Albedo is one of the key parameter in climatic studies. Albedo climatology investigation can be a tool to recognize environmental changes. The MODIS continuously produces the land surface albedo on a global scale and with the appropriate spatial resolution and makes it available to researchers. In this research, in order to analyze the climatology albedo of Iran, firstly, the data produced by the MODIS sensor MCD43A4 product in the range of Iran in the period from 1/1/2001 to 12/30/2021 with a spatial resolution of 500 meters and a daily temporal resolution was taken from the NASA website. After the necessary pre-processing, the long-term average monthly, seasonal and annual albedo of Iran was calculated. The findings on a monthly scale showed that in the months of Jan, Feb, and Mar which are known as Iran's snow-covered months, Iran's albedo is maximum and is decreased in the transition months, and then in the warm months of the year (June, July, and Aug) it is increased again due to the dryness of the land and the increase in the land surface temperature. This two-way behavior is also evident in the seasonal scale. These calculations are made in the worst conditions (July) over 98% of the area of Iran and in the best conditions (Jan) on 99.97% of the area of Iran. In other words, in the July, the albedo time series data was complete for about 98% of Iran's area, and there was a statistical gap in about 2% of Iran's area. In the research of Kefayat Motlagh et al. (2021), the albedo data gap values of the MODIS sensor have been investigated in different seasons and annually. Results show that the maximum distribution of albedo in winter and autumn seasons corresponds to the snow-covered heights of Alborz, Zagros and the northwest of the country. But in the spring and especially in the summer, with the increase in air temperature and surface temperature, most of the wetlands dry up. With the drying of the bottom of Jazmurian, Hamon, Shadgan, Maharlo and salt lakes and Urmia, salt flats appear. These salt marshes also show a high albedo due to their white color. On the coastal of the Caspian Sea, low albedo is seen due to the decrease in land surface temperature and increase in soil moisture. This part of the research is in harmony with the findings of other studies conducted on the land surface albedo of Iran (Soltani Akmal, 1397; Kefayat Motlagh, 1400; Karbalaee, 1399). Also, the findings showed that the long-term average albedo of Iran is 12.5%, which is about half of the average planetary albedo (24%) (Zhang et al., 2010). This part of the research is also in line with the research of Karbalaee et al. (2021).

The water salinity is the one of the important environmental marine factors and play important role in the Prediction of the surface ocean currents, spatial analysis of the fish aggregation, determination the density and studying its changes and also in ecological properties.This parameter change greatly with time and space and proper recognition is need to short time intervals measuring (monthly) for Multiple points of the study area.in the classic way, appraising one or more specific factors of water quality is expensive and spend great time and also is not good indicator for the whole area of a large region. But In recent years satellite technology and remote sensing are considered as appropriate tool for the assessment some of the water quality parameters, because due to the digital nature of this data, extensive availability, regular measurement, repeating data in the short time periods and spending less time and cost, can complete wide range of project.the purpose of this study is mapping sea surface salinity of Persian Gulf in iran and Gulf of st Lawrence in the Canada, using MODIS satellite data. in this regard a software produced in IRAN for the first time that can create salinity, temperature and density maps of sea surface In three different models with proper accuracy by entering the MODIS satellite data and field data.High ability and flexibility of artificial Neural Network in approximation of nonlinear and linear continuous functions in hybrid space, led this study to provide a new method based on using this network in which salinity map determines by a multilayer perceptron network.

MODIS LST high spatial and temporal resolution data can provide higher quality information about temperature variation in different regions of Iran. LST cluster analysis calculated to detect LST zones and LST spatiotemporal variation in every zone. Time consistent LST has been product from MODIS Terra and Aqua LSTs in any pixels of time series. Monthly long term mean temperature calculated from Time consistent LST and makes an array in 1765*2688*12 dimensions that is long term mean LST of Iran. Monthly data clustered using ward method. Tow temperature zone (warm & cold) separated in first step and each of them clustered to tow new sub clusters. The resulted zones include hot, warm, temperate, and cold. Four temperature zones is consistent with topographic and environmental characteristics of Iran. Temperate and cold zones located in higher areas in Zagros and Alborz mountains, while low altitudes in southern latitudes and deserts are the warm and hot zones. LST maximum values in warm and temperate zones are near to the values in hot zone. With regard to area of warm zone that cover the third of Iran area, it can be concluded that temperature increase in the large parts of the country has the ability to become hot areas.

This study was performed to evaluate the extent of leaf area in Iran from (2002) to (2016) using Remote sensing. For this purpose, we extracted data collection and leaf area index for the Iranian territory from MODIS website. The database was established with programming in MATLAB software to perform mathematical and Statistical calculations repeated. After the analysis of the data in this software a monthly average long-term map was developed. The maps show that the central, East and South-East are almost empty of leaf area or seen very sparse in some areas. In contrast areas of leaves in the northern and western parts of Iran, are good, which generally includes fields, except forest Arasbaran and Hirkany. Precipitation and the temperature, is the main factors for the growth and development of plants, that these two conditions are enumerated in the west due to being on the way of westerly winds. Lowest leaf area index is for January and February and the highest average of leaf area is for May and June. Next, study of 15 years of leaf area index data by cluster analysis based on the calculation of Euclidean distance and Ward method, showed that all 12 months fit in the two main groups and, in fact, divided for two periods of strong and weak vegetation. In this analysis, , April during the cold period and October in the warm period of the year as the transition months and they are located on a separate cluster.

Due to the importance of soil moisture in plants growth and biology interactions, it is considered as a key index of agricultural drought monitoring. In this research, to evaluate soil moisture deviation as an indicator of agricultural drought, a Soil Wetness Deficit Index (SWDI) was developed using satellite data of vegetation index (NDVI) and land surface temperature (LST). For this purposes, 8-day-products of land surface reflectance (MOD09Q1) and LST (MOD11A2) derived from MODIS satellite data over Esfahan in the period of 2000- 01 (dry year) and 2004-05 (wet year) 8-day time step were ordered and downloaded from internet. The SWDI in each time step was determined based on 8-day-Soil Wetness Index (SWI) derived from application of a triangle space concept between LST and NDVI data. Using satellite data of LST and vegetation index NDVI, amount of water stress during each time step was estimated from a linear relation. The new developed drought index mapped well the spatial distribution of dry periods and their intensity in the agronomy year of 2000-01. The cumulative number of dry days (SWDI<0) in the period of 2000-01 was estimated as 184 days. The results confirmed also the existence of wet periods in the year 2004-05. After comparison of SWDI variations in two areas of irrigated and dry farming, the promising results were obtained. SWDI index has the capability of drought monitoring in different time scales.

Introduction: Clouds and water vapor are important modulators of climate and are involved in feedbacks that strongly affect global circulation and energy balance. Typically، 50% of the earth surface is covered by clouds، at any given time. A cloud is defined as a visible mass of condensed water droplets or ice crystals suspended in the atmosphere above the earth surface. The cloud phenomenon plays an important role in the water cycle، radiation، temperature، precipitation، predictors of climate، and etc. As there are not adequate information on the spatial distribution of clouds in Iran، except for synoptic stations data، it is necessary to conduct researches on the physical properties of cloud by remote sensing data. Therefore، these kinds of data can provide us with a more accurate and comprehensive understanding of cloud phenomenon. This study examines the spatial distribution of cloud Fraction (cover) over Iran in 2007 using remotely sensed data. The results of this fundamental research could be applied in practical knowledge such as weather forecasting، climate modeling، seeding clouds، site selection of solar panels، and etc. Materials and methods: The aim of this research is to survey the spatial distribution of Cloud Fraction (CF) over Iran in annual and seasonal timescales. To do this، daily data of cloud product of MODIS Terra (MOD06_L2. A) over Iran was used in 2007. The data was obtained from the ftp linkftp: //ladsftp. nascom. nasa. gov/allData/6/MOD06_L2/. In this research، we have used spatial resolution of CF in 5 km×5 km scripting in MATLAB. In the first step، overlapping images have been removed and، then، the data within the Iranian border have been extracted from daily data. The data have been transferred into regular network of Iran for doing statistical computations. Results and discussion: The investigation on the annual mean percent of CF indicates that the value is 25. 3% for the morning and 29. 7%for afternoon. Thus، the amount of CF in the afternoon is increased compared to the morning times. In winter، the amount of CF for the morning is 48. 2% but for the afternoon it is reduced to 44. 1% and this reduction condition is not seen in other seasons. In spring، fall and winter seasons the amount of CF is increased in the afternoon compared to the morning. The spatial distribution of annual percent of CF indicates that the maximum is seen over the southern shores of the Caspian Sea and the minimum is observed in south east part of the country. The spatial distribution of seasonal percent of CF shows that the maximum amount of CF is over the highlands of Zagrous and Alborz mountains and the minimum is in south and south-east regions of the country. In this season، the maximum percent of CF is not seen over Caspian shores like other seasons. In spring، the maximum percent of CF can be seen over the southern shores of the Caspian Sea and parts of north-west but the minimum percent of CF can be observed over central areas of south and south-east regions of the country. In fall season، the maximum percent of CF is seen over the southern shores of the Caspian Sea and parts of north-east and the minimum is observed in south and south-east regions. In summer، the maximum percent of CF is in the southern shores of the Caspian Sea and the minimum can be seen over east and west parts of the country. In summer، the extent of minimum percent of CF is changed to other seasons and is far away from south-east regions. Conclusion: In this investigation، the CF parameter of MODIS Terra was applied in the daily temporal resolutions for the year 2007 to explore the spatial distribution of cloud cover over Iran. As the data did not have a regular geographical coordinated grid، a regular coordinate was initially constructed and CF data were trasnferred to this regular grid. This process was conducted to analyze the climatology of cloud cover. The results from MODIS Terra data for the pass of morning and afternoon times revealed that the maximum annual percent of CF is seen over the southern shores of the Caspian Sea and the minimum is occurred over south-east part of the country which is consistent with the results of Rasooli et al. (2013) and Masoodian and Kaviani (2008). For the seasonal time scales، the maximum percent of CF is occurred over the southern shores of the Caspian Sea for spring، fall and summer seasons but in winter it is seen over the elevations of Alborz and north-west parts of the country. The minimum percent of CF is seen in south-east and east parts of the country for spring، fall and winter seasons. In summer، it is observed over east and west regions where shows that the formation process of cloud is different in winter and summer compared to the other seasons. The validation of CF values in the annual time scale indicates that MODIS overestimes CF by 3% compared to the synoptic stations. This is acceptable when the results are compared with the findings of Bisoolli and Pahl (2001) in Germany with the erros of 6% and Kotarba (2009) in July and January months with the error of 4. 38% and 7. 28% for the year 2004. In general، the estimation of cloud cover is identical for the two data sets.

Snow is a huge water resource in most parts of the world. Snow water equivalent supplies 1/3 of the water requirement for farming and irrigation throughout the world. Water content estimation of a snow-cover or estimation of snowmelt runoff is necessary for Hydrologists. Several snowmelt-forecasting models have been suggested, most of which require continuous monitoring of snow-cover. Today monitoring snow-cover patches is done through satellites imagery and remote sensing methods. MODIS have smaller Spatial Resolution and more bands in comparison with Meteorology Satellite like NOAA. Therefore, in this research we used MODIS data for creating snow cover imagery. Existence of cloud in the study area is a major problem for snow cover monitoring. Therefore, in this research snow cover area changes were estimated without MODIS data period, but with DEM imagery and regressions between temperature, height and aspect. For this purpose, on 10 Esfand when the image was suitable we estimated the snow cover area. In comparison with real image, precision of the method was confirmed.

Dust event is one of the atmospheric events of the world arid and semi-arid areas that had a significant increase in recent years and negative effects in different parts. In this study used MODIS data to identify and select the best algorithm for dust detection. For this purpose, three dust events of South West of Iran detected in 2012 using five different algorithms of dust detection including Ackerman BTD, Miller, dust index, TIIDI and DUST RGB methods, and methods compared. Studies show that methods of Ackerman BTD, Dust index, and Miller need to threshold regulation for each dust event; for this reason, the suitable threshold was determined for each dust event using histogram method and dust identified. In addition, TIIDI method could separate dust phenomenon from other complications on the surface of the earth but as well could not identify dust on water. In DUST RGB method as well dust identified from other complication. In addition results of images classification and accuracy assessment showed that in all three dust events, DUST RGB method has maximum total accuracy among of other methods. Therefore, based on the results of matrix error and accuracy assessment, DUST RGB method was chosen as the best algorithm for dust detection.