جستجوی مقالات مرتبط با کلیدواژه « رطوبت خاک » در نشریات گروه « جغرافیا »

Dust events are one of the most important and challenging environmental and climatic hazards of the whole country and especially of Khuzestan province. The main goal of this research is to reveal the role of changes in vegetation cover and soil moisture in the annual distribution of dust events in Khuzestan province. In this regard, 3 categories of data were used. The data related to station dust codes obtained during the statistical period of 2010-2020 for the synoptic stations of Khuzestan province on an hourly scale. The second set of monthly vegetation data was obtained from the NDVI product (MOD13C3) of the MODIS sensor, and finally, the moisture data of the upper 5 cm layer of the soil was provided from the SMAP sensor on a monthly basis. By analyzing the frequency of dust codes, the monthly frequency of dust events, the concentration and persistence of dust were investigated. By obtaining the spatial average values of the two moisture indices of the upper 5 cm soil layer and the monthly NDVI index, the correlation and relationship between the dust indices and these indices was analyzed. The results showed that based on the average period of 2010-2020, the period of 4 months from May to August is the dust peak period in the province. There are at least 11 days of dust in every month, and the concentration of dust is more than 2800 micrograms per cubic meter in the air, and the duration of the dust event in the province is at least 40 hours. The investigation of the moisture indicators of the upper layer of the soil and vegetation showed that, exactly in the province during the same dust peak period, the soil moisture of the upper layer of the soil reached less than 10% in a large part of the central and southern regions of the province, and at the same time, the vegetation index also In these sectors, it has reached less than 0.18. In addition, in this research, it was observed that the monthly changes in the moisture content of the upper 5 cm layer of the soil with a correlation coefficient of -0.88 have the greatest effect on controlling the dust concentration in Khuzestan province.

Soil moisture can be considered in the control of desertification, agricultural activities, watershed management and optimal management of water resources.   Since the country of Iran is facing many problems in these fields, the expansion of studies in the field of accurate estimation of soil moisture becomes important (Mehrabi et al., 2019). The GLDAS system may have a high error compared to the measured data in some areas. Therefore, it is necessary before the data and results of this product are used as a decision-making tool in the region.  The quality of these data should be evaluated locally using ground-measured data (Polo et al, 2016 & Sanchez-Lorenzo et al, 2013 & Zhang, 2019).In this study, Terra MODIS data was used to estimate soil moisture due to higher spatial resolution (1 km). Due to the fact that it is difficult to estimate the humidity time series in the field, and radar remote sensing methods produce humidity maps with low spatial resolution. Therefore, in this study, a new method was introduced to prepare a soil moisture map with higher spatial resolution based on NDVI and LST MODIS products.The purpose of this study is to estimate soil moisture in Jiroft city using the products of the Morris sensor and NDVI and LST indices. Considering that Jiroft plain is one of the agricultural poles of Iran. Estimating the time series of soil moisture and then providing a drought index based on soil moisture is a useful method for investigating agricultural drought in the study area.

LST and NDVI have high relative importance in arid regions (Park et al, 2016). In the study area, an algorithm based on remote sensing was used for soil moisture time series due to the lack of access to soil moisture time series. Moody's LST and NDVI products with a resolution of 1 square kilometer were used during 2007-2022. Then multiple linear regression was created using OLS method between soil moisture observations (2021 and 2022) and NDVI and LST time series data. Based on the results, both independent variables NDVI and LST were significant at the 99% level) p-value<0.01) .VIF values were less than 10. Therefore, there is no linearity between the independent variables. The standard error is small, which indicates that the estimated value is exactly the true value. The value of t statistic of two variables is higher than 2, which is considered statistically significant. In general, R2 = 0.78 and it shows the accuracy of soil moisture estimation method. R2 was obtained as 0.74 and 0.8 in the years 2021 and 2022, respectively, which indicates the accuracy of the predicted values.

Estimation of spatial and temporal changes of soil moisture is an important issue in low data areas such as Iran.  We proposed a multiple regression model based on Modis NDVI and LST to obtain surface soil moisture at a regional scale. The results showed that the multivariate linear regression method can be used to estimate soil moisture products with high resolution in areas with little data in the surface layers of the soil. Park et al. (2016) stated that LST and NDVI have high relative importance in arid regions. In the studied area, according to previous studies (Bai et al, 2020 & Wang et al, 2007), an algorithm based on remote sensing was used for soil moisture time series due to the lack of access to soil moisture time series became. Moody's LST and NDVI products with a resolution of 1 square kilometer were used during 2007-2022. Then multiple linear regression was created between soil moisture observations (2021 and 2022) and NDVI and LST time series data using OLS method.  Based on the results of simulated soil moisture changes and the OLS method in estimating soil moisture (0-30 cm), both independent variables NDVI and LST were significant at the 99% level (p-value <0.01)). VIF values are less than 10 Therefore, there is no linearity between the independent variables. The standard error is small, which indicates that the estimated value is exactly the true value. The value of t statistic of two variables is higher than 2, which is considered statistically significant. Overall, R2 was 0.7  And it showed the accuracy of the soil moisture estimation method, which is consistent with the results of (Khanmohammadi et al, 2008 & Lin et al, 2015).

Evapotranspiration is one of the crucial parts of the water cycle balance. In Iran, the total annual rainfall is estimated at 413 billion cubic meters. According to an analysis, 296 billion cubic meters, or 72% of this amount, became out of reach due to evapotranspiration. Accurate estimation of evapotranspiration plays a crucial role in studies on the issues such as global climate changes, environmental evolution, and control of water resources. Due to the limited number of meteorological stations and the high costs and time of collecting ground data, using remote sensing techniques and satellite images to have accurate and appropriate outputs can be a suitable tool to determine the actual evapotranspiration rate. One remote sensing algorithm for estmating evapotranspiration is the Surface Energy Balance (SEBAL).

The SEBAL is a model based on image processing that includes twenty-five models for calculating the evapotranspiration (ET) rate as the remainder of the Earth's surface energy balance. This model was introduced by Bastiansen in the Netherlands and also developed for the Idaho Highlands based on measured evapotranspiration at ground level. The SEBAL model uses digital image information captured by the Landsat satellite or other remote sensing sensors capable of recording thermal infrared and visible and near-infrared radiations. The ET value per pixel (e.g., in 30 by 30 square meters of TM and ETM Landsat images) is calculated for the specific moment at which the photo is taken.The ET value will equal the net radiation minus the heat entering the soil minus the heat entering the air. Further details of this model have been provided by Bastiansen et al., but the general equation used by the SEBAL is as follows:LE = Rn – H – GWhere LE is the latent heat flux (Wm-2), which can be easily converted to ET; Rn is the net solar radiation (Wm-2); H is the sensible heat flux (Wm-2), and G is the ground or soil heat flux (Wm-2). From this formula, the formula can be inferred that the radiation that reaches the Earth's surface from the atmosphere is separated into three parts: a part of the Earth or soil is heated, another part of it near the surface of the Earth is heated, and the rest of the remaining energy is evaporated. The SEBAL aims to calculate the latent heat flux (ET), considering the actual ET. It should be noted that the essential accuracy of the results is for the LE or ET. It is affected by the accuracy of the shortwave band as well as the thermal band of the satellite. In the following equation, the net radiation from the surface energy equilibrium equation is calculated as:Rn= (1-α) Rs + (Lin-Lout)Where a is the surface albedo; Rs is the solar radiation (Wm-2); e is the reflection of the Earth's surface (emission), and Lin-Lout is the radiations entering and leaving the Earth in the form of long waves. A value is obtained by mixing spectral reflections from six shortwave bands on the Landsat satellite. Lin-Lout is also considered a function of the surface temperature, which can be extracted from the satellite image. The value of e is obtained by plant indices created from two short-wavelength bands. The potential importance of Rs per pixel with a definite slope can be determined using the precise sky theory curves. The soil heat flux or G can be obtained empirically using Bastiansen's et al. (1998).

According to the results obtained from analyzing the data and output maps captured by the Landsat satellite, and considering the LST map, in which green color indicates a deficiency and red color represents very high, there was an oscillating trend from 2000 to 2008. Still, according to LST maps, since 2010, there has been a sharp incremental trend, the peak of which has been in 2020, and the LST has reached its highest level. Such a trend has also been seen concerning the soil temperature map of Mazandaran province. A remarkable point about the increase in soil temperature is that it was significant and instantaneous in the fifth month of 2010, and the soil surface temperature has increased, just as in the LST, since 2010. Regarding the NDVI map of Mazandaran province, significant and impressive changes have occurred since 2012, and this trend has risen from this year until 2020. According to the maps obtained from the soil moisture in the province, the data show that oscillating changes occurred until 2012, and since the fourth month of 2012, the region's soil moisture has also increased. All the factors mentioned have a direct relationship with evapotranspiration. According to the results obtained and the increasing trends, especially from 2010 to 2020, there is expected to be an increasing trend for evapotranspiration using the SEBAL algorithm. The primary outcome of this research, which studies the changes in the evapotranspiration rates in Mazandaran province, is that: as expected, due to the increase in all the factors affecting the evapotranspiration increase, the results show that since 2010 the evapotranspiration trend has dramatically increased; Of course, due to the geographical location and proximity to the Caspian Sea, the evapotranspiration has always been relatively high, but there have been significant changes and a sharp increase from 2010 to 2020.

Drought, as a climate threat, has a significant impact on the environment and, consequently, on humans and other living organisms. Therefore, monitoring and predicting this phenomenon is necessary. This study, to examine and evaluate the drought forecast in the west and north -west of Iran, including Hamedan, Kermanshah, Kurdistan, West Azerbaijan, East Azerbaijan, Ardabil, Zanjan, Qazvin, Ilam, Markazi, Gilan, and Lorestan, have been used multivariate standardized drought index (MSDI) and methods based on artificial intelligence. To predict the values of this index in the study area, monthly rainfall, and soil moisture, as the input, and the calculated amount of MSDI, as output, was applied. The grid data on precipitation and soil moisture for a period of 36 years (1980 -2016) were obtained from the MERRA database. The results of monthly drought analysis based on these data showed that the most severe drought in the study area occurred from March to October and the main focus of this phenomenon are Lorestan provinces, especially Ilam and Kermanshah. The findings were following seasonal and annual maps. According to the MSDI index classification, no severe drought was observed in the study area and the droughts were in the middle class. The results of artificial neural network modeling also showed that the use of artificial neural networks, in general, has an appropriate ability to simulate properly. Among the algorithms used to optimize the artificial neural network, the genetic algorithm has the best performance compared to other methods in predicting drought.

Soil surface moisture is a key variable to describe drought, water and energy exchanges between the earth and the air. Due to the instability of spatial and temporal conditions, the environmental parameters affecting it are highly variable. The current study aims to downscale and extract the soil moisture distribution map with high resolution and its spatial analysis in the west of Iran. An educational layer was created by using the post-scattering bands of VV and VH polarizations as well as the angle of collision band (𝜃) extracted from Sentinel 1 radar images and land use extracted from MODIS sensor. Long-term average moisture per pixel of GLDAS data was also used. The micro-scale backup machine vector algorithm and the high volume resolution soil moisture dispersion map were estimated between 0.18 and 0.46 b. Field data collected from 38 sample farms of Kurdistan Agricultural Research Center were used to verify the output map, which was calculated to be R2 = 0.5012. The results were obtained for the elliptical direction of three times the standard spatial deviation of the northwest to the southeast, which shows that more than 99% of the moisture distribution is expanded in accordance with the spatial arrangement of the heights in this direction. Statistics of 0.3978 Moran index and P_Value value of 0.0000 showed spatial autocorrelation of soil moisture. The hot spot map also showed that the soil surface moisture is nuclear in the northwest and southeast directions and more at altitudes above 2000 meters. Hot spot analysis also reveal that the moisture has strongly clustered to the east and inside the country. Using the obtained spatial analysis results, low or high soil moisture areas can be identified in order to identify environmental potentials and improve the decision-making process, allocation and spatial distribution of services. 

  Soil moisture is a fundamental variable in water and climate cycles that plays an important role in our understanding of the interaction of the atmosphere and the earth's surface. In contrast to linear and nonlinear algorithms extracted from different bands of satellite images, machine support learning techniques have recently been introduced to improve low-resolution soil moisture data from various satellites. Extraction of soil moisture anomalies GLDAS data were scaled microscopically using Sentinel radar images, the results of which showed a correlation of 0.7 with ground data. The purpose of this study is to extract soil surface moisture with high spatial resolution by microscaling the soil moisture layer of the global system of data integration and sentinel radar images as a practical method in environmental studies and spatial analysis of moisture dispersion in western Iran during the study period.

The study area is between latitudes "36 '51 ° 31 to" 45 '49 ° 36 north to "18 '27 ° 45 to" 26 '04 ° 50 east with an area of ​​466.121 square kilometers. Western Iran generally has a mountainous climate. Among climatic variables, rain is considered as the most important climatic variable affecting soil moisture. Therefore, the water-rich year of 1997-98 was selected as the statistical period and western Iran for the study. In this research, first, the soil moisture layer with a spatial resolution of 0.25 degrees was extracted from the global land data integration system. In the next step, the middle of the radar images of Scintil 1 was extracted in a period of time to use the desired bands. The other two parameters, namely surface cover and vegetation, which were extracted from the images of Madis surveyed lands in effective microscaling. By combining the mentioned layers, an educational layer was obtained. Using the soil moisture layer backup (SVM) vector machine classification method, a small scale was obtained and a map with high resolution power was obtained. In this study, soil moisture microscaling based on the work of Pasoli (2015) and Jennifer (2016) with the functions available in the Google Earth Engine system was obtained. Field data collected from the Kurdistan Agricultural Research Center were used to validate the output. Spatial criterion deviation and Moran statistic were calculated to investigate the direction of scattering and spatial autocorrelation. Then Gates statistic was obtained to investigate severe and low clustering.

The soil moisture map of the global system showed the study range between 0.22 to 0.45 cubic meters per cubic meter. After executing the algorithm implemented in Google Earth system, the soil moisture layer engine with high spatial resolution between 0.22 to 0.45 cubic meters per cubic meter showed that by comparing the pixels of the high resolution layer with the real data, square root error and correlation coefficient 0.1641 and 0.5012 were obtained, respectively. Spatial standard deviation demonstrated the spatial distance of the moisture volume of each pixel from the mass moisture center in a northwest to southeast direction. The Gates statistic was calculated to show that hot spots were located in the same elliptical direction, ie northwest to southeast, and cold spots (low humidity clustering) were mostly studied in the southwest of the area. The intersection of severe clustering with the elevation layer indicates that the most intense clustering is located at an altitude of 2000 m and above and there is little clustering at low altitudes in the study area. The intersection of hotspots with the soil layer also indicated that the highest percentage of hotspots is in soils with Vertsul category. Simulated global system moisture data have been available since 1954, but the spatial resolution of this data is low. In this study, using high-resolution Sentinel 1 radar satellite imagery and the valuable algorithms available in Google Earth Engine, soil surface moisture was scaled and a high-resolution soil moisture map was extracted and then spatially analyzed. According to the correlation coefficient of R2 = 0.5012 with field data, it can be concluded that this method can be used to estimate soil moisture. The standard deviation ellipse for soil moisture is northwest to southeast, indicating that moisture expands in this direction due to the spatial arrangement of unevenness that causes rainfall to be diverted. The value of Moran and P. Valio index of 0.3978 showed the existence of spatial autocorrelation of soil moisture in the west of the country. The hot spot map also revealed that the surface moisture of the soil is nuclear in the northwest direction and to the southeast and most of the altitudes are above 2000 meters. The maximum nuclei are located in the form of three separate centers in the north of Marivan city, around Tuyserkan and south of Dorud city. Moisture clustering layer also intersected with soil layer, which was the highest percentage with 23% soil clustering in soils with Vertsols. These types of soils have expandable clay. The predominant type of clay is montmorilliant, which has increased water absorption in them.

The results of this study with respect to the correlation coefficient of 0.5012 with real data and high spatial resolution of the output map showed the efficiency of using different bands of radar images in estimating surface moisture. The spatial distribution of moisture with the ellipse indicated the standard deviation of the direction of northwest to southeast in accordance with the direction of roughness and spatial arrangement of the Zagros, which can explain the role of roughness in high rainfall in the west of the country. The spatial pattern also revealed the surface moisture of the soil with the Moran index of cluster moisture distribution and non-randomness. In future works, to increase the accuracy of the moisture map extracted from the algorithm implemented in this paper, infrared bands can be used to extract vegetation indices and increase the information of educational data to the algorithm. The results of this study also confirm that the algorithm used in this research can effectively lead to the extraction of the soil surface moisture layer with a higher resolution.

Soil moisture controls many processes in the climate and is considered as a very important variable in the ecosystem and a basic parameter of the environment. It directly influences the life of plants, animals and microorganisms and plays a major role in the energy exchange between air and soil. The purpose of this study is to estimate the surface moisture of soil using the temperature indicators of the earth surface and the inertia of apparent heat. Therefore, MODIS images were used in Mortazie area of Yazd province in two different time periods, one from Feb, 8, 2018 to Feb, 14, 2018 and the other in the summer from July, 4, 2018 to July, 9, 2018. Also, the temperature-vegetation drought index, mostly used in previous research on soil moisture was applied to evaluates results. The apparent thermal inertia was obtained from images of morning and evening modes from Terra and Aqua satellites. A linear relationship was established between the temperature-vegetation drought index and the apparent heat inertia on specific days. The RMSE and R2 were found to be 0.15 and 0.86 on July, 7, 2018 and 0.18 and 0.76 on Feb, 9, 2018 respectively. The overall results showed a linear and direct relationship between the apparent thermal inertia and the TVDI surface moisture index.

Dust phenomenon is one of the most common and destructive phenomena in the dry and desert regions of the world and Iran, which can have harmful effects on human life and the environment. Wind erosion and the removal and transport of soil particles by wind create dust. Controlling wind erosion will be effective when enough information is available on the factors affecting it. The results of the related studies show that wind erosion phenomenon is very complex and controls many factors. Soil characteristics influence the severity of wind erosion through its impact on its erosion. However, in most researches conducted on wind erosion throughout the world, the role of soil characteristics has been confirmed in wind erosion, but few studies have been conducted in Iran, and the Alborz province, which has been exposed to wind erosion and dust storms in recent years, has not been investigated in these studies. Considering the importance of the issue in this area, the present study was conducted to investigate the role and effect of soil characteristics on the wind erosion threshold velocity and the occurrence of dust phenomenon. In this study, the wind erosion threshold velocity was measured in normal conditions using desert wind tunnel device, and the physical, chemical and mechanical characteristics of the soil were also obtained after sampling in the laboratory and compared with the estimated threshold velocity. Also, using wind velocity data and velocity thresholds, wind rose and storm rose area of the area were drawn and examined. The characteristics of the soil samples examined with high-precision included limestone, sodium, phosphorus, calcium, magnesium, organic matter, nitrogen, humidity content, shear strength, compressive strength, EC, pH, and d.( In this study, using grading of the soil samples , a variable called d has been measured. This variable indicates the percentage of particles with diameters between 0.1 to 0.5 mm as the most sensitive particles for wind erosion in the collected samples.) The results of measuring the threshold velocity using a wind tunnel device showed that the minimum and maximum wind speed thresholds in the area were 7.5 m / s, and 9 m / s, respectively. The range of 7.5 to 9 m / s indicates the high sensitivity of the region to the wind erosion; this is while the width of these areas is also high and this could be a serious threat to the province of Alborz and its neighboring provinces. Studies of the wind rose and storm rose in the region showed that the dominant winds in the region are from the northwest. And more than half of the winds erupted in the area are erosive and have the ability to move particles of soil and create dust. The relationship between soil characteristics and threshold velocity was investigated using the stepwise regression test. The results showed that the most effective parameters on soil moisture threshold velocity included particle size, lime content and shear and pressure strength of the soil. That is, as the amount of lime in the soil increases, the adhesion of the particles and consequently the pressure and shear strength of the soil increase. Moreover, when the percentage of particles with a diameter of 0.1 to 0.5 mm in the soil increases, threshold velocity decreases. In this study, soil moisture has the highest effect on the threshold velocity and has a direct relation with the velocity of wind erosion. The percentage of sodium in the sampled soils was very high, and this caused the disintegration of soil particles and reduction in the wind erosion threshold velocity. The study showed that the risk of wind erosion and dust-fogging greatly threatens the Alborz province. The actual focal points for the production of dust constitute a vast area of the province. These areas, integrated into the central part of the Eshtehard plain and being bordered by the deserts of the Buinzahra plain in Qazvin province, have placed a wider field of dust in front of the wind and are regarded as a serious threat and would have harmful consequences in the provinces of Alborz, Tehran and other neighboring provinces. Therefore, it is necessary to take a special look at these areas in order to prevent the risks of this problem from being exacerbated in the first stage and to reduce the risks in the next stage.

Solar radiation is the main source of all energies on the Earth and is an important parameter in hydrology studies, water resource management, water balance equations, and plant growth simulation models. The most common instrument for recording global solar radiation data (GSR), is using pyranometer; however, because of the high costs of installation and maintenance, it is not possible to establish a radiation site for such purposes. In areas where ground measurements are not available, the Global Solar Radiation (GSR) can be estimated by empirical and semi-empirical models, satellite techniques, artificial intelligence models and other geostatistical approaches. In artificial intelligence models such as neural networks, various meteorological parameters like air temperature, relative humidity, sunshine hours, etc. are easily integrated to estimate global solar radiation. In most commonly used radiation models (e.g. Angstrom-based models) for estimating daily GSR, the sunshine hours and cloud cover are two important input parameters. Unfortunately, those parameters are not measured very accurately in weather site. Moreover, for time scales less than daily (e.g. hourly) using sunshine hour as an input, is not possible for predicting the sub-scale temporal GSR. The main purpose of this study, is comparing Multiple Linear Regression model and three types of artificial intelligence models (MLP, GRNN, ANFIS) against each other to estimate GSR in cold semi-arid climate of Hamedan, in order to present the most accurate model by including the soil data and ignoring the sunshine hours. Study Area: According to the Extended De-Martonne climate classification model, Hamedan is located in a semi-arid-very cold area and has a mean altitude of 1851 meters above sea level. In this study, GSR and meteorological variables (daily values of maximum air temperature, mean air temperature, minimum air temperature, air pressure, air relative humidity, soil temperature and rainfall) recorded at Bu-Ali Sina University weather site, located at latitude 34’48” and longitude 48’28”. These data were recorded every 10 minute during 31 Dec. 2016, to 10 Mar. 2018 by using an automated Spanish GEONICA Logger. Models: Multiple linear Regression (MR): This model is a simple and linear model that estimates the target variable by assigning a constant optimized coefficient for each input variable. Adaptive Neuro-Fuzzy Inference System (ANFIS): A multi-layered network model that uses advanced neural network learning algorithms and fuzzy logic, to describe the relationships between inputs and outputs. This model uses the neural network’s Learning ability and fuzzy rules, to define the relationships between input-output variables. Generalized Regression Neural Network (GRNN): Is a three-layered neural network, which the number of neurons in the first and last layers like other neural networks, is respectively equal to the input and output vectors. But, unlike other networks, the number of hidden layers of neurons in GRNN model is equal to the number of observational data. Evaluation criteria: To evaluate the models performances against actual field measurements, the Root Mean Square Error (RMSE) and Coefficient of Determination (R2) have been used. The correlations of models input variables (eight independent variables) versus GSR (dependent variable) were evaluated. Results revealed that maximum air temperature, average air temperature, relative humidity and soil temperature are respectively the most influencing inputs for modeling GSR, if using minimum numbers of meteorological parameters. Among them, maximum air temperature, minimum air temperature, atmospheric relative humidity and soil temperature, were selected as the best inputs, for modeling with least parameters. By using correlation test, as a 2-variables input matrix (relative humidity and soil temperature) 3-variables (mean air temperature, relative humidity and soil temperature) and the whole 4 parameters, were selected as 4-variables input matrix. The percentage of train and test data was 75% and 25% respectively. In this research, the models were run by using two different samples: Random and non-random samples. The results of the evaluations showed that random samples had higher accuracy in GSR estimates. In MR model, the 4-variables input, and in three artificial intelligence models (GRNN, ANFIS, MLP), 3-variables input showed the superior performances. Finally, the models were evaluated by using all of the eight inputs. At this stage, MLP with RMSE=3.04 Mj.m-2.day-1 and R2=86.33%, ANFIS with RMSE=3.26 Mj.m-2.day-1 and R2=84.43%, GRNN with RMSE=3.41 Mj.m-2.day-1 and R2=82.86%, and MR with RMSE=4.11 Mj.m-2.day-1 and R2=75.20%, provided the best GSR estimates respectively. The results showed that, in all numbers of input variables, random and non-random samples, artificial intelligence models present better performance than linear regression. By availability of the whole eight meteorological variables (daily values of maximum air temperature, mean air temperature, minimum air temperature, air pressure, air relative humidity, soil temperature and rainfall), MLP model can present the best GSR estimates. If all input parameters are not available, employing Generalized Regression Neural Network (GRNN) model and 3-variable inputs of mean air temperature, relative air humidity, and soil temperature is suggested for estimating the Global Solar Radiation (GSR) in cold semi-arid climate of Hamedan. It is noteworthy that in estimating GSR, two important parameters of sunshine hours and cloud cover were not used in our research. Testing the models performances in other climate types is suggested as future works.

 According to Cornelsen (2015), soil moisture is one of the most important variables in the hydrological cycle. In Manson's studies (2010), soil moisture was identified as one of the major climatic variables by the World Meteorological Organization, the Global Climate Observing System, and the Observational Satellite Observatory.  Remote sensing provides a powerful tool for detecting and monitoring soil moisture near the Earth's surface (0 to 5 cm). Also, according to Babaeian research (2015), optical reflection of the soil and thermal emission to Eliit (1979) and Microwave backed by Das researches (2008) is related on soil moisture. Remote sensing techniques based on microwave waves are effective techniques for estimating soil moisture. Surface water levels can be extracted using the NDVI index in Landsat images (Maryam Khosravian et al., 2012, p. 115), and user variations in time series can also be identified. (Malian et al., 1395, p. 49). Due to the limitation of access to radar information, the focus of the study is   on the near-visible infrared range and the amount of heat from the surface of the earth is measured from 3.5 to 14 micrometers (Curran, 1985). Soil moisture content with this method requires the estimation of soil surface temperature and vegetation index (Wang & Co, 2009). Vegetation and surface temperature have a complex dependence on soil moisture (Carlson, 1994). According to Gillies et al. (1997), the combination of these two indicators can be used to estimate soil moisture with an acceptable accuracy.In 2017, a model for estimating soil moisture using a visual distance assay was proposed based on the linear physical relationship between soil moisture and the short-range infrared reflection (STR), which is based on the distribution of pixels inside the surface temperature space and the normalized vegetation index (STR-VI) (Sadegi et al., 2017). A trapezoid or triangle model is one of the models used in remote sensing to estimate soil moisture. The study area is the Simineh River basin which is one of the sub basins of Lake Urmia Basin, with an of 3279 km2. The main data in this study are Landsat 8 satellite imagery. After applying atmospheric and radiometric corrections, the processing of images, between 2016-2017, was done according to the process of view of Figure 1.   Figure (1) Research process (Source: Writers) -Thermal-Optical Trapezoid Model (TOTRAM) This model is based on the distribution of pixels in the surface temperature and vegetation cover space that is fitted to estimate soil moisture using a linear equation in space (LST-VI) (Sadegi et al., 2017). Equation (1) -Optical Trapezoid Model (OPTRAM) The base of this model is the insertion of surface temperature to estimate the soil moisture in the visible wavelength range. In this physical model, the linear relationship between soil moisture and infrared reflection is expressed. According to the results of this study, the lowest average temperatures of satellite images were respectively -3.23 and 2.12 C in 2015 and 2016, indicating an increase in temperature. In 2017, the highest amount of vegetation density was 0.66. The correlation between the OPTRAM model in 2015 and the STR and NDVI variables, were positive and the correlation indices were respectively 0.709 and 1. These figures for STR and NDVI in 2016 were respectively -0.648 and 1, which indicated a negative correlation between STR and soil moisture; soil moisture decreased with increasing STR and increased with increasing NDVI. And the positive correlation between OPTRAM model and NDVI confirmed it. In 2017, the positive correlation between STR and NDVI with soil moisture were respectively 0.672 and 1. The TOTRAM model in 2015 had a negative correlation with the LST and NDVI indices and they were respectively -0.574 and -1. It indicated low accuracy of this model compared to the OPTRAM model in estimating soil moisture. In 2016, the correlation between LST and NDVI with soil moisture were respectively -0.974 and 0.409. They respectively reached -0.940 -0.787 in 2017. In this research, due to the limitations of the field information, soil moisture was extracted without the use of ground control points. The comparison of the accuracy of the two models in the region was investigated. The results indicated that soil moisture can be extracted from the STR index with high accuracy, compared to LST index, based on NDVI Triangular space. Due to the low cost and the availability of visible images, radar images were accurately obtained and the correlation between OPTRAM model and soil moisture estimation was confirmed. According to the extraction results, the OPTRAM model can estimate the soil moisture better than the TOTRAM model, due to the fact that it is not influenced by environmental factors and global parameters. According to research results, TOTRAM has two main constraints. First, it cannot be used for a satellite without thermal bonding. Secondly, in addition to soil moisture, the LST depends on environmental factors to be calibrated for each image. To overcome the limitations of the TOTRAM model as well as the empirical visibility of indicators, a new physical trapezoidal model, called OPTRAM, is proposed. It is based on the physical relationship developed between soil moisture and the "reflected infrared reflection" (Sadegi et al., 2015).

Droughts are long-term phenomena that affect vast areas, causing significant economic damages andlosses in human lives. Droughts are the most costly natural disaster in the world, and affect more people than any other natural disaster. Therefore, it is important to develop early warning systems to mitigate the effects of drought. The easiest way to monitor drought is to use drought indices that calculate drought severity, duration and actual range for each drought type. Several drought indices have been developed based on different variables and parametersto assess drought types. Soil moisture is a significant hydrological variable related to flood and drought and plays an important role in the process of converting precipitation into runoff andstorage of groundwater. Due to the difficulty, cost and time required for the field measurements of soil moisture, this parameter has not been widely used in drought indexes. Recent developments of global databases, based on satellite estimates, as well as rapid progress in hardware and software for modeling complex processes governing the water balance at the ground surface, have led to many efforts to deploy this new tool to reduce the limitations in this field. In this research, a new drought index based on soil moisture, derived from the land surface models of Global Land Data Assimilation System (GLDAS-SMDI) has been provided to monitor the evolution of drought severity.Thisindex is based on the fact that soil moisture is a determinant factor in most of complex environmental processes and has an important role in the occurrence of drought. The central Iran is located between 27N-37N latitudes and 48E-61E longitudes with an area of about 837,184 km2. There are 50 synoptic stations within the area. In the present study, soil moisture derived from Global Land Data Assimilation System using the GLDAS-SMDI index was used to prepare the spatial distribution map of drought in central Iran over the period of 2001-2004. The accuracy of the GLDAS-SMDI index based on satellite data was carried out using the evaluation criteria of R and RMSE compared with drought spatial distribution map derived from the SPI index based on monthly precipitationdata of 50 synoptic stations. In this study, the drought spatial distribution index of Soil Moisture based on the Global Land Data Assimilation System (GLDAS-SMDI) and SPI was obtained based on the monthly precipitation data from 50 synoptic stations over the period of 2001-2004. The results of the statistical criteria of the moisture drought spatial distribution mapcompatibility assessment based on GLDAS data with corresponding pixels on the drought spatial distribution map based on the precipitation data of thesynoptic stations showed that the drought severity map has had a high precision and good conformity with the land data (R=0.65, RMSE=0.22) based on GLDAS data.The highest correlation coefficient (0.74) was in 2004 and the lowest (0.45) in 2003.
The lowest and the highest mean errors in 2004 and 2001were 0.19 and 0.26, respectively,.The highest droughtseverity based on the GLDAS-SMDI index occurred in the Central Iran region at Iranshahr, Kahnuj, Bam, Baft and Birjandstationsduring the studied period. Droughts are hydro-meteorological anomalies characterized by prolonged shortage in regional water supply and can cause temporary difficulties (even failures) in water reservoirs. Today, most of the severe droughts are breaking out in terms of frequency, magnitude and duration due to constantly increasing water consumption, causing serious social, economic and environmental problems worldwide. Therefore, in order to deal with frequent droughts, great efforts have been made to estimate a more accurate assessment for better decision-making in order to prevent and mitigate drought losses. The most successful efforts among these methods might be the development and the use of various objective indices. In this research, the monthlymoisture data of the Global Land Data Assimilation System was evaluated to estimate the drought severity index based on soil moisture. The evaluation was performed using the coefficient of determination (R2) and Root Mean Square Error (RMSE). This analysis has demonstrated that the GLDAS products have very good compatibility with the land data over the selected area of Central Iran on monthly timescales and a 0.25° spatial scale. As a result, it can be said that the GLDAS data has a good potential for useful application of hydrological simulation and the calculation of water balance sheet, in the regions with low observations and low quality station. Therefore, it can be concluded that the soil moisture output of Global Land Data Assimilation System can be used for rapid and low cost estimation of drought severity based on soil moisture, which is a major factor in many complex environmental processes and has an important role in the occurrence ofdrought. In order to increase the spatial accuracy of drought intensity maps, it is recommended that the satellite data be combined with the values ​​of ground stations.

Soil moisture is a vital parameter in various land surface processes, and microwave remote sensing is widely used to estimate soil moisture. Hence Soil moisture retrieved from satellite microwave remote sensing normally has spatial resolution on the order of tens of kilometers, which are too coarse for many hydrological applications such as agriculture monitoring and drought prediction. Various downscaling methods have been proposed to enhance the spatial resolution of satellite soil moisture products. The aim of this study is to propose a statistical assimilative method to downscale European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative (ESA CCI) Microwave (MW) remote sensing soil moisture products. For this purpose, firstly we used the NOAA images and NDVI, LST and albedo indices in regression process to International Soil Moisture Network (ISMN) in-situ SM. Then we downscaled the ESA products by making proportion between results and ESA products. Because of some limitations, we operated on three study area, Kyeamba Creek catchment area in Australia and two areas in Parsabad in Iran. Validation results of each area were evaluated using ground data and results showed average R2 in 0.77, for Australia, and 0.59 and 0.34 for two case study in Iran. According to the results, it can be said that the proposed method, in addition to scalability and simplicity, has a higher productivity in uniform and unmixed areas such as Kyeamba Creek catchment, than agricultural land such as Parsabad.

1. Zagros forests (Quercus sp.) contribute to recharging groundwater, controlling air
pollution, ecotourism, by products, livestock grazing, sustaining agriculture, soil
conservation and flood control. Due to geological and topographical properties of Zagros
area, however, these forests are more vulnerable to deforestation agents mainly drought
driven die-off phenomenon. The effects of climate change are generally expected to
reduce the growth and survival of forests, which not only predisposes them to being
disturbed by insects and disease, but also increases the vulnerable ones to higher tree
mortality. As a consequence of continued climate change, forest mortality has spread
throughout Zagros areas inducing a severe environmental disaster. According to some
research, several biotic and abiotic factors, such as extreme weather conditions, drought,
storms, heat, and insect fluctuations, are responsible for oak reduction. Regarding rainfall
deficit, soil moisture storage and reduction in evapotranspiration should be considered as
the possible solutions through short term measures. Consequently, harvesting runoff by
micro-catchment construction such as crescent -shaped bund (CSB) is a possible adapted
measure in the semiarid regions for enhancement of soil moisture. In order to evaluate the effects of runoff harvesting on forest rehabilitation, the present research aimed to analyze the effects of CSB on soil soil moisture content.
2. Material and This study was conducted in Kalehzard site located in Kermanshah, west of Iran (UTM;
38S682887E, 3748385N). The respective annual precipitation and temperature are 440
mm and 15.2 ºC, indicating the semiarid region. The winter is so cold that the
temperature drops below zero on average for 90 days during December, January and
February. The summer is relatively hot and dry. A hill with a 15% slope and southeastern
aspect was chosen as the experimental site to represent the dieback phenomenonin Zagros forests. The experiment was a randomized complete block design with four
treatments and three replications. The experiment was conducted as a randomized
complete block design with four treatments including crescent shaped bund with
preservation (CSB), preservation treatment (PT), crescent shaped bund without
preservation (CSB-P) and control treatment (CT). CSB was constructed to assist
reduction in dieback rate and even re-vegetation of some dried forest trees through its
effects on harvesting runoff. This technique is perpendicularly in the slope direction
with the opening perpendicular to the flow of runoff, capturing runoff inside. The
frequency of both dieback and healthy trees was recorded twice a year, therefore, the
total number of trees was recorded before the construction of bunds within the plots
using 100% inventory method. Soil quality, soil moisture and plant canopy were
analyzed within treatments and repeated every year during the research period.
3. Results explored that the effects of CSB treatment on reduction of dieback rate and
re-growing of some dried trees was 36.7 and 19 tree ha-1 respectively (totally 55.7 ha-1
tree in the forest stand). Therefore, both the reduction of mortality rate and re-growing
of dried trees are two main positive effects demonstrated by CSB measure. PT
treatment has no effect on re-growing, while it contributes to the reduction of dieback
severity (37.7 tree ha-1). Finally, CSB-P was found the lower level on dieback
reduction was 6 tree ha-1, revealing the crucial importance of preservation for the
protection of built bund. As a result, micro-catchment and preservation have a
significant positive effect on forest restoration through soil moisture retention. also showed that the respective means SOC in the CSB, PT, CSP-P and CT
treatments were 2.35, 2.40, 1.90 and 1.80 %, indicating no significant difference among
them. However, the means significantly (p>0.05) increased in the CSB and P
treatments over time. CSB significantly reduced bulk density from 1.46 (1st yr) to
1.32 (3rd yr) enhancing soil moisture content. The crescent shaped bund was designed as the adaptive micro-catchment runoff harvesting system (MCRHS) and measured within
treatments plots. This technique has a lower soil and embankment movement than
earthy or stone dams which can be built perpendicularly in the flow of runoff. Besides,
it is arranged in staggered rows along the natural contour of the land with the open end
facing uphill. Consequently, these bunds slow down runoff enabling the harvested water
to be used in an effective way. This is particularly useful in increasing the soil moisture,
especially when precipitation is scarce.
4. It is concluded that CSB (first treatment) can be considered as the possible adaptation
approach for combating Zagros forest mortality induced by drought stress and climate
change. This technique is a possible measure for runoff harvesting and thereby
enhancement of soil moisture during dry season. However, proper and holistic
management of forests are needed to curtail forest dieback event. Furthermore, Zagros
forest soil should be protected from disturbance factors in terms of tillage practice, machinery traffic, grazing, logging and charcoal extraction. In our experience, runoff
harvesting through micro-catchment technique, forest preservation and sustaining SOC
are crucial short term measures for combating forest mortality in Zagros regions.

Soil moisture plays an important role in interactive processes between earth and atmosphere and global climate changes. In recent decades, there has been a great research interest to determine soil moisture from remote sensing methods. Triangular or trapezoidal methods are the most common remote sensing methods that apply the combination of thermal and optical satellite images to estimate soil moisture content. The accuracy of methods governed by the accuracy of saturated and dry edges that define from vegetation/ temperature scatter plot. A main limitation of these methods arose in some days or in some vegetation condition that dry and wet edges cannot be defined correctly. This concern is addressed in this paper by using the temperature and vegetation information during one year interval to form the temperature-vegetation scatter plot, saturated edge and dry edge exactly. The main contribution of the paper is, however, the introduction of co­-moisture lines in the one-year scatter plot. These lines are later applied to define the wet and dry edges of each individual day which are taken as the two closest co-moisture lines that contain all corresponding pixels of that day. The soil moisture index as a parameter dependent to evaporation efficiency is finally estimated from the slope and intercept of these two co-moisture lines. The proposed soil moisture index calculated from co-moisture was implemented and validated in Manitoba, Canada area while MODIS satellite images, taken in 28 cloudless days of year 2014, were used as the input data. The correlation between ground soil moisture data and proposed soil moisture index was estimated. Correlation of 0.92 was achieved for low vegetation days and lower in days with higher vegetation densities.

When aerial parts of the plants capable of storing absorbing mineral salts in their leaves fall، these dissolved materials are transferred to the soil surface، changing its physical and chemical properties. In this study، we have investigated the influence of the Haloxylon tree on the physical and chemical properties، and also humidity of the soil using two، 300 m long transects; one canopy of the trees (1. 5 m distance from the tree trunk) and another one used as the reference located at a distance five times as long as the tree’s height (almost 50 m away from the tree trunk). Thirty points were selected on each transect، and samples were taken from depths of 0-10، 10-20، and 20-30 cm of the soil. Measurements were taken with respect to Ca+، Mg+، K+، Na+، pH، EC، biomass and volume humidity.

Drought refers to random characteristic of natural phenomena, brought about by the irregular deficit or shortage of available water. It affects the plant growth and reduces its yield. To estimate the intensity and the frequency of droughts will help reduce the damaging effect of drought. In this study we have used the water-budget methods and Thornthwaite's aridity index and its standard deviation for Ramsar during1956-2008 to show the frequency and the intensity of drought effects in this place. Results showed that there was a normal monthly trend of the year. In this station, there is water deficit and the months that have water surplus. We found that this station every ten years will affect 3 or 4 times severe droughts and the profanity of drought is 0.4. For the year 2001 the rainfall was good but the drought effect was also severe and in the year 2002, the rainfall was less than the disastrous effect of drought.

Air temperature is one of the most important atmospheric parameters in energy supply of plants in different phonological stages. So soil temperature is significant chemical reactions of and soil nutrients translocation from root to growing and reproducting organ and consequently it results in more and better crop. In Meteorological Organization, air temperature continually measure and register in atmospheric, Synoptic and Climate Stations and in addition to experts read the parameters, register machines also register all the day long changing process on special papers. But soil depths temperature reads and takes note by observers in special times of morning, noon and evening in synoptic stations. The research goal was evaluating surface soil depth temperature in according to observing air temperature and soil moisture. To reach this goal, statistics about air temperature and soil moisture, 5 & 15 cm depth temperature and moisture are registered in 5 months period (March to July 2006). Statistics analysis was based on statistics standard methods and used Excel, Statgraph and SPSS software.