Dust storms are strongly and negatively associated with the annual cycle of rainfall and coincide with the west and southwesterly winds in west and south west of Iran. Accuracy assessment of particulate matter products of Moderate Resolution image spectroradiometer was studied in this research. Moderate Resolution image spectroradiometer products consist of aerosol optical thickness, its corresponding image red, green and blue and Moderate Resolution image spectroradiometer/ terra calibrated radiances 5 minutes L1B swath 1 km, which shows the environmental information at terrestrial, atmospheric and ocean phenomenology. Daily aerosol optical thickness data retrieved from Moderate Resolution image spectroradiometer from May 2009 to May 2010 were compared with the amount of particulate matter measured at ground in Sanandaj, Iran, using non-linear correlation coefficient.Results showed that the Moderate Resolution image spectroradiometer image/terra calibrated radiances 5 minutes L1B swath 1 km is able to detect dust storms distribution and their blowing direction over study area clearly. The air quality conditions obtained in with dust storm period were unhealthy and correlation coefficients between Moderate Resolution image spectroradiometer aerosol optical thickness and particulate matter concentration in this period were higher than without dust storm period. The Moderate Resolution image spectroradiometer aerosol optical thickness values lower than 0.1 were acquired uncertainty level. Comparison of Moderate Resolution image spectroradiometer images/ terra calibrated radiances 5 minutes L1B swath 1 km and image red, green and blue showed that Moderate Resolution image spectroradiometer has limitation in retrieval of aerosol optical thickness from the dust storm with high concentration of particulate matter. This study reveals that the algorithm which is applied to refine the aerosol optical thickness is not able to recognize the amount of particulate matter in low and very high concentrations sensitively. No study has previously been conducted to investigate the accuracy of the Moderate Resolution image spectroradiometer particulate matter products.

Cloudiness is of particular importance among other climatic elements and is one of the important issues in predicting climate change on a global and regional scale. The purpose of this study is to investigate the spatial distribution and estimate the long-term average of cloudiness on a seasonal and monthly time scale in the geographical area of Iran's atmosphere. Therefore, Multi-angle Imaging SpectroRadiometer (MISR) products were used during the years 2001-2019. The cloud products used were extracted with monthly temporal Resolution and spatial Resolution of 0. 5° x 0. 5° and after quality control and preprocessing, were used to build network layers. Cloud cover data from 44 synoptic meteorological stations were used to verify the accuracy of the cloud data of the MISR sensor. Based on the results, the average percentage of cloudiness in Iran's atmosphere is about 25%, which is low compared to the global average cloudiness (50%). In the long-term study, the maximum cloudiness was estimated on the southern and western coasts of the Caspian Sea and the highlands of Azerbaijan, Zagros and Khorasan ranked next. On the other hand, the lowest amount of cloudiness was observed in a wide area of central, eastern and southeastern Iran. Among the seasons, the highest and the lowest cloud fraction was estimated in winter and in summer, respectively. On a monthly time scale, it was found that the highest/lowest amount of cloud fraction is related to February/September. These differences indicate changes in the weather during different months of the year. Also the decreasing trend of cloud fraction during the study period is important in terms of global warming and climate change.

Soil moisture in the root zone is defined as soil moisture in depths of 1 to 2 meter from surface. This moisture is generally available for crop development and can be transported to the atmosphere through evaporation process. Due to the importance of soil moisture for plants growth and in the biology interactions, it is considered as a key factor for agriculture sector. In this research, to evaluate soil moisture routing, Soil Wetness Index (SWI) was derived from reflective and thermal satellite data. 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 used. Trend of soil moisture variations was evaluated using statistical methods such as Mann- Kendall, linear Regression and Wald- Wolfowitz in the 5 percent confident level. The results indicated that in the more than 40 percent of study area, there are no considerable variations in amount of soil moisture. The results showed also that Wald-Wolfowitz is not a suitable method for soil moisture routing in our area. The results indicated that in the western and central parts of study area, number of points with negative amount of moisture trend is increasing (from about 23% in dry year to 53% in wet year). It means that a long term trend of moisture declination and probably drought has occurred in the region. The points with positive trend in amount of soil moisture are limited only in the low eastern parts, Zayande roud river side and Gawkhoni swamp.

This Paper Investigates and estimates the variations of sea surface salinity (SSS) in the Persian Gulf using Moderate Resolution Imaging Spectro-radiometer (MODIS) data on the Aqua satellite and the advanced microwave sounding Unit-B (AMSU-B) sensor on the NOAA-16 satellite in order to use remote sensing science for a more efficient and with more time distribution of salinity in the Persian Gulf. In this research a multiple-linear regression model in R software was developed using the data of MODIS and AMSU-B sensors. The data were obtained during a period of one year and fed to the R software. After processing the data in the R software, the correlation coefficient (R2) for salinity was calculated between field data and MODIS and AMSU-B sensors data. The correlation coefficients for MODIS and AMSU-B sensors were 0. 86 and 0. 85, respectively. Also, root mean square error (RMSE) between satellite data and in Situ data for salinity using MODIS and AMSU-B sensors were 0. 62 Psu and 0. 07 Psu respectively. The results show the accuracy of sensors for determining the sea surface salinity pattern in this study.

While temperature is an important climatic variable in the majority of the fields such as hydrological and climatological modelling, spatial and temporal separation of this variable is a weakness all over the world which makes challenges in its usages. Lots of studies have been conducted to solve this problem; using Temperature Laps Rate (TLR) is one popular way to handle this challenge. Although TLR is an effective tool to interpolate temperature,an insufficient number of stations or inefficient spatial distribution of the stations could make calculated TLRs very uncertain. To cope with this discontinuity in temperature, satellite-sensed temperature data have been utilized. In comparison to station-based temperature, satellite-sensed temperature data is a well choice to map the temporal and spatial pattern of temperature in a wide area. With recent developments in Moderate-Resolution Imaging Spectroradiometer (MODIS), Land Surface Temperature (LST) data has been successfully employed in several areas such as earth surface radiation, evaporation, urban heat islands, climate change, hydrological modeling, sea surface and air temperature estimation. Iran is located in the arid and semi-arid region that has been always faces with water shortages. This has been worthen with global warming which has caused increases in water demand, too. Thus, having temperature data with good spatial Resolution has been always a need and challenge in the area and lots of the fields. In this study, an approach was introduced to estimate TLR utilizing MODIS LST with good spatial Resolution. These estimated TLRs were then used to downscale ERA5, CFS, and MERRA2 daily reanalysis temperature data sets to 1 km spatial Resolution. The downscaled data was compared with the recorded data at the stations in different climate regions and elevation clusters. The results showed that improvements resulted in all climate regions and elevation levels. On average 15, 18, and 4 percent improvements were seen in RMSE, MAE, and NSE, respectively.

Ocean color satellite sensors provide the only long-term Essential Climate Variable (ECV) globally that targets Chlorophyll-a concentrations (Chl-a) as the most important biological factor in the oceans. It is difficult to develop the long-term and consistent ocean color time-series for climate studies due to the differences in characteristics, atmospheric correction, Chl-a retrieval algorithms, and limited lifespans of individual satellite sensors. Therefore, the merged multi-sensor ocean color datasets were developed by merging data from different satellite sensor products. The performance of the commonly used single-sensor and multi-sensor merged ocean color datasets is a challenging issue over highly turbid coastal waters and dusty atmospheric conditions. In this study, we compared the common single-sensor [Sea-viewing Wide Field-ofview sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), Visible Imager Radiometer (VIIRS), and Sentinel-3 Ocean and Land Colour Instrument (OLCI)], and merged multi-sensor [Ocean Colour Climate Change Initiative (OC-CCI), and GlobColour weighted average (GC-AVW) and Garver-SiegelMaritorena (GC-GSM)] Chl-a datasets over the Persian Gulf, known as optically complex and highly turbid water bodies in a dusty atmospheric condition. The results indicate that the OC-CCI dataset provides more spatial and temporal coverages than the other datasets. Temporal consistency between single-sensor and merged datasets was made in two different timespans during the common period of sensors and during the continuous lifespan intersection between individual two-paired of datasets. The statistical metrics were calculated to show the temporal consistency between Chl-a datasets during the common and continuous time periods. Correlation between OC-CCI and the other datasets showed that the relationships between datasets did not change significantly during the proposed time periods. Further, it was indicated that the OC-CCI product is more constant than the other single-sensor and merged products. It was shown that OC-CCI datasets were more consistent with MERIS and GC-GSM datasets, and SeaWiFS and GC-AVW were not significantly correlated to the other datasets. The results revealed that the single sensor products that use POLYMER atmospheric correction algorithm (e. g. MERIS), and merged multi-sensor product that performs the GSM blending algorithms (e. g. GC-GSM) are more consistent and stable than the other products over the study area.

Drought is a major problematic phenomenon mostly for semi-arid areas of Iran.During drought periods, reduction in vegetation levels causes such problems as soil erosion, surface runoff, flood risk, etc. Therefore, the assessment of drought effects on plant covers is the most important issue. This research was conducted in 2015 using the extracted vegetation indices from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor data during 2000-2008. In Esfahan province, monthly rainfall data from 25 stations were used to derive the Standardized Precipitation Index (SPI) at 3 month scale, March to September. SPI was used to validate three index results in drought estimation.The result of calculating SPI showed that droughts occurred in 2000, 2001 and 2008. The result of Pearson correlations between SPI and Vegetation Indices showed that the highest correlation was related to VCI index and the lowest correlation was related to TCI index.The result of NDVI index in 2000, 2001 and 2008 indicated that the poor vegetation cover was increasingly occurred. Based on the results of this study, it can be concluded that the NDVI and VCI indices concerning MODIS sensor can be a good alternative for estimating the drought with respect to meteorological indices and consequently can give a better idea on drought conditions in the study area. It was shown that remote sensing data can be practically useful in analyzing the drought events in Esfahan province.

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

Presently, the conventional methods in, Direction- Finding (DF) are substituted by new array process-finding algorithms. The new ones are applied on many applications such as, DF, RADAR, SONAR and atc. Although these are able to remove the failures of conventional algorithms, their abilities are affected by some factors such as, noise, unknown parameters of received signals, coupling effects of array sensors and many others. On the other hand, Cramer-Rao Bound (CRB) is the minimum obtainable variance of the detection or Resolution error. In this paper, it is intended we are intending to extract the positions of sensors from the CRB so that it goes to minimum value. Also, the results are applied on some arrays with different shapes (sensor positions).