absorption of 34oC (2017). This shows the role of different uses in determining surface temperatures.  Also, the relationship between surface temperature and vegetation cover was investigated in this study. The results showed that areas such as soil and urban areas with a lower coverage than areas such as agriculture and pasture, have a higher temperature. Because the coating is always an obstacle to the entry of heat, it has an inverse relationship with superficial heat.

BACKGROUND AND OBJECTIVES: This study aimed to investigate the long-term relationship between chlorophyll-a, sea surface temperature, and sea surface salinity monthly from January 2015 to December 2021. It was carried out in the Northern Bay of Bengal, which experiences extreme monsoons, in the southwest monsoon and northeast monsoon from June to September and November to February, respectively. Monsoon is the main cause of changes in chlorophyll-a, sea surface temperature and sea surface salinity. METHODS: The seasonal model was used to examine the relationship between these three parameters, which were obtained using the Copernicus Marine Environment Monitoring Service data. The seasonal model was used to observe periodic patterns and predict parameters based on their regularity. Meanwhile, Pearson’, s correlation analysis was conducted to determine the relationship between chlorophyll-a, sea surface temperature and sea surface salinity. FINDINGS: This study found that the three parameters, namely chlorophyll-a, sea surface temperature, and sea surface salinity, follow the monsoon pattern, as shown in the seasonal model. The minimum value of chlorophyll-a occurred in February, March and April, while the maximum value of approximately 2 milligram per cubic meter occured at stations 1, 2, 3, 4, 5 and 7, but at 9 and 10, it increased to 12-14 mg/m3. This indicates that station positions are very sensitive to changes in chlorohophyll-a values. When the southwest monsoon occurred, it reached the maximum. Furthermore, the minimum sea surface temperature values occurred in January and at almost every station in the year. It was shown to be associated with the northeast monsoon, which causes winter. On the sea surface temperature graph, several peaks were observed in positive local extremes yearly at almost all stations. The maximum sea surface temperature occurred in May, June, and July, according to the shape of the graph, which peaked in the middle of the year. The sea surface salinity graph formed a peak and valley which occurred yearly in May or April, as well as September and October, respectively. CONCLUSION: Chlorophyll-a had 1 trough and 1 peak, with the sea surface temperature graph possessing only 1 peak, while the sea surface salinity graph had 1 peak and 1 trough, respectively. These graph patterns implied that chlorophyll-a first achieved a minimum value before reaching the má, ximum. The sea surface temperature graph had a maximum value in the middle of the year, while the minimum occurred at the beginning or end. Moreover, the sea surface salinity graph first reached the maximum value and then declined to the minimum.

Due to the importance of meteorological data and limitations of data gathering from ground stations, remote sensing can play an important role in the preparation of these data. The purpose of this study was to quantitatively evaluate the Land Surface Temperature (LST) obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor images for estimating the maximum and minimum daily air temperature in the Taleghan watershed. For this purpose, the maximum and minimum daily air temperature data of three existing ground stations for the period 2009 to 2015 were obtained. Day and night LST and Normalized Difference Vegetation Index (NDVI) values ​​of MODIS were also prepared. The relationships between each of the effective variables and maximum and minimum daily air temperature in ground stations have been extracted using multiple linear regression method. The results showed that there was a significant correlation between maximum and minimum daily temperature of ground stations with day and night LST and NDVI from MODIS sensor. Therefore, these variables were used in regression relationships. The results of validation showed that the established relationships with all effective variables had the most accuracy. Therefore, the best model for estimating the maximum daily air temperature had , NSE and RMSE values ​​of 0.74, 0.74, and +4.7, respectively and for estimating the minimum daily air temperature had 0.71, 0.72 and +2.9, respectively. Therefore, by converting the surface temperature obtained from MODIS sensor images, the air temperature can be estimated with high accuracy on a daily and monthly scales for various studies.

One of the most important applications of satellite images is the use of it to determine and estimate the surface temperature of the earth. To investigate this, the Landsat 8 series was used to calculate and estimate the air temperature in the city of Isfahan and surrounding area. The surface of the earth's surface temperature (LST) was calculated using the Subrino window algorithm. The results showed that the use of this method on satellite images of Isfahan region, which covers a variety of urban areas, green spaces, and major industrial areas, shows the ability of this model and the proportion of these images for this purpose. The comparison of a temperature measured on the image and the temperature calculated in two of Ozone-metric and Airport stations in Isfahan shows a range of changes of about 4. 5 degrees Celsius. However, the calculated temperature in four days of the year is more than the measured temperature at a depth of 5 cm in the soil at the site of the two Ozone-metric and Isfahan Airport stations and this value is at a maximum of 4. 25 degrees Celsius above the station's temperature. The correlation relations between the LST surface temperature map and the normalized vegetation difference index (NDVI) at 9100 pixels of the area code with a correlation coefficient of 0. 445 are confirmed at a confidence level of 99% which confirmed the validity of the method used to estimate the surface temperature. .