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

Aerosol optical depth in 550 nm and angstrom exponent measurements with MODIS have been studied with 1-degree resolution for the period 2006-2017 in the middle east. Moreover, tropospheric aerosol optical depth and depolarization ratios measured at 532 nm with CALIOP have been studied for same area and same period of time too. These parameters have been classified seasonally. Optical depth results show high values for the region especially in spring and summer seasons. During the cold seasons, optical depth values are much less compared with their values at warm seasons. At spring, dust sources located in northern Iraq and those located in central and northern parts of Arabian Peninsula are much more active. Sources located in southern parts of Arabian Peninsula get more active by summer. Angstrom exponent results show that in arid and semi-arid parts of middle east, aerosol sizes are mainly in coarse mode. In arid parts of Iraq and Arabian Peninsula coarse mode particles are dominant during 4 seasons, but for arid parts inside Iran coarse mode is dominant during warm seasons and a modification in suspended particle sizes can be seen during cold seasons. Depolarization measurements of CALIOP show that almost in all seasons, non-spherical particles are ready in middle east atmosphere which is usual for an area inside the dust belt.

In recent years, the technology of crop production has been greatly expanded using satellite data. Today, Landsat 8 and OLI sensor data, with a spatial resolution of 30 meters, allow the discovery of factors that control phenology on a local scale. In this study, the remote sensing indices - NDVI, EVI, Greenness, and Brightness - obtained from the OLI sensor and the GCC index obtained from digital camera images were used to estimate the phenological stages of the rapeseed plant. The Savitzky-Goli filter was used to remove outlier data and to produce smooth curves of time series of plant indices. The results showed that the curves obtained from the indices of NDVI, EVI, GCC show all four stages of remote sensing phenology – green-up, dormancy, maturity, and senescence - well, but the Greenness index did not show the dormancy stage well. The Brightness index curve shows the inverse behavior to other curves. According to Pearsonchr('39')s correlation test, GCC index data are correlated with NDVI and Brightness index data .we used the ratio threshold, rate of change and first derivative methods, to estimate "start of season" and "end of season" and the results showed that the first derivative and ratio threshold methods with an average difference of 18 and 19 days in the "start of the season"  and the rate of change method, with an average difference of 8 days, has the best performance in estimating the “end of the season”. Also, the Brightness index with an average difference of 16 days and the EVI index with an average difference of 7 days have the best performance in estimating "start of season" and "end of season", respectively.

Corona and Hexagon satellite photographs with a spatial resolution of 1.8 to 9 meters are a good source for monitoring and evaluating changes in surface phenomena. The purpose of this study is to monitor the changes in Zaribar Lake in the period 1969-2019 using the data of Corona, Hexagon and Aster satellites and their ability to monitor and extract the coastline, lake boundary and water surface. In this study, to geometric correction the corona and hexagon data from Google Earth images, linear extraction algorithms, binary mask and mean shift segmentation were used to extract the coastline and lake boundary, detect lake changes and extract and monitor the water area of ​​Zaribar Lake. The results showed that in the first step: geometric correction of corona and hexagon images was obtained using Google Earth images with RMSE of 0.3 and 0.4 pixels. In the second stage, the linear extraction algorithm for extracting the lake boundary and coastline using corona and hexagon photographs has high accuracy and has a high correlation with the topographic map of 1.50000. In the third step, the unsupervised classification of binary mask method, in order to detect lake changes using corona and hexagon photographs, acceptablely identified the altered and unchanged pixels, so that 11 hectares of lake surface had the most changes. Finally, in the fourth step, it was found that the mean shift segmentation algorithm and threshold worked better by applying the corona, hexagon, and Aster events to extract the water surface, and in the meantime, the corona image performed better due to its higher resolution. The above results showed that Zaribar Lake decreased by 6.5% from 1969 to 2019 and the findings have a high correlation with the product of the ester aquifer. In general, the findings of this study show the potential of using digital image processing methods for corona and hexagon data to monitor and detect changes in lakes. 

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

Wetland cover classification is of special importance in order to identify the type of plant species inside the wetland and also to distinguish it from the wetland margin vegetation and to study their ecosystem changes.  Due to the spectral similarity between different plant species of wetlands and plants along the wetlands and agricultural lands, this is faced with problems using multispectral data and hyperspectral data can be very useful in this regard. in this study power of hyperspectral and multispectral sensors in identifying the characteristics of the wetland and the ability of ETM + (2011), Hyperion (2011) and ALI (2011) sensors to study the characteristics of Shadegan wetland during 1390 and different spectral indices with a suitable combination of The satellite imagery bands of these sensors were compared as input to a variety of classification methods including maximum likelihood, minimum distance, neural network and support vector machine. The results showed that the support vector machine and neural network methods with closer classification accuracy of 85% in all three images show closer results to reality. The classification accuracy for all three images was at its highest for the backup vector machine method, with a total accuracy of 95.73 for the Hyperion image, 88.03 for the ALI and 89.34 for the ETM +. Therefore, the characteristics considered for the wetland, in the three images obtained from the SVM algorithm showed that showing the differentiation of wetland vegetation use from irrigated agricultural land use is more ambiguous than other wetland features. Studies have shown that this part is less recognizable in ALI and ETM + images than Hyperion images, or in some areas these parts are not separable from aquaculture land at all, while Hyperion due to having 220 bands and having a higher level of Spectral details have the ability to distinguish between the two classes.

Relative humidity is considered to be one of the most important climatic parameters and atmospheric phenomena. The purpose of the present study is to evaluate the regional algorithms for estimating relative humidity using remote sensing data in Hormozgan province. To this end, MOD05 and MOD07 products were employed to estimate total perceptible water, air temperature, and sea-level pressure Additionally, MOD35 was used for cloud verification, , resulting in the identification of 2190 cloudless images with 95% confidence level for analysis. radiosound data of Bandar Abbas ststion and synoptic stations Covering entire Hormozgan Province. were used to evaluate the results. The findings demonstrated high accuracy of the algorithms and experimental model, with acceptable R² and RMSE values between Modis product and ground data. These results align well with ground station measurements. The province's climate was determined to be semi-desert with a long warm season and a short cool period. Further analysis revealed a strong correlation between sea-level pressure and total perceptible water (TPW) with the region's topography. Maximum TPW and sea-level pressure values were recorded in coastal lowlands, while minimum values occurred in the highlands. Based on zoning maps, Hormozgan province can be divided into four regions based on relative humidity: from very dry conditions with less than 20% relative humidity in the highlands to humid areas with over 65% relative humidity along the coast.

In vast areas, accessing satellite images with appropriate spatial resolution, such as Landsat images, is often challenging.  dditionally, the temporal resolution of the Landsat satellite does not allow for the examination of short-term changes in phenomena such as vegetation. The aim of this research is to utilize temporal and spatial fusion techniques of Landsat-8 and MODIS satellite images to prepare a Normalized Difference Vegetation Index (NDVI) map.  For this purpose, six image fusion algorithms—NNDiffuse (Nearest Neighbor Diffusion), PC (Principal Component), Brovey, CN (Color Normalized), Gram-Schmidt, and SFIM—were applied in an experimental area in Khuzestan province. After evaluating the results of these algorithms and selecting the most appropriate algorithm based on statistical indicators (spectral criteria such as the correlation coefficient and spatial criteria such as the Laplacian filter), the spectral and spatial information from the red and near-infrared bands of eight mosaic Landsat-8 images (30 m resolution) were combined with the red and near-infrared bands of one MODIS image (250 m resolution). To investigate vegetation cover, the NDVI was calculated using the fused satellite image for Khuzestan province. The results showed that the NNDiffuse fusion algorithm demonstrated very high accuracy among the tested algorithms in terms of spatial evaluation and spectral quality criteria. Consequently, this algorithm was selected to combine the red and near-infrared bands of Landsat-8 and MODIS images. Compared to the original Landsat-8 image, the NDVI map prepared using this algorithm had the lowest statistical errors, with an RMSE (Root Mean Square Error) of 0.1234 and an MAE (Mean Absolute Error) of 0.081.

The persistent drought conditions and the increasing reliance on groundwater resources over the past decades have significantly expanded the areas affected by land subsidence across various regions of the country, leading to substantial damage. To mitigate the impacts of subsidence, a comprehensive and precise understanding of this phenomenon is essential. In recent decades, the Synthetic Aperture Radar (SAR) interferometric technique has emerged as a widely used method for measuring subsidence. This study utilizes field data, including piezometric wells, groundwater level fluctuations during minimum and maximum periods, and exploitation wells, to calculate aquifer discharge rates using Inverse Distance Weighting (IDW) interpolation. The aim is to analyze the time series of subsidence in the Esfarayen plain. Additionally, radar data from Sentinel-1 images were employed to estimate the subsidence rate during the first eight months of 2023. The findings reveal that subsidence in the study area ranged from 1 to 12 mm over the eight-month period, with 75.2% of the basin area classified as medium to highly critical. This indicates that the Esfarayen plain is in a critical state. The highest levels of water extraction and subsidence were observed in the southern regions of Sankhasat, Kharasha, Arg, Gazan, Jafarabad Kharaba, and Mehdiabad of Kal Beko wells, all of which fall within the highly critical zone. These areas require efficient groundwater management strategies to control and mitigate land subsidence. 

The emergence of new cities can be attributed to the significant increase in the population of urban areas. Over the past two decades, numerous new cities have been established in proximity to the country's metropolises, with the new city of Sahand serving as a pertinent example. The primary objective of this research is to elucidate the physical development trajectory of Sahand, utilizing Landsat satellite imagery spanning the statistical period from 1373 to 1401. To this end, satellite images corresponding to four distinct statistical periods (1373, 1383, 1393, and 1401) were acquired from the Landsat 5 and Landsat 8 satellites. By applying a band calculation function to the images captured by the Thematic Mapper (TM) and Operational Land Imager (OLI) sensors, the physical changes in the urban fabric of Sahand during the specified temporal intervals were quantified and analyzed. The findings of this research indicate that the physical growth and development of Sahand commenced in 2013, at which point the urban area encompassed 282 hectares, representing a 28-fold increase since that year. In the subsequent decade, the urban area expanded to 570 hectares, reflecting a 100% growth relative to the previous decade. Ultimately, during the final decade under review, the urban area reached 850 hectares, exhibiting a growth rate of 50%. Notably, District 6 of Sahand, which constitutes approximately 35% of the city's physical fabric, emerged as one of the fastest-growing regions between the years 1393 and 1400. Moreover, a statistically significant correlation was identified between population growth and the physical development of Sahand during the statistical period from 1380 to 1400, with a confidence level of 0.95 (P_value=0.05) and a correlation coefficient (R) of 0.91. Consequently, the regression model fitted to the relationship between population growth and urban fabric expansion, when incorporating the projected population density following the implementation of Mehr housing policies (which anticipates a population of 185,000), suggests that the area of Sahand's physical fabric will increase to 1,181 hectares in the forthcoming decade, indicating a growth rate of 38%.