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

The aim of this study was to evaluate the effectiveness of estimated soil moisture data obtained from the GLDAS, ESA and SMAP sensor databases with the observed data of the Silakhor Agricultural Meteorological Station to investigate the spatial and temporal variation of soil moisture in Lorestan province. The data used in this research include the soil moisture data of the Silakhor station, GLDAS database, ESA center and SMAP sensor products during a six-year period (2016-2021). Estimated soil moisture data were evaluated against observed data using R2, RMSE and MAD statistics. The results showed that the SMAP satellite is associated with underestimation and the GLDAS model and the ESA satellite are associated with overestimation of soil moisture. However, in general, the estimated soil moisture values of the three mentioned sources have good accuracy. The value of the correlation coefficient between observed soil moisture data with soil moisture data obtained from SMAP and ESA satellites and GLDAS model was obtained as 0.62, 0.59 and 0.72 respectively, and in the combined case (SMAP, ESA and GLDAS) the value of correlation coefficient was increased to 0.77, therefore, it is suggested to use combine data to use soil moisture estimation.

Drought is a natural phenomenon that is mainly caused by less than normal precipitation over a long period that occurs in almost all climates. Since the late 1970s, the occurrence of drought has increased globally due to increased evaporation (caused by global warming). This phenomenon occurs slowly and can be short-term or terminated within a few months or continue for several years. drought has increased significantly in Iran in recent decades. The increase in droughts has many negative consequences in different areas of agriculture, water resources, and wildfire. The purpose of this study is to investigate the drought situation in the vegetation areas of Iran using the Keetch-Byram drought index (KBDI). For this purpose, the KBDI drought index derived from the data set of the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth edition (ERA5) with a horizontal resolution of 0.25o during the period of 1981-2020 has been used. Also, the combined product of surface soil moisture (SSM) from microwave sensors AMI-WS (ERS-1 and ERS-2 satellites) and ASCAT (MetOpA-B satellite) with a horizontal resolution of 0.25 o to evaluate the KBDI drought index results as well as the moisture condition. The results showed that the khalij-Omani and Irani-Turani vegetation areas have very low soil moisture and high drought in Iran. The amount of soil moisture in these areas is low and the area-averaged is equal to 12.65% and 18.42%, respectively, which has been somewhat constant in all months, in other words, dryness is the predominant climate feature of these areas. The monthly analysis of the KBDI index shows the spread of drought severity towards the west, i.e., the vegetation area of Zagros, in the hot months of the year. A decrease in soil moisture and dryness can be observed in a major part of Iran from early spring to mid-autumn. Also, in the Zagros region, from mid-summer to mid-autumn, increasing values of the drought index and decreasing soil moisture can be observed. The minimum drought index is in the vegetation areas of Hyrkani and Arsbaran. The correspondence between the results of the surface soil moisture (SSM) of the satellite product and the KBDI drought index obtained from ECMWF-ERA5 shows the appropriate efficiency of the KBDI drought index in identifying drought centers in Iran and investigating the spatio-temporal patterns of drought occurrence. frequent low-intensity drought may favor more drought-tolerant species and adapt forests and grasslands to future conditions without the need for management measures.

spatial modeling is one of the method for understanding and predicting environmental variables .soil surface moisture is a key variable for drought description , water and energy exchanges between the earth and the earth .soil moisture can affect many environmental phenomena such as runoff , soil erosion and crop production , but because of the lack of spatial and temporal conditions of soil parameters , soil moisture is highly changeable .the purpose of this paper is to evaluate the overall regression model and geographically weighted regression in spatial modeling of soil moisture in Fars province . soil moisture distribution as dependent variable and precipitation, snow equivalent water, vegetation index and topographic wetness index were selected as independent variables and then, using the general regression model and geographically weighted regression is used to model the spatial modeling .based on the evaluation criteria, the results showed the GWR model has better explanatory power with the R2=0/71 and a better estimate than the overall regression model with the R2=0/66. The spatial factors of precipitation and topographic wetness have the most positive effect and evapotranspiration had negative effect on soil moisture in the study area.The spatial factors of precipitation and topographic wetness have the most positive effect and evapotranspiration had negative effect on soil moisture in the study area.

Khuzestan province is affected by dust storms. The purpose of this study is to identify ‎the most significant climate and surface factors in the emission and transfer of dust in this province. By means ‎of remote sensing data including MERRA-2, GLDAS as well as Aqua, the ‎monthly average mass concentration of surface dust, air column dust and climate and surface ‎variables from 2011 to 2019 have been prepared and analyzed. The result revealed that the ‎region with an average vegetation cover index of 0.15 is susceptible to wind erosion and local dust. Also the ‎result demonstrated that the dust surface concentration and the dust column density declines from the ‎southwest to the northeast of the region due to the increased distance from the cross-border dust sources and ‎also the mountainous structure of the northeast. Analyzing of the annual distribution, showed a negative ‎relationship between the dust surface mass concentration and vegetation, and also positive/negative relationship ‎between dust column density and vegetation/ precipitation. In the monthly distribution of dust ‎ surface concentration and wind speed a positive correlation of 0.84 and 0.96 obtained, respectively. while ‎negative correlation values of -0.84, -0.81, and -0.54 were found Respectively. Therefore, dust surface concentration has the highest correlation with climate factors, which indicate the impact of cross-border ‎dust. Based on the results obtained, the annual distribution, showed a negative relationship with the dust surface concentration and vegetation. In addition, the relationship between dust column density and ‎vegetation and precipitation showed negative and positive values, respectively.‎

Despite the important role of soil surface moisture in the management of water and soil resources, unfortunately in Iran, there is no national network for monitoring soil moisture and the spatial distribution of measuring stations is limited. Therefore, in the present study, an attempt was made to extract and analyze the spatial and temporal distribution maps of soil moisture from SMAP products during the period 2015 to 2021 in Iran. The results showed that the average 7-year soil moisture at a depth of 5 cm in Iran varies between 0.005 to 0.5388 m3/m3, and the temporal average of soil moisture is 0.137 m3/m3. Evaluation of monthly average soil moisture maps by SMAP showed that the highest and lowest long-term average soil moisture in February and August occurred at 0.192 and 0.079 m3/m3, respectively. The spatial assessment also shows that the highest average annual soil moisture values are concentrated in the northern and western regions, and the lowest values are concentrated in the central and southeastern regions. Evaluation of the results based on the provincial scale shows the highest average annual soil moisture in the provinces of Gilan, Mazandaran, Kohgiluyeh and Boyer-Ahmad, and Alborz, respectively 0.294, 0.243, 0.216, and 0.209 m3 / m3. While the lowest values in Sistan and Baluchestan, South Khorasan, and Yazd provinces are 0.076, 0.091, and 0.096 m3 / m3, respectively. The results of this study can help the management of soil and water resources by knowing the amount of soil moisture in different areas.

The current study deals with the monitoring and evaluation of soil moisture and vegetation changes using remote sensing technique and its relationship with the standardized precipitation-evapotranspiration index in Bandar-Dij and Kol-Mehran watersheds in Hormozgan province. For this purpose, soil moisture data at a depth of 0-10 cm was calculated from GLDAS satellite images and standardized soil moisture drought index (SMDI) and standardized normalized difference vegetation index (SNDVI) were used to calculate changes in vegetation cover. The results of the correlation between SPEI and Standardized Moisture Drought Index (SMDI) showed that in all sub-basins, the correlation coefficients increase with the increase of the time scale, and the highest correlation is related to the Minab sub-basin (R=0.764) in the 12-month time scale. Examining the characteristics of drought (severity-duration and magnitude) showed that SPEI showed more intensities in shorter periods of time than SMDI, so it shows more magnitudes of drought. The investigation of the standardized vegetation cover index showed that in most of the sub-basins, since 2012, the decreasing trend of vegetation cover is evident. Investigating the relationship between SPEI and SMDI and SNDVI showed that SNDVI has a high correlation with SPEI and SMDI in a long-term time scale. A comparison of historical droughts between the three indices showed that, although all three indices often showed drought conditions, SPEI always showed extreme conditions compared to changes in soil moisture and vegetation, indicating a delayed response to changes in soil moisture. and vegetation to meteorological drought.

The aim of this study was to investigate the effect of different levels of drip irrigation treatment on yield, yield components and quality of green tea leaves during drought. This research was conducted for the first time during 2017 and 2018 in the tea garden in Bazkiagorab area of ​​Lahijan in Gilan province. The experiment was performed as split plots in a randomized complete block design with three replications. Irrigation treatment considered was consisted of five levels of 0, 25, 50, 75 and 100% of irrigation needs. Drip irrigation program in the period of water stress was carried out twice a week based on monitoring soil moisture at the depth of root development. Full irrigation of 2225 and 1610 m3/ha in two successive years of experiment caused the production of harvestable shoots in summer to increase by 162 and 153%, respectively, compared with the area without irrigation. Increased water stress at incomplete irrigation levels led to an increase in solids (woodiness) of tea shoots. Conversely, as the amount of irrigation water in the drip irrigation system increased, the weight of the shoots increased. The biennial average of the highest yield of green tea leaves was equal to 9125 kg/ha with 100% irrigation requirement and the lowest yield of 3468 kg/ha was obtained from the area without irrigation. Improvement of tea quality characteristics such as total polyphenols, caffeine, total ash and nitrogen percentage of shoots was also observed by complete irrigation in the drip system.

Drought has a huge impact on crop production. Therefore, a better understanding of drought and other atmospheric phenomena is important in order to reduce the damage caused by them. In Iran, most of the studies related to agricultural drought is carried out using remote sensing data due to the lack of in-situ soil moisture data. In fact, because of shortage of soil moisture data alongside a reasonable spatial distribution across the country, the researches pertaining to agricultural drought have been conducted less comparing to meteorological and hydrological drought. However, a few attempts were made to monitor agricultural drought by some researchers in Iran. This study aimed to monitor soil moisture at different depths using in-situ together with remotely-sensed data in Neyshabur City. In addition, the impact of soil moisture on rain-fed wheat yield in different crop growth stages was investigated.
Neyshabur city with an area of 869843.23 ha is extended from center to northwest of Khorasan Razavi province. It is in the neighborhood of Torbat Heydariyeh and Kashmar counties from the south, Sabzevar from the west, Faroj and Quchan from the north and Mashhad and Chenaran from the east. Its’ geographical location ranges from 35° 30 ʹ to 36° 59ʹ N latitudes and 57° 40ʹ to 59° 30 ʹ E longitudes. Farmers cultivate rain-fed wheat in early November and harvest it in late June. The observed data of rain-fed wheat growth stages from Neyishabur agrometeorological research station of 2010-2011 to 2014-2014 were used. The start and end dates of wheat growth stages of Sardari cultivar, together with measured biometric variables were used for further statistical analysis.
In addition, measured soil moisture was used at different depths of 10, 20, 30, 40, 50 and 70 cm, in a weekly base during 2010-2011 to 2013-2014. To investigate the impact of meteorological variables on wheat yield, the drought indices, including Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were calculated. Moreover, remotely-sensed based indices, including Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) were derived from different bandwidths of TM, ETM+ and OLI sensors of Landsat satellite. Statistical analysis was performed using Pearson correlation coefficient to find out the correlation among variables. Besides, the simple regression analysis was carried out to calculate the relationship between soil moisture at different depths and meteorological and remote sensing indices. The impact of soil moisture on rain-fed wheat yield was investigated using statistical regression analysis. The statistical analysis was carried out for three wheat growth stages including primary stage (from germination to four leaf), intermediate stage (from tillering to stem elongation) and final growth stage (from booting to physiological maturity).
The results revealed that the maximum and minimum NDVI values during December (germination and four leaf growth stages) were 0.769 and -0.355 in 2011-12 and 2010-11, respectively. It is interesting that wheat yield was 1645 and 3845 kg/ha in 2010-11 and 2011-12, respectively, which correspond to mentioned NDVI values. In addition, the maximum and minimum range of NDVI variations for whole growing season were for 2013-14 and 2011-12 with maximum and minimum wheat yield, respectively (3845 and 1645 kg/ha). Therefore, it could be partly due to appropriate temporal distribution of precipitation and partly because of adequate soil moisture at crop root zone at germination and four leaf growth stages.
The statistical regression analysis showed that soil moisture in 10 cm depth had a significant positive correlation with NDVImax and SPEI, with correlation coefficients of 0.595 and 0.780, respectively. In other words, 35 and 61 percent of variations of 10 cm soil moisture depth were accounted for NDVImax and SPEI, respectively. The results showed that, the relationship between rain-fed wheat yield and soil moisture depths was non-significant at different crop growth stages. Although, it should be noted that the April precipitation was important for soil moisture variations and consequently for rain-fed wheat yield. It coincided with start of booting stage of crop.
The results of this study revealed that NDVImax and SPEI had the most significant correlations with soil moisture at 10 cm depth. However, no significant relationships were found among soil moisture at 20, 30, 40, 50, and 70 cm depths with meteorological and remote sensing indices.

In order to increase water productivity at farm level and smart irrigation, appropriate systems are essential so that can measure the moisture content in a proper and acceptable accuracy. In this regard, the wetting front device was made at the Soil and Water Research Institute and was evaluated in 3 textures: light, medium, and heavy at 3 levels of water salinity: no salinity (S0), 5(S1), and 10 (S2) dS/m. Then, to evaluate and measure the device in the mentioned conditions, it was tried to simultaneously measure the device numbers and soil sampling at certain depths at intervals of 24 h. After collecting data on the time of arrival of the moisture front to the soil depth and changes in soil moisture, statistical analysis was performed by soil sampling and soil detection device. The results showed that the device reacts in different textures and salinities. Moreover, the sensitivity of its sensors to sudden changes in soil moisture due to the arrival of the moisture front to a certain depth of soil has acceptable accuracy. Statistical results showed that the device has about 6 to 9% normal error in determining soil moisture in non-saline conditions, 28 to 41% in terms of using water with salinity of 5 dS/m, and 31-37% when a water with salinity of 10 dS/m was used. The model efficiency index also showed that the device is very useful in non-saline conditions with an average efficiency of 0.75 and is not recommended in saline conditions with negative efficiency index.

All objects whose temperature exceeds absolute zero (-273°C) can emit energy. The amount of energy emitted from the objects depends on their temperature and can be measured according to Stephan-Boltzmann's law. The maximum emission of this energy is at a certain wavelength defined by Planck's law. Regarding the surface temperature of the sun, it emits maximum energy at a wavelength of 0.48 microns, in the middle of visible waves, while the Earth emits its maximum energy at 10 microns (infrared) wavelengths. This radiation which starts from 3 microns and continues to 100 microns (infrared), is known as Outgoing Long Radiation (OLR). Measuring this radiation is very important for understanding the energy balance and the temperature of the Earth. Because of the difficulties in measuring this radiation, the use of remote sensing data can effectively help in understanding the tempo-spatial variations of OLR. The purpose of this study is to estimate the seasonal trend of Iran’s outgoing longwave radiation by using National Oceanic and Atmospheric Administration (NOAA) satellites. In this study, the daily mean outgoing longwave radiation data for the period 1988/3/21 to 2018/3/20, with 1° spatial coverage, was extracted on a global scale from the United States Climate Data Record (CDR) database. Then, based on nearly 700 million pixels, the seasonal mean of Iran’s outgoing longwave radiation was calculated for each year, and a time-space matrix was obtained with dimensions of 154*30, for each season. The rows of the matrix are locations (pixels) and the columns are the time (season). For each season of the year, the non-parametric test of Mann-Kendall was calculated at a confidence level of %90 for each individual pixel. The results showed that there was no negative trend in different seasons in Iran, and only in winter, Iran's territory has an extensive positive trend. Hence, the outgoing longwave radiation does not show trends in other seasons of the year. The positive trend of the outgoing longwave radiation during winter is due to cloudiness and snow in most of Iran. Also, in this study, the long-term mean outgoing longwave radiation pattern of Iran was calculated for each season, separately. Findings of the long-term mean of the seasons showed that outgoing longwave radiation depends on latitude and topography of the earth. So, the highest outgoing longwave radiation is seen in low and flat latitudes (especially in summer) and the lowest one is seen in high and uneven latitudes(especially in winter).

Soil moisture influences the partitioning of rainfall into evapotranspiration, infiltration and runoff, hence it is an important factor for determining the magnitude of flood events. The Soil Moisture Active and Passive (SMAP) mission is a microwave all-weather sensor with cloud penetration capability it can be harnessed for inundation mapping. On the other hand, Global Precipitation Measurement (GPM) is the first satellite that has been designed to measure light rain and snowfall, in addition to heavy tropical rainfall. The Integrated Multi-satellitE Retrievals for GPM (IMERG) products, with 0.1° × 0.1° spatial resolution and 30 min temporal resolution, are available in the form of near-real-time data, i.e., IMERG Early and Late, and in the form of post-real-time research data, i.e., IMERG Final, after monthly rain gauge analysis is received and taken into account.
In this study, daily rainfall estimates from IMERG Early, Late, and Final runs (IMERG-E, IMERG-L and IMERG-F) are compared with daily precipitation measured by 22 synoptic rain-gauges over the northwest and western regions of Iran. Assessment is implemented for a period from April 2016 to February 2017. The assessment technique is using a contingency table that reflects the frequency of “Yes” and “No” of the satellite estimation. We have used two threshold values of 0.1 mm/day to define rain/no rain and 5.0 mm/day as moderate or higher rainfall events. The scatter plots of daily precipitation values, IMERG-E, IMERG-L and IMERG-F data against rain-gauge observations, indicate underestimation according to Bias for moderate or higher rainfall; this is more so when the precipitation threshold is increased. However, the IMERG-F shows better performance (i.e., closer to one-to-one line) in estimating moderate or higher rain. For the first threshold, all the three runs show approximately same performances; but some differences are seen at the second threshold. At this threshold, POD for IMERG-F is about 0.27 and for the two other products is about 0.14, which means larger fraction of the observed “Yes” events was correctly estimated by IMERG-F than IMERG-E and IMERG-L.
At the second part of this work, the synergistic use of satellite-based precipitation and soil moisture observations was dedicated to mapping of flood monitoring in the northwest of Iran on 14 April 2017. In this study, a value-added product was used that over-samples the SMAP volumetric soil moisture data with a spatial resolution of 40 km and posts it on a 9 km grid, SPL2SMP_E. The SMAP data maps show a pattern that is consistent with the precipitation maps; i.e., following the rainfall on 14 April, there is an increase in the saturated area and after that it begins to decay.
So together, SMAP and GPM can provide information on the surface water fluxes, an important quantity for assessing and monitoring the Earth’s fresh water resources. Therefore, integrated GPM and SMAP data can serve as a key tool for application users and emergency management to assess the extent and potential impact of flooding events among other hydrometeorological phenomena.

Models that predict the timing of weed seedling emergence are used to optimize weed control schedules. This study was carried out with the aim to predict the time of seedling emergence of three weed species, namely, Datura stramonium, Solanum nigrum, and Amaranthus retroflexus in field conditions using soil temperature and moisture. Three planting dates including first of June, first of July, and first of August were studied with three irrigation regimes including every other two, three, and four days and with three weed species. To express the changes in the emergence process, hydrothermal time model () was used. MPa - °C - days required for the emergence was calculated with the base temperature and water potential that was calculated by hydrothermal time model and soil moisture and temperature. Cumulative emergence percentage was plotted with hydrothermal time model and the sigmoid three-parameter model was fitted on it. Then the maximum germination percentage and MPa - °C - days required for 50% seedling emergence was calculated. Results showed that seedling emergence of three weed species was the highest and lowest in the first regime of irrigation in August and third regime of irrigation in June, respectively. Three species seedlings were emerged higher and faster in August than in June and July. Hydrothermal time required for the seedling emergence of these species was varied. D. stramonium had the lowest and S. nigrum had the highest hydrothermal time for 50% seedling emergence. The optimum temperatures for A. retroflexus, S. nigrum, and D. stramonium were 30-35, 37-30, and 29-34 °C respectively in all water potentials.

Soil temperature is the most important factor in sustainable development and agriculture. These factors are not controllable, but by examining the facts and figures it can be predictable and under control. The aim of this study was to evaluate soil temperature at synoptic stations in Sari, Mazandaran province. The study period is from 2003 to 2014. The database consisted of soil temperature at depths of 5, 10, 20, 30, 50 and 100 cm, dry air temperature, soil moisture and air in the above-mentioned period which is collected by the equipment used in meteorological stations. Neural network method is used to estimate the model. Results showed that soil temperature is directly related to air temperature But deep down near the surface of this dependency has been.and Soil temperature with air humidity has fluctuates. So that up to 50 cm rise but decreased with increasing depth. Soil moisture also examined and The results have shown that this agent is effective in high depths.

Iran is located in arid and semiarid areas; hence, it is essential to optimize the use of water resources, conserve, and reduce the loss of these resources. The use of mulch is one way to reduce water loss, especially in agriculture. This research was conducted in a completely randomized design in 2014. In order to investigate the effect of the amount and types of mulch on reducing soil surface moisture, nine treatments with three replications experiments were conducted in University of Birjand. The treatments were gravel mulch types (3 and 4), straw (10 and 20 g), coco peat (5 and 10 g), and zeolite (5 and 20 g). The treatments were saturated then weighed. The moisture loss per day was calculated for each treatment. The research showed that during testing the treatment, effect was significant at 1%. In addition, moisture content reduction did not change on a regular basis in none of the treatments. Moreover, it was found that the greatest effect in reducing evaporation from the soil surface obtained when coco peat, stubble, and straw were applied at 10, 10, and 20 g respectively). Comparing total evaporation after the completion of the experiment indicated that all treatments had positive and significant effect on reducing evaporation. The reduction of evaporation rate in treatments of straw 10 g, coco peat 10, gravel 4, and especially in straw 20 g was more than other treatments.

An accurate¡ precise¡ fast and ease as well as the ability for measurements in depth are the characteristics that are desirable in measuring soil moisture methods. To compare methods (time domain reflectometry and capacitance) with neutron scattering for soil water monitoring¡ an experiment was carried out in a randomized complete block (RCB) design (Split Split plot) on tomato with three replications on the experimental field of IAEA (Seibersdorf-Austria). The treatment instruments for the soil moisture monitoring (main factor) consist of neutron gauge¡ Diviner2000¡ time domain reflectometer (TDR) and an EnviroScan and different irrigation systems (first sub factor) consist of trickle and furrow irrigations and different depths of soil (second sub factor) consist of 0-20¡ 20-40 and 40-60cm. The results showed that for the neutron gauge and TDR the amount of soil moisture in both of trickle and furrow irrigations were the same¡ but the significant differences were recorded in Diviner2000 and EnviroScan measurements. The results of this study showed that the neutron gauge is an acceptable and reliable means with the modern technology¡ with the precision of ±2mm in 450mm soil water to a depth of 1.5 meter and can be considorelas the most practical method for measuring soil moisture profiles and irrigation planning program. The TDR method in most mineral soils¡ without the need for calibration¡ with an accuracy ±0.01m3m-3 has a good performance in soil moisture and electrical conductivity measurements. The Diviner2000 and EnviroScan are not well suitable for the above conditions for several reasons such as much higher soil moisture and a large error measurement and also its sensitivity to soil gap and to small change in the soil moisture in comparison with the neutron gauge and the TDR methods.

Soil moisture is one of the critical parameters in climatic, ecological and hydrological modeling and vegetation growth. Soil moisture is important for environmental studies but due to ground-based point measurement of soil moisture is expensive and impractical for watershed measurement, this parameter is not be used widely to simulate and predict models. Overall and uniform view on different part of land, repetitive photography from wide and inaccessible areas along with data with regular periods and decrement effects of plans on environment are important criteria's of remote sensing. Rangeland growth is heavily functioning of the water availability therefore the vegetation index from satellites may respond to the change of soil moisture. We used MODIS sensor images with the consideration of economical aspects, availability of images and high radiometric, temporal and spatial resolution to estimate the surface soil moisture. MODIS images were provided by Iranian Space Agency for the day's availability of soil moisture field data matched by satellite images. Radiometric and geometric correction was conducted by orbital parameters on all images and the images were converted to BIL format and shifted in relation to accurate boundary of Caspian sea. Using bathymetry map of Caspian Sea, atmospheric correction was conducted by dark pixel method. This case study analyzes the correlation the surface soil moisture measured gravimetric sampling and MODIS NDVI (250m by 250m), compares the collected coincided and lagged soil moisture and MODIS NDVI. Also estimates the soil moisture using NDVI by linear regression models during growing season (April-Aug) from 2003 through 2005 in arid and semi arid rangeland in Khorasan Razavi Province. NDVI shows lag to soil moisture because of vegetations response lag it. Results show that surface soil moisture in arid and semi arid rangelands has moderate correlation with simultaneous and lagged NDVI and can be estimated using NDVI during growing season. Stronger relations can be obtained with surface soil moisture data that are lagged by 2 weeks with respect to the vegetation index.