modis sensor

Climate change is a significant environmental concern due to the sensitivity of glacier melting processes and snow density to climate variations. Currently, a range of satellite sensors, including AVHRR, MODIS, GEOS, and MERIS, are employed for snow monitoring and are extensively utilized to analyze fluctuations and changes in global snow cover. The MODIS sensor is particularly favored for its extensive global spatial coverage, suitable spatial accuracy, and frequent temporal coverage across various scales. Consequently, this study utilizes snow products derived from the MODIS sensor. In this investigation, data on snow-related days from the statistical period of 1989 to 2018 were collected for three provinces: Kermanshah, Ilam, and Lorestan. This data was processed using MODIS snow cover information pertinent to the Middle Zagros region, in conjunction with remote sensing techniques. The study provides a detailed examination of snow cover changes within the specified area. The Normalized Difference Snow Index (NDSI) was employed to detect snow cover within the MODIS sensor products. To differentiate pixels and identify various phenomena, the acquired images were processed using Geographic Information Systems (GIS) technology. Analysis of seasonal snow cover changes using MODIS sensor imagery indicates a significant decreasing trend in the majority of the studied area, particularly in the elevated regions. Notably, only the western and southwestern regions of the study area exhibit no discernible decreasing trend. Furthermore, the examination of snow-covered days throughout the study period reveals a decrease in snow cover in the Middle Zagros, with these changes intensifying in recent years, especially in the snow-covered areas of the region. Changes in snow cover during the winter months, particularly in elevated areas, were observed to be more pronounced compared to other seasons and regions within the study area.

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 the current research, based on the data of the satellite images of the MODIS sensor, located on the Terra and Aqua satellites and the GRACE gravimetric satellite, with the innovative approach of algorithm and coding in the web system of Google Earth Engine in the cities of the provinces (Golestan, Mazandaran and Gilan) and within the interval of years 2001 to 2022; Estimation and analysis of changes trend in snow surface area, snow depth, snow equivalent water and groundwater aquifer changes in the studied area were done. According to the obtained results, the highest surface area of snowfall with a value of 9496.01 Km2 occurred in 2005 and the lowest with a value of 2906.21 Km2 in 2010, while the maximum amount of snow depth with a value of 0.07 meters in 2008 and the highest water equivalent of snow was obtained with a value of 10 kg/m2 in 2008, and this shows the high correlation between snow depth and water equivalent of snow. The results of the investigation of the groundwater aquifer based on the three CSR, GFZ and JPL models in the studied years showed that the maximum volume of the groundwater aquifer with a value of 12.5 cm was in 2005 and the lowest with a value of -27.5 cm is in 2017. The findings of the research show the decreasing trend of the groundwater aquifer in 2017 with the value of -12.5 cm in connection with the decreasing trend of snow cover in 2017 with the amount of snow cover of 5432.56 Km2 in the study area. Based on the results obtained from the present research, it can be concluded that the process of snow cover changes had an effect on the volume of the groundwater aquifer in the studied area.

Deforestation is one of the biggest global environmental challenges because forests are one of the most important components of the world's ecosystem and play a key role in adjusting the climate and regulating various biochemical cycles. The forests of northern Iran are considered one of the most valuable forest ecosystems in Iran, and they are among the rich forests in terms of the diversity of plant species. Therefore, detecting changes with the help of multi-temporal data in forest levels allows us to prevent further destruction by automatically identifying these changes. The main goal of this research is to identify the thresholds and apply them to the NDVI vegetation index images in MODIS sensors and automatic monitoring of forest areas.

This research was conducted in the Central Alborz protected area with an area of more than 398 thousand hectares and very rich vegetation with more than 1100 plant species. In this research, the MOD13Q1 product of the MODIS sensor and the normalized vegetation difference index (NDVI) with a spatial resolution of 250 meters and a temporal resolution of 16 days, as well as The MCD12C1 product images of the MODIS sensor with a spatial resolution of 5600 m and a temporal resolution of one year were used in three layers with the classification schemes of the International Geosphere-Biosphere Program (IGBP), University of Maryland (UMD) and the Modis Derived Scheme (LAI). The 16-day NDVI images were converted to monthly images with the maximum value algorithm and the PCA algorithm with 25 components was applied to them to eliminate errors and noise. Using the Google Earth system, 5 random polygons were selected on virgin forest areas, in order to estimate the value of the pixels and finally determine the thresholds. Vegetation homogeneity was checked using MCD12C1 images. Forest thresholds were determined by calculating the mean and standard deviation of the sample areas during 16 years. Finally, the changes were detected by applying a threshold on the images of 2001 and 2016 and applying Boolean operations with the bolean operators on them. Then, using the Google Earth system, these points were validated.

By applying PCA to the images, the amount of noise was reduced to the optimal level while maintaining the original information of the images. The results of investigating the homogeneity of the vegetation in 5 sample areas showed that they are homogeneous in three levels and are deciduous broadleaf forests The results obtained from calculating the standard deviation and monthly average of forest values indicate that the months of June, July, and August are most suitable for assessing changes in the forest cover within the study area. The threshold for these months was obtained as 8596.55, 8000 and 8497.51 respectively. By applying these thresholds to the images captured in these three months in 2001 and 2016, and then subtracting the images from each other, the pixels that experienced a decrease in forest value were identified. Finally, after performing the Boolean operation, 200 pixels with an approximate area of 1728.23 hectares were identified as changed points with a decrease in forest cover. Upon cheching, the overall accuracy for the determined points was found to be 88.5%. Hence, the application of these thresholds enables the automatic detection of changes in forest cover areas in an up-to-date and continuous manner.

In general, the Central Alborz protected area is one of the important forest centers of the country, and according to the results, we have lost a significant number of forests during the 16 years due to various reasons. Also, the results of this research indicate the importance of using automation methods in identifying forest cover changes by applying certain thresholds on the NDVI index images of the Modis sensor in remote sensing.

Dust aerosols have adverse effects on public health, air quality, and the environment; therefore, continuous monitoring of their spatio-temporal changes in different regions, especially arid and semi-arid regions, is very important. Considering the vast spatial extent of Kerman province, the inappropriate distribution of air pollutant measurement stations, a number of synoptic stations, as well as the large area of the province and the different climatic and biological diversity; the use of remote sensing techniques and especially the use of Aerosol optical depth (AOD) index of the MODIS sensor can be one of the appropriate tools to analyze and investigate the trend of temporal changes and the pattern of spatial distribution of dust particles in different regions of this province. This study has been carried out with the main purpose of analyzing the changes in dust aerosols based on the average monthly, seasonal and annual values of aerosol optical depth in different regions of Kerman province during the period from 2000 to 2022, so that while knowing the state of these changes, the high-risk areas of dust production in recent decades should also be identified.

In the present study, the MODIS sensor aerosol optical depth index product (MCD19A2) was used to analyze the trend of changes in dust aerosols in different regions of Kerman province. In order to validate this product, the average daily horizontal visibility recorded on the days of dust events was used. The data related to the MCD19A2 satellite product was downloaded through programming in the Google Earth Engine environment for different cities of Kerman province in the period from 2000 to 2022. After filtering the dusty days (AOD>0.5), the average values of the mentioned index on monthly, seasonal and annual time scales were calculated separately for each region. The Mann-Kendall test was then used to analyze the temporal changes of the optical depth of dust aerosols in different regions of Kerman province.

Based on the spatial distribution pattern of the air quality, it was found that the air quality of the eastern half in March and April was lower than other areas of the province, while in the central areas, the air pollution was less and the air quality was more favorable. The results of the Mann-Kendall test showed that in June and December, the trend of changes in the dust aerosols optical depth in more than two thirds of Kerman province had a non-significant decrease (Z>-1.96). This is why in November and May, half of the province and in other months more than two-thirds of the province have experienced incremental changes over the last 23 years. The pattern of long-term average spatial changes of the optical depth of aerosols on a seasonal scale showed that Kerman province was more affected by aerosols in the spring and summer seasons. Maximum dust pollution has occurred from Raver to Rigan, Jiroft to Manojan, Sirjan and Anar cities. The mentioned areas in the cold seasons of the year have also experienced the worst conditions in terms of the presence of aerosols, but their concentration has been significantly lower compared to the warm seasons of the year. The results of the Mann-Kendall test showed that 75% of the cities of Kerman province have faced an increasing-insignificant trend of spring dust events, which has led to an increase in the concentration of aerosols. In summer, these changes were increased in more than 95% of the province's regions, and the most changes were observed in Anar, Baft, Fahraj, Kohnouj, Kohbanan, Orzuiyeh and Rafsanjan. Although in the fall season, about 25% of the regions of the province had a decreasing trend and about 4% had no particular trend, but in more than 70% of the regions of Kerman province, the trend of changes in dust aerosols was an increase, and among the regions with a positive trend, the cities Baft and Jiraft had the worst conditions (Z>1.96). The remarkable result is the rising trend of dust and dust aerosols in all regions of Kerman province during winter, particularly in the cities of Anar, Anbarabad, Baft, Faryab, Ghaleganj, Manojan and Rabor. Although Anbarabad, Baft, Orzuiyeh, Shahrbabak and Sirjan exhibit a stronger annual increase compared to other areas, the rise in AOD changes is evident throughout the province. This 23-year trend indicates that Kerman has not experienced favorable conditions, highlighting the increasing frequency of dust events in this desert region.

Based on the findings of the analysis of the temporal changes of dust aerosols; the western cities of the province have become more sensitive to the phenomenon of wind erosion compared to the past. This can be a serious threat to the residents of these areas. On the other hand, the increasing trend in the southern and eastern regions highlights the intensification of dust events, contributing to the decline in air quality in the province over recent decades. Considering the intensification of the phenomenon of wind erosion and dust storms in many areas of Kerman province, it is necessary to control this phenomenon in order to increase the health security of the residents of the affected areas. Although the awareness of the spatial and temporal changes of dust aerosols has led to an increase in our understanding of the phenomenon of wind and dust erosion in the province, but in order to prevent, control and reduce the risks caused by the occurrence of this destructive environmental phenomenon, it is necessary to identify the main driving factors of these changes that are suggested to be investigated separately for each city in future researches.

Studying and measuring changes in snow levels is very important as one of the important sources of water supply. Due to the harsh physical conditions of mountainous environments, there is no possibility of permanent surface measurement to estimate snow sources and form a database. The use of satellite images and remote sensing due to their low cost, up-to-dateness, and wide coverage is a way forward in this field, and it can be a suitable method for identifying snow catchment areas and evaluating its changes to achieve this goal. The presence of snow in the basins affects the water resources that are stored in the form of frozen water on the surface Therefore, temporal and spatial monitoring of snow cover has been used for hydrological forecasts for years. Remote sensing images are a useful tool for estimating snow cover changes and analyzing the spatial pattern of this important environmental phenomenon, especially in high areas where there are few available ground stations or there are no stations

In this research, data from V006-MOD10A1 of the Terra satellite and V006-MYD10A1 of the Aqua satellite were used for snow coverage from 2003 to 2020, which have a spatial resolution of 500 meters and a daily time resolution. The Modis sensor data in both Terra and Aqua satellites were converted from HDF format to TIF format with a threshold of 0.1-1 to binary and ASCII and with a geographic coordinate system. These data were processed using Python coding language. Cloud effect was reduced by using three algorithms: data combination, spatial, and temporal filtering. For the digital elevation model (DEM), the data of the Japan Space Exploration Agency (JAXA) called ALOS World 3D (AW3D) was used. After preparing the snow cover database, average snow-covered days (SCDs) were calculated on a seasonal scale and maps of the spatial distribution of snow cover in this period were produced. Then, the relationship between the two components of SCAs and SCDs and the relationship between SCDs and altitude in the north-western geographical area were investigated and analyzed.

According to the pattern of winter SCDs, the flat plains around Lake Urmia, the plains around Zanjan City, Moghan Plain, and the lands around Sanandaj City show the lowest rate of SCDs (less than 10 days of snow cover). The maximum of winter SCDs corresponds to Sablan Heights with more than 60 days of snow cover. According to the average spring SCDs in almost the entire northwest area, the drop of SCDs compared to the winter season is evident. This reduction is much more evident in low and flat areas and plains than in high and mountainous areas. Spring SCDs are still high in the main altitudes such as Sahand, Sablan, Qandil, Bozghoush, and Qara Dagh and reach about 54 days in the spring season in Sablan. The decline of SCD started from the south and covered more areas of the northwest region. Summer SCDs are facing a sharp decrease in the northwest region. Except for the heights of Sablan, Sahand, and the heights of the western border including the Qandil range, the rest of the areas have snow cover for less than 2 days in summer. It is noteworthy about the heights of Sablan, which still can maintain snow cover for up to 55 days in the summer season. The average autumn SCDs in the northwest region show an increase again in the autumn season. The heights of Sablan, Bozghoush, Sahand, Qara Dagh, and Qandil all show a significant increase in SCDs in the fall season, and in the high parts of these roughnesses such as Sablan, the fall SCDs increase to over 60 days. In other areas, mainly the main body of the roughness has SCDs above 20 days. Investigating the phenological changes of SCAs and SCDs in the seasonal period showed that the highest SC levels in the winter season have SCDs of about 15 days. This surface is about 18000 square kilometers. In general, the areas and regions that have SCDs between 5 and 25 days have the highest SC levels in this season. In the spring season in the northwest region, the SC levels, which have SCDs of less than about 8 days, reach a maximum of 160,000 square kilometers. In the summer season, lands with SCDs of less than 5 days occupy the highest levels, reaching a maximum of 160,000 square kilometers. In autumn, the regions with SCDs of about 7 days have the highest SC levels with an area of about 50,000 square kilometers.

The findings indicate a decreasing trend of SCDs from winter to summer. Meanwhile, the average autumn SCDs in the northwest area again take on an increasing tone in the autumn season. Analyzing the seasonal changes of SCDs in the studied area showed that this phenological component of snow is strongly influenced by the two factors of altitude and latitude. Sablan mountain range has special conditions to maintain snow cover in the study area even in the summer season. The analysis of seasonal changes of SCAs with SCDs shows that the maximum extents of SCs in winter, spring, summer, and autumn seasons have SCDs of approximately 15, 8, less than 5, and 7 days, respectively. In winter, areas with SCDs of more than 40 days include significant SC levels of about 2000 to 8000 km2. On the contrary, in the autumn season, the areas with SCDs higher than 40 days have almost negligible SC extents, whose values reach below several hundred square kilometers. Analyzing the changes of SCDs with the altitude measurement in the seasonal interval, showed the obvious relationship of this snow phenological component in association with the altitude; In such a way that the highest values of SCDs in winter belong to the altitude band of 2500-3500 meters, in spring to the altitude range of more than 2500 meters, in summer to altitudes above 4000 meters and in autumn to the altitude belt of 2000-3500 meters.

The main goal of this research is to analyze the time series trend of fire events in natural areas and reveal the relationship between these fire events and vegetation levels in Lorestan province. In this regard, the data of the fire product of the Madis sensor (MOD14A1) and the vegetation product (MOD13A3) of the Madis sensor were used during the statistical period of 2000-2020. The monthly and annual spatial distribution of fires in Lorestan province was investigated. Cross-information matrix analysis and spatial correlation matrix were used to reveal the relationship between fire occurrences and vegetation. The results showed that more than 70% of the total frequency of fire occurrences in natural resources fields (fires with code 2) in Lorestan province is related to June and then July. In terms of the long-term trend, the 21-year trend of the frequency of fire incidents in the province showed that the frequency of incidents in the natural resources areas of the province has generally increased with an annual slope of 3 incidents. The results of the correlation analysis between the monthly vegetation cover and the annual frequency of fire occurrences showed that the fire occurrences in the province showed a significant correlation with the vegetation cover changes in 4 months of the growing period, i.e. from May to August. Cross-matrix analysis between the spatial distribution of fire occurrence foci and NDVI index, both of which were products of MODIS measurement, indicated that, in general, the highest frequency of fire occurrences in Lorestan province in the period from May to August corresponds to Greenness range was 0.15 to 0.22. This range of vegetation generally corresponded to rainfed lands, weak pastures and low-density forest patches

Extensive and massive agriculture, along with other agricultural activities such as animal husbandry, industrial activities in the southern half of the province, has created and intensified extensive changes in the environmental resources and natural structure of the province. This extensive change can show its effects and consequences in the destruction of forest lands, the transformation of rich pastures into poor pastures and barren lands, severe soil erosion, and finally the creation and development of internal centers of dust. and intensify the severity of dust incidents in the province. Dust events have profound and significant effects on agriculture and soil fertility, health and hygiene, disruption and destruction of industries and power plants, and negative effects on the environment, including the deterioration of forests. Airborne particles, which are mainly driven to the region by dust storms, are one of the important components of the atmospheric system. They can not only change the albedo of the energy balance by acting as cloud particle nuclei, or ice nuclei.

The study location of this research is Khuzestan province, which is one of the most challenging provinces in the country in terms of environmental hazards. This province, with an area of about 6.5 million hectares, occupies about 4% of the country's area. Dust is one of the major and most important challenges of this province. Its destructive effects can be traced in various dimensions, such as the quality of water resources, the quality and performance of agricultural products, industries and energy transmission networks, and the air quality of cities. Three categories of data have been used in this research. The data of the first category is related to the data of widespread dust days in Khuzestan province. These data were obtained from the dust codes of the current air condition (ww parameter of synoptic stations of the province) during the statistical period of 2000, 2020. The second category of data was actually the remote sensing data of MODIS sensor, which included the Aerosol Optical Depth (AOD) product of MODIS sensor (MOD04 product) and Aerosol Exponential Index (AEA). These two indicators are dimensionless but with different directions. In the AOD index, higher numbers represent more aerosols in the atmosphere and in the AEA index, in addition to the presence of dust in the place, it also provides the size of the aerosol particles. Finally, the third category of data is the reanalysis data related to incoming net shortwave radiation (SNSR), which was taken from the reanalysis data of the European ECMWF database version ERA5 with a spatial resolution of 0.5 arc degrees. 

In this research, it was tried to investigate the influence of the dust event in the context of fluctuations and daily changes in the amount of net shortwave radiation received on the earth's surface. The results of the investigation of three cases of widespread dust in the province showed that in these three cases of widespread dust, aerosol particles are generally in the central, southern and western parts of the province (plain and lowland areas of the province) from the type of medium to large particles (index angstrom between 0.5 and 1) and in the eastern and northeastern parts, it was of the type of coarse particles (angstrom index less than 0.5). In the context of the impact of dust events on the amount of shortwave radiation received by the earth's surface, it was seen that in the dust event of July 22, 2010, the Angstrom exponential index indicates the presence of coarse particles in the atmosphere near the earth's surface and the AOD index also indicates the presence of dense dust in the entire area of the province. The received net shortwave radiation (at 12 noon or 09 UTC) was about 194 watts per square meter (about 28 percent) lower than the average for the same month. This drop rate was less in the other two dust waves, whose AOD and Angstrom index values indicated finer and less concentrated dust. In the dust wave of June 19, 2012, the amount of net shortwave radiation received was only 5% (25 W/m2 at 12 noon or 09 UTC) less than the long-term average, and this drop in the dust event of May 12, 2018 was equal to 28 W/m square (about 4% drop compared to the average of the same month).

Drought is a creepy phenomenon, reversible climate and an unavoidable reality in different climates that is caused by a lack of rainfall over a period of time. The purpose of this research is to monitor in Bushehr province based on SPI and VCI indices using MODIS sensors. In this regard, using 54 time-series images of the Normalized Vegetation Difference Index (NDVI) of MODIS satellite products from the growing seasons (January, February and March) between 2001 and 2018, the VCI, Vegetation Status Index, which is a normalized index to assess drought is calculated. Then, the standard SPI precipitation index for 5 synoptic stations (Borazjan, Bushehr, Deylam, Dayyer and Jam) was calculated during an 18-year statistical period, and according to the SPI classification table, very severe drought, severe drought, drought Moderate, almost normal, moderate wet, very wet and very severe wet were divided. Then the correlation coefficient of these two indices was calculated and it was found that the two indices have a significant correlation coefficient. The results of drought monitoring showed that during this 18-year period, in some years, the VCI index of severe drought (2009) shows that less rainfall occurred in the same year, and in 2014 it shows a very severe wet season where significant rainfall has occurred. Also, the results of the correlation between these two indices showed that the six-month SPI correlation coefficient has a higher correlation with the average VCI index than the quarter. In general, it was found that in the absence of meteorological indicators, a satellite index can be used to monitor drought.

The increase in fire activity in many regions of Iran in recent decades has raised concerns about the short-term and long-term consequences. Forest and pasture fires will affect the quantity, quality, and health of natural ecosystems. The first step in controlling and preventing forest fires is monitoring. Therefore, this study was conducted to estimate ​​fire areas and identify the spatial-temporal changes of these events in the vegetation areas of Iran. To achieve this goal, TERRA satellite MODIS sensor data including active fire products (MOD14A1) and burned areas (MCD64A1) for 20 years (2001-2020) have been used. The results showed that the maximum extent of fires in the vegetation areas of Iran is related to July, which covers about 4100 hectares. This month, a large part of the country's forests, especially in the northwestern parts of the Arasbaran vegetation area, has caught fire. In contrast, the minimum amount of fire areas in the vegetation areas of Iran is related to April, which was observed as two limited zones in the northwest and west of the country. Due to the maximum time of forest fires in the hot and dry months of the year, including June, July, August, and September in the western part of the country in the Zagros. The linear correlation between the burned areas and the areas with active fires in Iran showed that there is a significant relationship between these two satellite products. This connection shows that the burned areas in the vegetation areas of Iran are among the active centers of forest fires.

Climatic phenomena in different regions of the world are different in relation to the geographical characteristics of each region. Dust is one of the dominant atmospheric phenomena in desert and semi-desert regions of the world. This phenomenon is related to air pollution in most parts of Iran with dry climate, including Birjand in eastern Iran. The origin and direction of dust in this area are varied in the year, and knowing them helps urban risk management, forecasting and desertification. In this study, Landsat satellite images were used to determine the origin of dust in the period of 1955-2017. The dust day was defined based on the classification of Shao and Dong with code 06 and 07 with horizontal visibility of less than 10 km, and revealed with BTD and TDI index. To determine dust atmospheric patterns, grid data from NOAA was used, and dust trajectories were carried out with the HYSPLIT model. The results showed that the most dust days in 1963 and 2008 (45 years), with 148 and 128 days, respectively, which mainly occur in the warm season and July. The 120-day winds of Sistan in the east of Iran plays a major role in transporting dust particles and air pollution in Birjand from the east and northeast from the deserts of Central Asia, Turkmenistan, Afghanistan, Khorasan Razavi plains and interior plains. But in the cold period of the year, Siberian cold masses from the east, cold fronts (migrating anticyclone), occluded fronts and low pressures from the northwest, west and southwest by passing through the central plains of Iran and sometimes the deserts of Syria and Iraq causes dust in the upper levels of the atmosphere. In all these centers, the occurrence of particles and pollution of cities prevails during periods of drought.

In recent years, dust has been Iran's most important environmental crisis. On 25 February 1396, there was heavy dust in Iran which was important for research in two aspects: spatial extent and time of occurrence (winter). The purpose of this research is to analyze the synoptic conditions and to trace the phenomenon. For this purpose, satellite and re-analyzed data were used. Modis data showed that more than 60 percent of Iran's area to this day was affected by dusty airwaves, indicating that this phenomenon is widespread. The maximum optical depth of aerospace was observed in the northeast of Yazd province with a mean of 2.03. The results of the synoptic analysis of this phenomenon showed that at sea level a low-lying core comprises northeast and central parts of Iran. At a level of 850 hPa, a low altitude north of Lake Baikal and a high altitude over southern Pakistan and their resulting cyclonic and anticyclonic movements have led to the convergence of airflow in central Iran. The thermal contrast caused by the cold air of the upper latitudes and the warm air of the lower latitudes have resulted in the formation of a core of the hornbeam at a level of 500 hPa in central Iran. Minimum moisture content in all levels as well as rising air (based on equilibrium values) have provided the conditions for the rise of sweeping dust in Iran. Analysis of air mass routing 48 hours before the dust peak in three parts of southwest, central and northeast of Iran showed that air mass transfer route was in all parts of western Iran. However, even in the center of Iran, even at a height of 500 meters, the air mass has shifted from the Arabian deserts to Iran, resulting in heavy winter dust transfer to central Iran.

Snow-covered (SC) surfaces influence the land surface energy balance through albedo feedback, and also have a major impact on climate processes, human activities, and the hydrological cycle. Land surface temperature is one of the main elements in knowing the climate of a region, whose changes and fluctuations in different altitude classes are very useful for hydrological studies. The purpose of this study is to evaluate and investigate the relationship between ground surface temperature and snow cover level with the topographical component of height in Urmia lake basin. In this research, due to the ease of access to remote sensing data and the appropriate temporal and spatial separation of Terra satellite images, monthly, seasonal and annual MODIS sensor images have been used in the period of 1379-1399. The obtained results show that there is an inverse relationship between LST and SC, also the examination of SC maps and elevation classes shows that there is a direct relationship between these two variables, in fact, with the increase in altitude, the stability of snow in the region increases so that at altitudes higher than 3000 m, the amount of snow cover is more than 98% compared to the region. The changes in the temperature of the earth's surface at different altitudes are the reverse of the changes in the snow cover, so at altitudes less than 2000 meters, the annual average temperature is 21-35 Celsius, but at altitudes higher than 3500 meters, the average temperature is about 7-13 Degree.

The study of snow cover as one of the most important sources of freshwater supply is of great importance. Due to the mountainous conditions of Iran, it is not possible to measure the area of snow cover. Accordingly, the use of satellite imagery to identify snow storage is of great importance. In this study, the spatio-temporal changes of Iran snow cover for the cold period of the year were evaluated using the snow cover product of MODIS Terra satellite during the period of 2003-2018. The trend and slope of the snow cover were investigated using Man-Kendall non-parametric tests and the Sen's slope estimator and change-point of snow cover using Buishand test. The results showed that in January, the highest amount of snow cover is 16.6 percent, and the lowest amount of snow cover was computed in October, which is less than 1 percent. The main center of Iran's snow cover in the cold period of the year in the highlands is above 4000 meters. The snow cover trend is negative in all studied months and the maximum decrease in snow cover was calculated in January and the change-point was calculated in 2008 January, which is statistically significant at the level of 0.05. The significant decrease in snow cover during the cold period of the year which is a major threat to Iran's water resources.

The use of remote sensing is particularly important in identifying burned areas due to its extensive spatial coverage and the provision of information at different times. Today, Modis fire products are widely used for this purpose. The purpose of this study is to evaluate the capability of Modis MOD14, MOD14A2 (Terra), and MYD14, MYD14A2 (Aqua) fire detector products and to identify fire areas in Golestan state. First, a point map of all the images was generated, then to evaluate the accuracy of the fire products, the prepared point map for the products was compared with terrestrial reality data. If the location of each of the reported fires is consistent with the fires identified by the products, that location was correctly identified as the fire. Landsat images were used as a ground accuracy map to evaluate the accuracy of Modis images. The results showed that six regions identified by level 2 fire products and eight regions were detected by level 3 fire products were identified. The results show the accuracy of the images with a coefficient of R ^ 2 of 0.94 and a coefficient of RMSE of 426.12 ha. The studies conducted in this study show that to improve the performance of the text fire detection algorithm, this algorithm is proposed for the forests of Golestan province and following the conditions and characteristics of the fire area, its intensity, and area. Be developed to provide better results.

Land surface temperature (LST) plays an important role in surface energy balance. A set of environmental parameters, such as temporal and geographical changes, thermal properties, biophysical properties, climatic parameters and subsurface conditions can cause heterogeneous spatio-temporal distribution of LST and its anomalies to be. LULCC-induced surface temperature anomalies have important implications for understanding the physical mechanisms associated with the surface to changes in various biophysical factors, including albido and surface roughness (also known as aerodynamic resistance). The purpose of this study is to evaluate the seasonal changes and abnormalities of daytime and nighttime land surface temperature in Iran based on LST derived from satellite data.

In this study, the following steps were performed:A study area:The whole country of Iran was wanted. To better reveal the behavior of surface temperature anomalies in Iran, the data has been converted to a seasonal scale and also for the first time in the country, surface temperature anomalies have been studied separately for night and day.B) DataB-1) Moderate Resolution Imaging Spectroradiometer(MODIS)To investigate the anomaly of land surface temperature, the MODIS sensor data of Terra satellite MOD_LSTAD and MOD_LSTAN products were used for day and night data with a horizontal separation of 10 km and the statistical period of 2001-2018, respectively.C) Calculate trend and trend slope using non-parametric Mann-Kendall and Sen’s testsIn order to evaluate the abnormal trend of land surface temperature in Iran, non-parametric Mann-Kendall (M-K) test was used. The non-parametric Sen's method was used to estimate the slope of the process in the time series of land surface temperature anomalies and day and night in Iran.

The results showed that the mean anomaly of daytime land surface temperature in Iran (LSTAD) in the three seasons of winter, spring and autumn is negative and in summer is positive. Also, the long-term mean anomaly of night surface temperature (LSTAN) is negative in cold seasons (winter and autumn) and positive in warm seasons. The positive maximum of LSTAD in Iran was 0.172 in summer and its negative maximum was -0.672 in autumn. The same statistical quantity was obtained for LSTAN positive anomaly in summer 0.266 and in autumn 0.244. The minimum LSTAD was calculated between -1.942 to -3.097 and the maximum was calculated between 1.047 to 2.865. For night, it showed a minimum between -0.748 to -1.296 and a maximum between 1.597 to 2.189. The average statistical trend of Iran LSTAD and LSTAN in all seasons except autumn is increasing. This amount, despite being incremental, is not significant. During the day, the maximum average trend of increasing abnormality is obtained in summer (0.744) and at night in spring (1.038). The minimum and maximum trends in both day and night in Iran are significant at the alpha level of 0.01 and in terms of trend intensity, the warm seasons are more intense. The highest computational Z-score of Mann-Kendall test was obtained at night with the value of 4.097 (spring). Also, the same maximum amount per day was calculated with the amount of 3.917 in summer.

In this study, we have evaluated the day and night land surface temperature anomaly of Iran using Terra satellite MODIS sensor data during a long-term statistical period (2001-2018). The non-parametric Mann-Kendall test was used to study the trend and the non-parametric Sen test was used to calculate the trend slope. The positive anomaly of Iran's land surface temperature is higher at night than during the day and this amount is also significant in the warm seasons of the year. The maximum positive anomaly was obtained during the day during the summer with a value of 0.172 degrees Celsius and for the night with a value of 0.266 degrees Celsius. The average anomaly trend of land surface temperature during the day and night in winter to summer is increasing and only in autumn this amount is decreasing. The minimum and maximum trend in each period of time is significant at the alpha level of 0.01 and the intensity of the trend is more at night than during the day. The main focus of negative anomalies is recognizable in low-lying dry areas, inland arid regions located in the east and southeast of Iran and inland holes of Iran. While the increasing anomaly in the highlands and high latitudes of Iran is significant. Also, the dominant upward trend can be seen in the highlands of Iran, except in autumn; In this regard (Fallah Ghalhari, Shakeri and Dadashi Roudbari,2019) who used three methods of microcirculation SDSM, MarkSimGCM and CORDEX simulated the minimum and maximum temperature of Iran under the models CanESM2, GFDL-ESM2M and MPI-ESM-LR up to 2100 ; It was concluded that the annual temperature anomalies of the selected models are at high latitudes and mountainous highlands, which is in line with the results obtained in this study. One of the most important roles of land surface temperature and its anomaly is changes in convective processes, mixture layer depth and wind speed. Therefore, increasing the anomaly of land surface temperature in Iran can increase convection on the one hand and change the regional wind speed. (Dadashi Roudbari,1399) in explaining the role of surface temperature and climate change has stated that the warm surface of convection increases and causes the mixing of surface air and high surface air. Since the velocities of horizontal winds at land level are zero and at higher levels, the vertical mixing of horizontal winds causes wind speeds close to the earth's land surface to increase and wind speeds at high levels to decrease. Variability in surface temperature also changes the air temperature near the surface. In addition to what has been said, land surface warming in the highlands of Alborz and Zagros also affects the carbon cycle; Because surface heating accelerates the melting of snow and ice in these areas, resulting in the release of excess carbon (Fili, Roir, Gotha, & Pregent, 2003). Therefore, it is worthwhile to pay more attention to policies related to carbon stabilization as well as programs related to water resources and dam construction based on what was addressed in this study.

Drought evaluation is important in terms of spatial and temporal for planning to reduce damages in the Kordestan province. In this research, Standardized Precipitation Index and the Enhanced Vegetation Index have been used from the extracted satellite images for determinants of drought. so, the statistical data of Meteorological stations including maximum monthly temperatures, total annual precipitation and the images of MODIS sensor have been employed. By comparing meteorological parameters such as average annual temperature, rainfall and the comparison of maps of the Standard Precipitation Index and Enhance Vegetation Index, the condition of drought has been investigated in the region in a 17-year period. The results of the two SPI and EVI indices indicate that the drought is due to rain changes have in the west-to-east direction. This phenomenon is more severe in the eastern regions whereas vegetation sensitivity and the fluctuation of its variations have been affected by precipitation changes in the north-to-south direction over the region. In this way, the southern regions have shown higher sensitivity. Southern regions are generally more vulnerable to droughts, especially in the south-east of the province. Regions with high drought sensitivity make up about 10 percent of the area regarding the regions in the province, whereas 91 percent of the area of regions with very high drought sensitivity is places where the landuse involves growing wheat with rain water.

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 heavy metals, 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.

Drought phenomenon as one of the most disastrous natural disasters in the arid and semi-arid regions of the world has caused a lot of problems in these areas. Due to its proximity to large desert areas, Lorestan province is frequently exposed to severe dust. Our goal in this research is to identify areas susceptible to becoming dusty in Lorestan province The method of doing research is analytical and statistical and the method of doing the work based on classification, each of the layers concerned, determining or extracting the criteria of dusty cores, weighing each of the layers using the AHP pair weighing matrix, Finally, the final collapse of the relevant layers is to determine the favorable areas of dusts using GIS, Spss, Expert Choice software. The results showed that about 9% of the area of ​​Lorestan province has a high potential for becoming dusty locally. The spatial distribution of this floor in Lorestan province was presented in the final map of the total weight loss of all layers, which indicated that the largest area of ​​this class is concentrated in the eastern regions, especially the northeast of the province, which includes the Azna and Oligudarz counties. There are also parts of this category in the southern regions of the study area, including the cities of Poldokhtar and Rumshagan. In the central regions of the province as well as in the northwest of the province including the Khorramabad, Delfan, Dynasty and Dorood districts this class is not observed. In the southern parts of the city of Kohdasht, small parts of the floor of the potential centers of dust are also observed. This floor has the most risk of becoming a dusty focus. The power source of many of the provincechr dusty incidents can also be said to be areas where some of them are currently potential sources of dust.

Particulate matter is any liquid or solid component (except pure water) that is dispersed in the Earthchr('39')s atmosphere and is microscopic or sub-microscopic but larger than the molecular size. These particles play an important role in the Earthchr('39')s climate. Suspended particles are created by various natural or anthropogenic processes and are among the deadliest types of air pollution, especially smaller particles less than 10 micrometers in diameter. Since the number of pollution station is very low, Satellite measurements have been widely used to estimate particulate matters (PMs) on the ground and their effects on human health.

In this research, we tried to estimate PM10 using the regression model based on the Aerosols optical depth. Because the AOD value recorded by satellite sensors is affected by the weather conditions, to increase the accuracy of the PM10 estimation, meteorological parameters were also used in the AOD to PM10 conversion model. The used meteorological parameters include surface wind speed, surface temperature, relative humidity, visibility, and planetary boundary layer height.  Since the data used were extracted from four different sources with different temporal and spatial resolution, it is necessary to apply a method for integration and synchronization in space and time. To generate AOD and PM10 data from the Aqua satellite, pollution stations were mapped onto satellite images and AOD values ​​for the nearest neighbor pixels as well as values ​​for a 3 by 3 window were extracted. So, two pairs of AOD and PM10 were formed, one with the nearest neighbor values ​​and the other with the weighted average AOD values ​​in a 3 by 3 window. Because the PM10 values ​​were more closely related to the AOD values ​​than the nearest neighbor, the pair of the weighted average was excluded from the calculations. There  were extracted 100 samples in warm season (June, July, August and September) for model development (Shokoufa station data) and 65 samples for validation (Cheshmeh and Atisaz station data) and 140 samples in cold season (November, December, February and March) for model development, and 50 samples for validation. In the last step, the accuracy of the model was evaluated using indices such as coefficient of determination, mean error deviation (Bias), and mean square error (RMSE).

The results showed that AOD and PM10 have a better relationship with each other in the warm season than in the cold season. Only two variables of AOD and wind speed were included as independent variables in the best model presented for the warm season; both of which have a direct relationship with PM10, that is, with increasing of both variables the value of PM10 increases. The results showed that the regression model of warm season can only predict 16% of the PM10 variations correctly, which is not a satisfactory result. In the multivariate cold season regression model, only the visibility remained, and other variables that had no significant effect on model improvement were excluded from the regression model. Multivariate correlation coefficient of this model was estimated to be 0.59. Therefore, the cold season regression model, at best can predict 35% of the PM10 variations correctly. By deleting the visibility variable, it was attempted to measure the impact of other variables such as AOD on PM10 estimation. In this model, the boundary layer height, AOD and temperature variables were retained. The boundary layer height variable has a negative relationship and the other two variables have a positive relationship with PM10. The maximum effect of temperature on PM10 is justified by the increase in boundary layer and the relationship of these two dependent variables which decreases PM10 density, but since this role of temperature element is represented by the same boundary layer height variable, what remains is the secondary role. Temperature is in the PM10 particle production. However, the latter model is weaker than the previous model and its multivariate correlation coefficient is 0.45 and accounts for 20% of the PM10 variations. In the evaluation of the model in the warm season, the root mean square error at the Cheshmeh station was 31.76 µg / m3 and at the Atisaz station was 33.56 µg / m3. In the cold season, the root mean square error was estimated to be 47.10 µg / m3 at the Cheshmeh station and 49.81 µg / m3 at the Atisaz station, respectively. However, using the model with independent variables AOD, boundary layer height and temperature, the root mean square error was estimated to be 38.42 μg / m3 at the Cheshmeh station and 39.11 μg / m3 at the Atisaz station. The former shows a decrease of approximately 10 micrograms per cubic meter. Therefore, although the latter model with independent variables of boundary layer height, AOD and temperature had less multivariate correlation coefficients and determination coefficients than the model with independent observational variables in cold season, it yielded better results based on evaluation of the model for different locations and days from modeling location and days.

Generally, based on the results, it can be stated that regression models of warm and cold seasons are statistically acceptable at a confidence level of 99%. Therefore, the amount of PM10 fluctuations that is justifiable by the model is not accidental, although the modifications justified by models are low. The calculated errors in the evaluation section showed that the proposed models are not very accurate. It was also found that in the warm season, the wind speed can improve the results of the regression model of the relationship between AOD and PM10, and in the cold season, the variables of the boundary layer height and temperature in the regression model are statistically acceptable and improve the results of the model.