جستجوی مقالات مرتبط با کلیدواژه « دمای سطح زمین » در نشریات گروه « آب و خاک »

Drought is a natural phenomenon that affects different climatic, hydrological and environmental systems. Drought modeling is an important issue because it is considered important and necessary in order to contain or reduce its effects and to preserve water resources and social management. In this research, by using multiple indicators based on MODIS and CHIRPS precipitation data set, to investigate the spatial and temporal characteristics, intensity and frequency of drought in Qazvin plain in a 20-year period in order to identify and describe drought based on different indicators. The results of the surveys based on the TCI index showed that in 2004 and 2020, about 63% and 76% of the plain is under the influence of moderate to severe drought, with the difference that the result of the VCI index survey in the same year The analyzed data show that only 54.1% and 67.3% of the affected area show similar drought intensity levels. Therefore, the TCI index has estimated relatively more drought stress than the VCI index. From 2014 onwards, the eastern and southeastern areas of the plain are gradually facing an increasing trend of drought, and in the years 2018 to 2020, drought has been faced with much greater intensity. In examining the correlation between different indices, the PDSI index has the highest correlation with the SPI-1 index with a numerical value of 0.74. Qazvin plain is currently exposed to drought due to climate changes and lack of proper productivity in the use and distribution of water resources. Therefore, it is recommended that continuous monitoring of drought and the use of early warning systems are effective in preventing the possible occurrence of severe drought events in the future and the possibility of implicit risks for Qazvin Plain.

Evapotranspiration is one of the key components of water balance and irrigation planning. Thus, the accurate estimation of this component and the water consumption of plants can improve the management of water use and increase the efficiency of water consumption. Due to the limitation of tools for measuring evaporation-transpiration, remote sensing methods can be used for this purpose. There are several remote sensing algorithms for actual evaporation estimation including SEBAL, SEBS, Metric, etc. In this study we used the triangle method which only was used by Salimifard et al. (2022) in Mashhad Plain. They evaluated the results for the agricultural products, i.e., wheat and maize. The aim of this study is to evaluate the triangle method for a horticultural crop, i.e., pistachio in Kerman Plain. 

The study area is Kerman Plain in which pistachio is one of the most important agricultural products. Due to water scarcity in this plain, determining the water requirement of the crops is crucial for agricultural activities. Accordingly, it is important to have an appropriate estimation of actual evapotranspiration in the plain. In this paper, the triangular algorithm was used to estimate actual evapotranspiration in the Kerman Plain in the growing seasons of 2020 (1399) and 2021 (1400). For this purpose, the Landsat 8 satellite images with less than 10% cloudiness were used. The variables such as NDVI, LST, etc., were calculated by using the JAVA programming language in the Google Earth Engine code (GEE) system environment. The required meteorological data of Kerman station were acquired from IRIMO. The triangular algorithm is based on the two-dimensional spatial plot of normalized LST and normalized NDVI, which were calculated using bands 10, 5, and 4 of the Landsat 8 in the GEE. Estimation of the wet and dry edges was conducted by MATLAB code. the actual evapotranspiration obtained using the triangular method for a pistachio orchard, which was under irrigation management, was compared to the values obtained by the FAO-56 method. The results were evaluated by correlation coefficient (r), Root Mean Square Error (RMSE), and Mean Error (ME).

The results showed that the amount of evapotranspiration for pistachio was estimated with acceptable accuracy (r= 0.73 and RMSE=1.8, nRMSE=0.4, ME=-1.6). However, the NSE less than zero (-1.3) shows that the observed (FAO-56) mean is a better predictor than the Triangle algorithm. The values obtained from the triangular algorithm were lower than the values of FAO 56, which was in line with the results of the previous studies for both Agricultural and horticultural crops. This underestimation could be due to the uncertainty of the algorithm, uncertainty in the measured data, or due to the time difference between the date of the selected images and the date of irrigation. Moreover, inappropriate quality of water and soil in Kerman Plain and the uncertainty of plant coefficients used are among the factors that can underestimate evapotranspiration values by the algorithm. 

In this study, the triangular algorithm was used to estimate actual evapotranspiration in Kerman plain using remote sensing data. Actual evapotranspiration values obtained from the triangular algorithm were lower than FAO 56 values, which might be due to the uncertainty of the algorithm, uncertainty in the measured data, uncertainty of plant coefficients, or due to the time difference between the date of the selected images and the date of irrigation. To have a better evaluation of the remote sensing algorithms, it can be suggested to develop and apply a micro lysimeter in the farms and orchards, or to use the soil water balance of the farms and orchards. These may help to choose the more appropriate algorithm for the given study area, leading to providing the more proper and applicable advices for the farmers for managing the shortage of the water resources. Furthermore, it may help to update the crop coefficients which may lead to better estimation of evapotranspiration.

Accurate estimation of reference evapotranspiration (ET0) is essential in water management in the agricultural sector, especially for arid and semi-arid climates. ET0 plays a vital role in the water and energy cycle and is an essential link between ecological and hydrological processes. Therefore, accurately estimating ET0 is a major issue for understanding the water cycle in continuous soil-plant-atmosphere systems. The traditional ET0 estimation methods are mainly based on physical principles, such as Priestley-Taylor, Hargreaves, and Samani, which have many limitations in accurate ET0 estimation in cases of minimum meteorological parameters (such as radiation solar, wind speed, and air temperature). Numerous studies have focused on ET0 estimation using terrestrial data. However, in the case of a lack of meteorological stations, the conventional methods of estimating ET0 using ground data will be inefficient, so remote sensing (RS) provides the possibility to fill such a gap, in such conditions, satellite images are the most effectivefor evaluating ET0 in large areas. Because satellite images have a suitable spatial and temporal resolution, the time series of satellite images can be used to estimate ET0. The successful estimation of ET0 from satellite images paved the way for its prediction using artificial intelligence models. The primary satellite imagery sources can be obtained from Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS), and Global Land Surface Satellite (GLASS). Remote sensing data provides the possibility of recording more information through satellite images. Remote sensing methods can be used to extract vegetation information and different types of radiation, which help estimate ET0.

In the current research, two different agro-climatic locations including Ahvaz and Tabriz stations were selected. According to De Martonne classification method, Ahvaz was classified as dry climate and Tabriz as semi-arid climate. In this research, random forest (RF) and multi-layer perceptron (MLP) algorithms have been used to estimate monthly ET0 in Ahvaz and Tabriz stations. The input parameters were selected from Landsat 8 and MODIS satellite images in the time period of 2014 to 2021. The utilized parameters were the monthly average, Landsat Land Surface Temperature (LSTLand), MODIS Land Surface Temperature (LSTMOD), Landsat Satellite Normalized Difference Vegetation Index (NDVILand) and MODIS Normalized Difference Vegetation Index (NDVIMOD). To evaluate the accuracy of the input parameters and models, the estimated monthly ET0 was evaluated with the monthly ET0 of the FAO-Penman-Monteth equation.

The input parameters for implemented models were Landsat land surface temperature (LSTLand), MODIS land surface temperature (LSTMOD), Landsat Satellite Normalized Difference Vegetation Index (NDVILand), and MODIS Normalized Difference Vegetation Index (NDVIMOD). Six possible scenarios were defined to estimate monthly ET0. The first two scenarios were considered as a single parameter (scenarios 1 and 2) and other scenarios were evaluated with two input parameters. Scenarios 3 and 4 were evaluated based on the parameters of the Landsat satellite and MODIS sensor, respectively. In scenarios 5 and 6, monthly ET0 was estimated with Landsat and MODIS NDVI and Landsat and MODIS LST, respectively, to determine the effect of NDVI and LST values on ET0 estimation. According to the obtained results, for the MLP and RF models in Ahvaz station, the value of R2 ranges from 0.440 to 0.972 and 0.271 to 0.983, respectively. In Ahvaz station, the lowest and highest RMSE is 0.279 mm.month-1 (RF-5 model) and 1.396 mm.month-1 (RF-4 model), respectively. Additionally, in this station, the highest and lowest values of NS are 0.962 (RF-5 model) and 0.042 (RF-4 model), respectively. According to the obtained results, in estimating the monthly ET0, the best performance is related to MLP-6 (R2=0.972, RMSE=0.348, and NS=0.940) and RF-4 (R2=0.983, RMSE=0.279, and NS=0.962). The highest and lowest values of R2 in Tabriz station were 0.988 and 0.186, respectively. Moreover, MLP-4 and RF-5 models in this station have the lowest and highest RMSE, respectively. The results showed that in Tabriz station, the best performances were related to MLP-4 (R2=0.988, RMSE=0.299, and NS=0.935) and RF-4 (R2=0.979, RMSE=0.302, and NS=0.933). In addition, in this station, the RF-5 model has the weakest performance among all models with R2=0.186, RMSE=1.169, and NS=0.012.

The results showed that 1) the accuracy of monthly ET0 estimation in Ahvaz (arid climate) and Tabriz stations (semi-arid climate) with scenario 4 including LSTMOD and NDVIMOD was better than other investigated scenarios; 2) in estimating monthly ET0 using a single input parameter including LSTLand (scenario 1) and LSTMOD (scenario 2), in both Ahvaz and Tabriz stations, scenario 2 had better performance with both MLP and RF models; 3) estimation of monthly ET0 in Ahvaz and Tabriz stations has performed best with RF-4 and MLP-4 models, respectively, with LSTMOD and NDVIMOD input parameters (scenario 4); 4) in the comparison of scenario 5 (NDVILand, NDVIMOD) and scenario 6 (LSTLand, LSTMOD) in both RF and MLP models, scenario 6 has the best performance in estimating monthly ET0; and 5) in the comparison of monthly ET0 estimation in both arid and semi-arid climates, the best performance with a high correlation coefficient was obtained with the MLP model in semi-arid climates.

LST (LST) is one of the important parameters that affect the physical, chemical, and biological processes of the earth as well as environmental science and urban planning. Human activities such as land use changes and the development of urban areas led to an increase in the LST and the appearance of thermal islands. The main source of climate data such as temperature are synoptic stations, however, it is impossible and time-consuming to use traditional methods to estimate the LST for all types of earth conditions, on the other hand, synoptic stations only measure temperature information for specific points, and the obtained values are only related to that specific point; while according to the land cover and other conditions, the temperature in different parts of a region is different compared to the temperature recorded for a specific point and can be several degrees celsius lower or higher, therefore, it is necessary to use scientific methods that provide the possibility of calculating the temperature of any point on the earth's surface. At present, remote sensing images due to features such as wide and continuous coverage, low cost, timeliness, and the ability to obtain information in reflective and thermal ranges, are suitable tools for extracting LST and land use maps. Spatial analysis is one of the important subjects in the temporal and spatial evaluation of land surface data, which can be used to examine the spatial and temporal changes of spatial data in a region. Given that the data that are examined in environmental studies are not independent of each other in most cases and their dependence is due to the location of the observations in the studied space, which are called spatial data; Due to the existence of a spatial correlation between the data, the usual statistical methods are not a suitable method for examining these data, and spatial statistics can be used as a suitable option for analyzing these data. The aim of this research is to extract LST and land use map of Yazd county using a remote sensing technique. In this study, the spatial autocorrelation of LST in Yazd city and the identification of hot thermal clusters have been investigated using the global Moran statistic and the Getis-Ord GI statistic.

In this research, Landsat 8 satellite’s multi-spectral and thermal images have been used to extract the land use and LST in the study area, After performing the necessary corrections in the preprocessing stage, the land use map of the study area was prepared in 5 classes (built-up, vegetation cover, water bodies, bare land, and rock) using the support vector machine method and the overall accuracy and kappa coefficient were used to evaluate the classification result. In the next step, LST was extracted by the split window method. The relationship between LST and Soil adjusted vegetation index (SaVI) was investigated using regression analysis. In order to identify the spatial pattern of the LST, the global Moran index was used and hot spots were identified by Getis-Ord GI statistics.

Our findings show that the kappa coefficient and overall accuracy were equal to 0.96% and 98.99%, respectively, bare lands are the most, and water bodies have the least area, equal to 76.16 and 0.09%, respectively. The average LST was 50.83°C. The result showed that the type of land use had an effect on LST, the water bodies had the lowest, and barren lands had the highest mean LST, equal to 36.91 and 52.13 °C, respectively. Vegetation is one of the factors that regulate the LST, areas without vegetation have a maximum LST and areas with high density vegetation have minimum LST .Based on the results, the vegetation quality of the study area was poor and its average temperature was 45.61°C. The mean of SAVI index was equal to 0.09 and correlation analysis showed a negative correlation between SAVI index and LST (r = -051). The analysis of spatial correlation with global Moron indexes showed that the LST of Yazd has a spatial structure, in other words, LST is distributed in a cluster form, Based on the results of the Getis-Ord GI statistic, the area of hot and cold spots was equal to 66.86% and 27.4%, respectively. In general, parks, cultivated lands, tree and forest cover and water areas, formed the cold spot areas of yazd city, and the hot spot areas of yazd city were located in the industrial areas and surrounding urban lands, hospitals, passenger terminals, gas stations, places near busy roads and bare and uncovered lands.

The results showed a strong relationship between land use and LST. Based on the results, the LST data of Yazd has a spatial structure pattern, barren lands and industrial areas formed hot thermal islands, and vegetation and water bodies formed cold thermal islands in the study area; the wide area of barren lands, the lack and poor vegetation cover due to the lack of rainfall and drought are factors affecting the LST and the creation of hot thermal islands in the study area. The result showed a negative relationship between LST and SAVI, the vegetation of the study area is weak and its temperature is high. Considering the role of vegetation in adjusting LST, it is recommended to take necessary management measures in order to improve the quality of vegetation and reduce bare land in the study area, and also prevent the conversion of natural land uses into built-up land. The results of this research can be used by managers and planners for better urban management. The results of this research confirm the capability of remote sensing in environmental studies, it is suggested to identify thermal islands in other seasons and at night and compare the results with the results of this research.

Surface soil moisture is an important variable in nature's water cycle and can be affected by various factors, including temperature and soil characteristics. The use of ground sensors for measuring moisture can lead to spending time and expense and inappropriate distribution of samples on large scales. Therefore, Remote sensing observations can be an important tool in estimating soil moisture. The present study aims to use the TOTRAM model using Landsat 8 images and the SVR method using Sentile 1 images to estimate soil moisture.

In the present study, two TOTRAM methods based on pixel distribution in LST- VI space and the SVR method were used to extract soil moisture using the SAR technique and Sentinel 1 data. To implement the TOTRAM method, Landsat 8 images related to 4/29/1398 and 5/30/1398 are downloaded and after extracting NDVI and LST maps, The correlation between the dependent variable of moisture and independent temperature variables and vegetation variables has been investigated using Geographically weighted regression (GWR). To implement the SVR method after acquiring Sentinel 1 images related to 31/05/1398 and 27/04/1398, Soil Moisture Data Product FLDAS and 500 meters product of Modis Satellite (MCD12q1) were called to classify land cover in the Google Earth Engine system, and maps related to soil moisture were extracted. After extracting the moisture maps the distribution of moisture using the local Moran index has been investigated. By defining this index, positive values ​​for this index represent the cluster of distribution.

Examination of the soil moisture map obtained by the SVR method showed the concentration of moisture in areas with vegetation and water and the change in moisture status from July to August was visible. The humidity pattern has shown the reflection of the precipitation pattern so that maximum precipitation and humidity were observed in April and in summer both precipitation and humidity components decreased. Examination of the TOTRAM method and application of the GWR method has shown a complete correlation between NDVI LST and moisture. However, the correlation between LST and humidity with B (values) and standard error (SE) of 0.995 and zero corresponding to July and 0.981 and zero corresponding to August showed the highest correlation with vegetation variable with moisture dependence parameter, which this correlation In August, with increasing the coefficient of determination of R2 to 0.997 and a significant decrease of NDVI to the value of 0.415 in July, it has increased much more. Application of Moran local index with values ​​less than 0.05 for p-value and positive values ​​for z and near positive number 1 for Moran index showed the cluster distribution of moisture variable.

The results of TOTRAM and SVR methods showed the dependence of soil moisture status on conditions and cluster moisture distribution. According to the correlation coefficients of geographical regression, there is a greater correlation between temperature and humidity variables, especially in August, due to the decrease in vegetation density. The results of the SVR algorithm maps showed that in areas with the presence of vegetation, especially dense vegetation, we see an increase and with increasing temperature, we see a decrease in humidity. Also, the coordination of moisture patterns of the SVR algorithm and precipitation showed a direct relationship between moisture and precipitation. Considering that the SVR method uses parameters such as radar scattering intensity and land cover classification, as well as the use of Sentinel 1 radar images by this algorithm, more accurate results can be expected from this algorithm.

 Drought analysis in agriculture can not only be achieved by measuring precipitation changes but also by using other parameters such as soil moisture. Due to the fact that soil moisture affects plant growth and yield, it is often considered for monitoring agricultural drought. Remote sensing data are often provided from three sources: microwave, visible and thermal. Most satellite soil moisture-based algorithms rely on passive microwave images, active microwaves, or a combination of data from several different sensors. Among the various remote sensing methods, the microwave electromagnetic spectrum has fewer physical limitations than other spectrum in measuring soil moisture. However, microwave soil moisture data often have very large pixel dimensions (more than 10 km), making it difficult to use them on a small scale.

 In this study, in order to calculate the agricultural drought index at the field-scale, AMSR2 Retrieval data were calibrated first using field moisture measurement data in the Neishabour plain during 2017 to 2019. During the research period, 560 soil samples (20 samples in 28 shifts) were collected and soil moisture was measured in the laboratory of the Department of Water Science and Engineering, Ferdowsi University of Mashhad. LPRM_AMSR2_ SOILM3_001 is one of the third level products of the AMSR2 sensor, which is produced on a daily basis with a spatial resolution of 25 × 25 km2. Land surface parameters including surface temperature, surface soil moisture and plant water availability were obtained by passive microwave data using the Land parameter Retrieval Method (LPRM). Then, by using Modis sensor images (NDVI and LST), linear downscaling equations were extracted. The dimensions of the AMSR2 images were reduced from 25 kilometers to 1000 meters using these equations. In next step, SMADI Agricultural Drought Index, which is a combination of vegetation characteristics, soil moisture and land surface temperature, was used to monitor agricultural drought at the field-scale. Statistical indicators such as coefficient of determination (R^2), mean absolute error (MAE) and root mean square error (RMSE) were also used to evaluate the statistical performance.

By visual analysis of the role of vegetation and land unevenness, it was found that these two factors affect the regression relationships extracted for calibration of remote sensing data. The RMSE and MAE values for the regression equations used in the calibration process were calculated in the range of 1.6 to 4%, which can be considered acceptable in comparison with the mean values of the soil moisture data (15 to 20). The results showed that changes in SMADI index in three land use zones including rainfed cultivation (R1), medium rangeland (R2) and poor rangeland (R3) have experienced a similar trend to precipitation changes, illustrating that precipitation is one of the most effective factors in major changes in SMADI agricultural drought index fluctuations. It was also observed that SMADI index changes with a delay of 1 to 8 days compared to the precipitation changes in all three zones. In all three zones, the SMADI index followed a similar trend to in-situ soil moisture changes. At mot 80% of the changes in SMADI-R1 index can be explained by in-situ SM-R1, and the rest of the changes were related to other environmental factors or measurement error. This decreases to 68% in the R3 zone. It should be noted that soil moisture monitoring can more accurately reflect the impact of environmental factors on the changes in agricultural drought index such as SMADI than other variables; because the rainfall recorded at the meteorological station does not necessarily occur uniformly throughout the study area. On the other hand, any amount of precipitation will not necessarily lead to an effective change in soil moisture storage. This also renders assessment of the performance of agricultural drought indicators difficult.

 Examination of statistical indices of coefficient of determination (R2), mean absolute error value (MAE) and root mean square error (RMSE) showed that the algorithm used in downscaling as well as estimating SMADI agricultural drought index is well able to reflect the interactions between precipitation, soil moisture, vegetation and changes in canopy temperature profile. This feature justifies and strengthens its application in agrometeorological analysis.

The southern regions of Kerman Province have repeatedly encountered dust storms. Therefore, the objective of this study was to identify dust sources using effective parameters such as vegetation cover, land surface temperature, soil moisture, soil texture, and slope as well as to detect dust storms originating from these regions based on 31 MODIS images in 2016 and SRTM data. After normalizing parameters, the dust source map was prepared by fuzzy logic and assessed with an error matrix and available dust source map. Results showed that 30.5% of the study area was classified as a low source of dust, 39.55% as moderate, and 29.85% as severe-very severe. The overall accuracy of the produced map was about 70% and the producer and user accuracy of the severe-very severe class was more than 87%. The detection of dust storms originated from the identified dust sources also confirmed a crisis situation in the region. Due to the repeatability and continuity of obtained dust source map at pixel scale, it can be used to update available dust source maps and manage dust crisis in the region, properly.

Land Surface Temperature (LST) is an important parameter in weather and climate systems. Satellite remote sensing is a unique way to estimate this important parameter. However, satellite products have either low spatial resolution or low temporal resolution that limits their potential use in various studies. In recent years, the use of Spatio-temporal fusion techniques to produce high resolution simultaneous spatial and temporal images has been extensively investigated. In this study, a Flexible Spatio-temporal Data Fusion (FSDAF) was used to produce Landsat-like LST images with Landsat spatial resolution and MODIS temporal resolution. The quantitative and qualitative validation of the images was performed by comparing them with the Actual Landsat LST images. The results showed that the FSDAF algorithm has high accuracy in estimating daily LST data both qualitatively and quantitatively. The RMSE and MAE parameters of the images produced compared to the actual Landsat images were 1.18 to 1.71 and 0.88 to 1.29°C, respectively. The correlation coefficient above 0.87 and bias between -0.6 to 1.45°C also confirms the high accuracy of the algorithm in estimating Landsat-like land surface temperature on a daily time scale.

Achieving satellite images with high simultaneously spatial-temporal resolution has been one of the serious challenges faced by researchers in the field of remote sensing and its applications. In recent years, researchers have made serious efforts to solve the problem. In this study, producing Landsat like land surface temperature images with less than 16 day temporal resolution and over different land covers, using spatio-temporal image fusion algorithm (STI-FM) and MODIS Land surface temperature images, was investigated. The STI-FM technique consist of two main steps. First establishing a linear relationship between two consecutive MODIS LST images acquired at time 1 and time 2; then utilizing the above mentioned relationship as a function of a Landsat-8 LST image acquired at time 1 in order to predict a synthetic Landsat -8 LST image at time 2. The results showed strong linear relationship between the two consecutive MODIS images at times 1 and 2 (R2 in the range 0.85-0.95). The synthetic LST images were evaluated qualitatively and quantitatively and it was found that there is a high visual and strong agreements with the actual Landsat-8 LST images over different land covers. For example R2 and RMSE values were ranged 0.74-0.94 and 1.44-2.52, respectively.

Land surface temperature (LST) is used as one of the key sources to study land surface processes such as evapotranspiration, development of indexes, air temperature modeling and climate change. Remote sensing data offer the possibility of estimating LST all over the world with high temporal and spatial resolution. Landsat-8, which has two thermal infrared channels, provides an opportunity for the retrieval of LST using the split- window method. The main objective of this research was to analyze the LST of land use/land cover types of the central part of Isfahan Province using the split- window algorithm. The obtained results demonstrated that the "other" class which had been mainly covered with bare lands exhibited the highest LST (50.9°C). Impervious surfaces including residential areas, roads and industries had the LST of 45°C. The lowest temperature was observed in the "water" class, which was followed by vegetation. Vegetation recorded a mean LST of 42.3°C. R2 was 0.63 when regression was carried out on LST and air temperature.

Air temperature and land surface are among the most important factors used in estimating many hydrological parameters at the catchment area. These parameters are measurable by the presence of thermal bands in sensors. In this study, GLDAS model with MODIS sensor and GLDAS model and NCEP / NCAR temperature estimation with station data in Alborz, Qazvin, Zanjan, Kurdistan and Hamadan provinces were evaluated. The GLDAS model with MODIS sensor did not achieve good results in assessing land surface temperature data. GLDAS and NCEP-NCAR air temperature data were evaluated with station data. The results showed that GLDAS and NCEP-NCAR have a good accuracy. For example, in comparing NCEP-NCAR with the average of Agkhiriz stations, Ekbatan Dam, Washj, Varineh, Khodabandeh, Sanandaj, Bijar and Ghorveh in 2008, the coefficient of explanation (R2), the efficiency coefficient of the model (EF), the mean error error (MBE) Mean absolute error (MAE) and root mean square error (RMSE) were 0.984, 0.987, 0.979, 0.979, and 1713.1 degrees Celsius, respectively

The object of this research is the estimation of spatial distribution of three air temperature parameters at daily and monthly scales including minimum, maximum and mean temperatures at the Kurdistan province using MODIS sensor images setted on Aqua and Terra satellites. For this object, 8 synoptic stations at Kurdistan province were selected and for these 8 stations at 2013 and 2014, daily minimum, maximum and mean air temperature data and also land surface temperature at 4 daily times at these 8 stations for 2013 and 2014 years were extracted by processing on the MODIS sensor images. Afterwards, the multiple linear regression method was used to extract regional regression models for Kurdistan province between these 3 air temperature parameters and land surface temperature and to assesing the errors, cross validation based on the Mean Absolute Error, Mean Bias Error and Nash-Sutcliffe Efficiency coefficient was adopted. The results showed that it is a powerful relation between all of these 3 air temperture and land surface temperatures extracted by sattelite images. The results of cross validation showed an approperiate and reseanable agreement between the measured and estimated values of these 3 parameters at both daily and monthly scales so that the mean absolute error for minimum, maximum and mean temperatures were 2.7, 2.1 and 1.6 °C at daily scale and 1.9, 2. 1 and 1.1 °C at monthly scale, respectively. These results showed that it is possible to estimate these air temperature parameters at the places witout any meteorological stations with an approperiate and acceptable accuracy by extracting land surface temperatures of MODIS sensor for these places and applying the extracted regression models.

The amount of earth's surface net radiation directly depend on surface temperature, land use, soil and topography. In the present study, Landsat8 satellite imagery is used to estimate net radiation flux. Then, with using systematic sampling at 500 m intervals, the value of each surface layer for example The LST, NDVI, altitude, slope, aspect, soil type and land use at the sample points for analysis were extracted. Mono-Window algorithm has been used to extract LST. The results showed that there is a direct correlation between the increase in altitude and NDVI with net radiation flux. The linear correlation coefficients were also 0.68 and 0.19 respectively. There is also an inverse relationship with the linear correlation coefficient of 0.74 between net radiation flux and LST. And from survey the rate of net radiation flux in different geographical directions in the case study it was found that in the northwest with 637(w/m2) the highest net radiation flux and eastern orientation with 582.7 (w/m2) had the lowest rate of net radiation flux. The net radiation flux in these directions with the slope rate had a direct correlation with correlation coefficient 0.54. In addition, the rate of net radiation flux at water levels such as lake and reservoir dam with 817 (w/m2) has the highest rate of net radiation flux and saline lands with 509 (w/m2) of minimum net radiation flux. There is the highest and lowest rate of net radiation flux in inceptisols and badland areas, respectively.

Land surface temperature (LST) is a key parameter in estimating energy balance that has determinate role in climate change studies. Various scientists have studied monitoring of LST in recent decades. The land surface temperature, which is measured by means of thermometers for certain points, for large scale basin is not cost effective. Using of satellite images for estimating LST make the estimates easier and more economical than ground measurement. In this study, MODIS land surface temperature (LST) was evaluated. In addition, due to use of correction factors which may not always be available for Iran, land surface temperature estimated by Landsat 5 image,which its spatial resolution is much higher than MODIS, was also evaluated.
For this study, two groups of data were used: satellite data and in-situ data. Ground measurements were collected from 261 points of a wheat farm in Marvdasht plain located in Fars province. Temperature was measured in four height of wheat including: canopy cover, middle, 10 centimeter from floor and soil surface. After statistical tests, acceptable data were selected for the comparison. In this study, twenty eight satellite images were implemented; including 26 MODIS images (MOD02 & MOD11 product) and 2 level-1G Landsat 5 images. Land surface temperature was estimated from thermal band’s of Landsat 5 images by applying the necessary corrections. After providing land surface temperature (LST) maps, land surface temperature was extracted from LST map (Landsat5 & MODIS) based on the measurement points. Afterward, the equation between the observed data and estimated surface temperatures from Landsat 5 (MODIS images) were obtained. Relationship between estimated and in-situ data was analyzed on four different heights of the wheat. Land surface temperatures were also estimated by three different split–window algorithms from Becker and Li (1990), Price (1984) and Ultivertal (1994) and the coefficients were calibrated. Finally, Fisher test was used to determine significant differences between the observed and the estimated data.
It was found that the estimated temperature by satellite has the best correlations with the plant canopy temperature. Estimated data were evaluated against the in-situ data. Results showed that Landsat and MODIS images overestimated the LST by RMSE of 4.4 oC and 7.1 oC respectively. Error of Estimating LST with split–window algorithms was within the range of 3.5–3.7 degree centigrade. Among the three studied algorithms, Becker and Li (1990) approach showed the best performance (the least error). The significant differences between in-situ data and the satellite estimates were examined by Fisher Test. No significant differences were observed in any of the pairs of data.
For meso-scale and large-scale studies, using satellite images is efficient and economic than the point surface measurements. The choice of satellite images (Landsat or MODIS) is depend on the accuracy which is expected from the study.

Surface temperature maps are the most important components of the water requirements in basin scale and also are the most difficult to measure. Conventional methods are very local, ranging from region to field scale. Estimates of the Surface temperature and crop density over the entire area, especially for irrigated areas, are essential. Today, surface temperature can be estimated by using satellites and remote sensing (RS) techniques. In order to obtain the surface temperature, a set of satellite images was used. From a set of 12 Landsat 7 images during the 1998-2002, NDVI and SAVI indicators were established. Based on these indicators the surface temperature was estimated using the SEBAL (surface energy balance algorithm for land) algorithm and compared by measured data that reported by meteorological stations in Hamedan province. Results indicated that, no significant difference between surface temperature using remote sensing data and that reported by meteorological stations. Primary results showed that there was a significant relationship between measured and estimated surface temperature. The results of correlation coefficient were 0.75 and Root Mean Square Error (RMSE) was 5.4 c○, respectively.Surface temperature maps are the most important components of the water requirements in basin scale and also are the most difficult to measure. Conventional methods are very local, ranging from region to field scale. Estimates of the Surface temperature and crop density over the entire area, especially for irrigated areas, are essential. Today, surface temperature can be estimated by using satellites and remote sensing (RS) techniques. In order to obtain the surface temperature, a set of satellite images was used. From a set of 12 Landsat 7 images during the 1998-2002, NDVI and SAVI indicators were established. Based on these indicators the surface temperature was estimated using the SEBAL (surface energy balance algorithm for land) algorithm and compared by measured data that reported by meteorological stations in Hamedan province. Results indicated that, no significant difference between surface temperature using remote sensing data and that reported by meteorological stations. Primary results showed that there was a significant relationship between measured and estimated surface temperature. The results of correlation coefficient were 0.75 and Root Mean Square Error (RMSE) was 5.4 c○, respectively.Surface temperature maps are the most important components of the water requirements in basin scale and also are the most difficult to measure. Conventional methods are very local, ranging from region to field scale. Estimates of the Surface temperature and crop density over the entire area, especially for irrigated areas, are essential. Today, surface temperature can be estimated by using satellites and remote sensing (RS) techniques. In order to obtain the surface temperature, a set of satellite images was used. From a set of 12 Landsat 7 images during the 1998-2002, NDVI and SAVI indicators were established. Based on these indicators the surface temperature was estimated using the SEBAL (surface energy balance algorithm for land) algorithm and compared by measured data that reported by meteorological stations in Hamedan province. Results indicated that, no significant difference between surface temperature using remote sensing data and that reported by meteorological stations. Primary results showed that there was a significant relationship between measured and estimated surface temperature. The results of correlation coefficient were 0.75 and Root Mean Square Error (RMSE) was 5.4 c○, respectively.Surface temperature maps are the most important components of the water requirements in basin scale and also are the most difficult to measure. Conventional methods are very local, ranging from region to field scale. Estimates of the Surface temperature and crop density over the entire area, especially for irrigated areas, are essential. Today, surface temperature can be estimated by using satellites and remote sensing (RS) techniques. In order to obtain the surface temperature, a set of satellite images was used. From a set of 12 Landsat 7 images during the 1998-2002, NDVI and SAVI indicators were established. Based on these indicators the surface temperature was estimated using the SEBAL (surface energy balance algorithm for land) algorithm and compared by measured data that reported by meteorological stations in Hamedan province. Results indicated that, no significant difference between surface temperature using remote sensing data and that reported by meteorological stations. Primary results showed that there was a significant relationship between measured and estimated surface temperature. The results of correlation coefficient were 0.75 and Root Mean Square Error (RMSE) was 5.4 c○, respectively.

Reference crop evapotranspiration (ET0) data are desirable for assessing crop water requirements and scheduling farm irrigation. A large number of methods have been developed for assessing ET0 from meteorological data. In several places of the world including iran، the existing network of weather stations is insufficient to capture the spatial heterogeneity of this variable. The purpose of this study is to adjust the Hargreaves equation (HG) for using land surface temperature (Ts) data instead of air temperature to estimate ET0 only on the basis of the remote sensing-based surface temperature. For this purpose، a total of 365 images of MODIS Ts product for the years 2006 and 2007، covering irrigated sugarcane farms in Khuzestan were used. The results show that HG equation adjusted by both types of input data (land surface temperature and air temperature) has the same accuracy. RMSE and R2 were estimated 1. 5 mm d-1 and 0. 79، respectively.

The use of satellite data in an estimation of air temperature (Ta) near the earth’s surface has turned into an effective way for a large area of high spatial and temporal resolution. Throughout the present study، Artificial Neural Network (ANN) as well as M5 model tree were employed to estimate Ta in Khuzestan Province (South West of Iran)، using satellite remotely sensed land surface temperature (Ts) data acquired through the MODIS-Terra sensor. The input variables for the models consisted of the daytime and nighttime MODIS Ts as well as extraterrestrial solar radiation. A total of 365 images of MOD11A1 Ts product for the year 2007، covering the area under study were collected from the Land Processes Distributed Active Archive Center (LP DAAC). The results indicated that coefficient of determination (R2) for both models exceeded 0. 96. However، ANN model estimations of air temperature were more accurate than RMSE with the respective R2 values of 1. 7 and 0. 97 oC.

Estimation the amount of radiation reaching the Earth's surface (Rs) is an important factor in the energy balance models simulation of plant growth and evapotranspiration estimation. Most Estimation models to radiation reaching the Earth's surface use satellite data and they are based on land surface temperatures. In this study, the Accuracy of solar radiation estimation is investigated Using four different models of neural networks (with the names of ANN1,ANN2, ANN3, ANN4) with the inputs Including products land surface temperature MODIS sensor (models 1 and 2, and models 3 and 4 are based on MOD11A1 MYD11A1 products, respectively), extraterrestrial radiation (Ra) and relative sunshine (n / N). The results show that four neural network models are able to estimate the amount of radiation reaching the Earth's surface with good correlation (R2>. 85). However, models based on MOD11A1 products have a higher accuracy than models based on MYD11A1 products. Neural network model of ANN1 (based on MOD11A1 products, relative sunshine and extraterrestrial radiation (Ra)) with the coefficient of determination (R2) equal to. 9332 and the root mean square error (RMSE) equal to 1.4448 MJ per square meter per day is more accurate on the estimation of solar radiation than other models. The results also showed that the Neural network model ANN2, comparing with Hargreaves and Samani models based on air temperature and extraterrestrial radiation, is More accurate in estimating of solar radiation.

Land surface temperature is one of the main parameters required for hydrological elements estimations in regional scale. This variable can be measured using thermal band of satellite sensors. In this research ETM+ and MODIS imageries of downstream end of Qazvin irrigation network of Qazvin were used. Four different scenarios were proposed and the obtained results were evaluated by volumetric lysimeter measurements. Results indicated that if water bodies are removed from satellite imageries، a reverse relation can be observed between NDVI crop index and land surface temperature in both ETM+ and MODIS imageries. Although، non-linear relation has a good result in compare to linear، no significant differences were observed between them. Using simple aggregation method produced acceptable result in compare to the aggregate nearest neighbour method. Finally، cells with dimensions following f (n)  602، n 1،2،3،4،… n rule after integration، produced the best results. Among recommended scenarios، initial segregation of lands and application of proposed algorithm in lands with vegetation cover produced the best coincidence between land surface temperature data and vegetation index (NDVI).

The determination of air temperature is important in the energy balance calculation, hydrology and meteorological studies. In this regard, the limited number of meteorological stations is one of the serious problems for air temperature determination on a large spatial scale. The remote sensing technique by covering large areas and using updated satellite images might be appropriate for estimation of this parameter. In this research, the negative correlation between land surface temperature and vegetation index (NDVI) has been used for air temperature estimation through TVX method in which the inference of air temperature is based on the hypothesis that the temperature of the dense vegetation canopy is close to air temperature. For investigation the performance of TVX method, images of MODIS sensor have been applied for the Sefidrod River basin in the years 1381- 1382-1384. The spilt window technique which was developed by Price has been used for land surface temperature calculation. The mean difference between observed and estimated land surface temperature using Price algorithm was about 6.2Co. This error can affect the air temperature values. Because of using NDVI index in TVX method, this method has the sensitivity to the vegetation density, though in the parts with sparse vegetation, the value of error increases. 4 percent variation of air temperature against the 0.05 increasing of maximum NDVI indicates the high performance of TVX method for air temperature estimation in large areas.