Water is one of most important human needs for life. According to importance of subject, discussion of management and utilization of water resources has become one of the most important global issues. Remote sensing data are often used in water body extraction studies and type of remote sensing data used plays an important role in water body extraction. In this study, ability of Landsat satellite with application of water indices, to extraction of Gamasiab River in Kermanshah and comparing these indices have been investigated. Specific feature of Low width and shallow rivers has increased the complexity of studies of such rivers using available data. Water body extraction from remote sensing images has been over the past two decades. Water indices were first developed using Landsat TM and Landsat ETM. But its better performance in Landsat 8 is well documented by the researchers. In this study, NDWI, MNDWI, AWEI_nsh, AWEI_sh and WRI indices were used. With extracting optimal threshold from histogram of indices and applying this threshold, the study area was classified into two classes of water and non-water. Then overall accuracy and kappa coefficient values were taken from each of the indices. Finally, AWEI index with overall accuracy of 99.09% and a Kappa coefficient of 0.98 was the best response among the indices in the study area. The results this study showed that approach can easily extract water from satellite imagery.

Urban development and air pollution are among the most important issues related to climate. The expansion of urbanization and urban development, population growth, industrial development and excessive use of fossil fuels significantly increased air pollution and it is more than the capacity of the environment. In our country, the emissions of air pollutants in some metropolitan areas have reached a dangerous level, Mashhad is considered to be the most polluted cities of the country in some days of the year. Nitrogen dioxide is one of the indicators of air pollution. In this study, the OMI data and atmospheric parameters such as wind, surface temperature, and horizontal visibility data for the period from 2004 to May 2016 were used to investigate the air pollution in Mashhad. The results show that the maximum (minimum) nitrogen dioxide levels occur in the cold (hot) season. The highest amount of nitrogen dioxide in January is equal to 5.56 × 10¹⁵  molec/cm² and its lowest value in September is 4.18 × 10¹⁵  molec/cm². Standard deviation of nitrogen dioxide also indicates that the greatest changes occur in cold seasons. Also, the results showed that the dominant wind in the city of Mashhad is from the south, and most of the winds are slow. Correlation coefficient of nitrogen dioxide with wind and surface temperature is -0.36 and -0.57, respectively, which shows the higher importance of temperature in nitrogen dioxide changes in Mashhad city. The correlation coefficient of nitrogen dioxide with horizontal visibility is -0.15, which indicates that with increasing nitrogen dioxide contamination, horizontal visibility decreases. Spectral analysis of least squares of the six and twelve-month periods of rotation was observed, they were also statistically significant. After eliminating the significant components of the time series of the average monthly nitrogen dioxide, the trend was calculated. The amount of nitrogen dioxide in each year for Mashhad was 2.41 × 10¹³  molec/cm².

Various satellite remote sensing data, images and products have proven their place in drought, drought and agriculture studies since the production of this type of information resource. Visible, near-infrared and thermal bands are among the most widely used in the production of products such as vegetation and surface temperature. In this study, from MODIS sensor data to investigate and find the coefficients of spatial relationship between vegetation-surface temperature index (NDVI-TS) and NDVI-ΔTS to extract the time of agricultural drought from June to October 2007 to 2010 in the catchment Siminehrood has been extracted from the Temperature-Vegetation Condition Index (VTCI) and the Water Lack Index (WDI), which are able to detect drought stress on a regional scale. The results of this study showed that in both indicators, the drought stress situation was higher in 2007 and 2008. Also based on the NDVI-TS space relationship in all the years 2007 to 2010 the high slope of the triangular space for the hot edge is negative. This means that with increasing NDVI, the LST level decreases while for the cold edge the slope is positive. In addition, the slope obtained from the NDVI-ΔTS space relationship is negative for the dry line, ie the dry line or the minimum transpiration-sweat line (ETR) shows a negative correlation with NDVI. While for the wet line, especially in 2010, the slope is positive and in other years, no significant change is seen. The present study showed that the VTCI threshold for drought stress was severe in 2007 and 2008.

In this paper, aerosol optical depth measured by Caliop, MODIS, MISR, and OMI satellite sensors is compared with Sun-photometer data located in the Institute for Advanced Studies in Basic Sciences (IASBS) from December 2009 to December 2013 over Zanjan city. We computed figures for root mean square error (RMSE), mean absolute error (MAE) and root mean bias (RMB) between space-born and ground-based sensor measurements. The results show that the Caliop and MISR sensors have the highest correlation (0.61 and 0.54), respectively, with Sun-photometer measurements over Zanjan area. MISR, Caliop, and OMI sensors have the closest aerosol optical depth data to the Sun-photometer measurements (the fitted line slope is 0.68, 0.61 and 0.59, respectively) which represents the appropriate model used in the sensors to extract the aerosol optical depth. The variable monthly AOD figures obtained with different sensors indicate underestimation by MODIS and Caliop instruments (0.32 and 0.83 respectively) over Zanjan city relative to the Sun-photometer data, and overestimation by OMI and MISR instruments (1. 23 and 1.08 respectively).

It is very matter to study and measure snow covers as one of the important sources of water supply. Due to the hard physical conditions of mountainous environments, there is no possibility of snow measurement. the use of  remote sensing with regard to low costs, up-to-date and extensive coverage in this field can be proven to be a good way to identify in snowflake areas. the main objective of this research is to estimate the surface coverage of Sabalan mountains using satellite images of OLI and TIRS sensors and using the object-oriented classification method. The classification of satellite digital images is one of the most important methods for extracting information, which is currently done with two pixel-based and object-oriented processing methods. The base pixel method is based on the classification of numerical values of images, and the new object-oriented method, which, in addition to numerical values, uses content, Texture, and Background information also in the image classification process. Therefore, in the present study based on the precision of the object-oriented classification, the object-oriented techniques were used to extract the surface of snow cover. In this study, due to the use of high resolution spatial resolution (Landsat 8) and the new method of classification of images, the snow surface was characterized by Normalized Difference Snow Index (NDSI), Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Brightness with a total accuracy of 91 percent, to 2142.62 square kilometers for the range Sabalan mountains have been extracted and the results can be used as alternatives to snowflake stations.

Awareness of the status of vegetation, land use change and surface temperature in each region, and the timing and location of their changes over time are important for micro and macro planning. In order to make optimal use of land, knowledge of land use changes is necessary, which is usually possible by detecting and predicting land use changes. Measuring the role of researches and researchers has been instrumental in the study of natural resources, especially vegetation, surface temperature and user variations in each location, as well as the availability of information for different times for valuable studies. In this study, ETM and OLI were used to study the process of land use change, vegetation cover, surface temperature, and hazards caused by them in perennial seasons. The results show that the area of use changes over the period 2000-2010 has decreased the area of use of the developed area, agricultural and growing gardens and the area of land and rangelands. Artificial vegetation has risen in aggregate and rangeland lands are showing a decreasing trend. Due to the importance of vegetation and its role in reducing the temperature of the earth's surface, the trend has been decreasing in regions with intensive vegetation and high temperature. Also, in the period from 2010 to 2017, the range was further increased and the city's growth continued sporadically, causing environmental changes and rising temperatures in the city. The change in the city's increased range has increased environmental risks, including the loss of good agricultural land and the increase in the temperature of the city. Due to the fact that most agricultural land is located in the vicinity of the city under cultivation of saffron, which in the warm seasons does not have surface coatings, changes in the type of cultivation can also affect the temperature of the earth.

This study was conducted with the aim of providing a remotely sensed water quality index in Assaluyeh port using remote sensing technology. so, according to the region conditions, studying of scientific resources and access to satellite data, the parameters of heavy­metals, dissolved ions, SST, chlorophyll-a and pH were selected. Then, by reviewing sources, the product MYD091km, MYD021km, MOD021km, MOD091km and level2 images of chlorophyll-a and SST of MODIS sensor were used after preprocessing operations. Also In-situ data were collected Simultaneously with the capture of satellite images in August 2014. Then, the relationships between the water quality parameters and MODIS data, with (R²) from 0.59 to 0.94 and (RMSE) from 0.07 to 0.1 were obtained. Next the images of the MODIS sensor from 2015 to 2017 were prepared and the models were applied to them, then the layers were standardized by fuzzy logic. Also time series of SST data from 2003 to 2017 were prepared and for each month the average pixel values were calculated and based on this, from 2015 to 2017, the variation of this parameter was standardized. Finally, an effective index for assessing the quality of coastal waters was provided by time series of satellite images and the waters of Assaluyeh port were zoned. The results showed that the water quality in 2015 and 2016 has shifted from poor to very ­­poor status in 2017. Based on the results, with the development of a proposed index, in future studies a continuous assessment of environmental monitoring is possible.

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.