sebal

Spatial quantification of actual evapotranspiration (ET) is crucial for water resource management and planning in arid regions. This research focuses on the investigation and estimation of evapotranspiration using Py_SEBAL and METRIC algorithms, as well as the WaPOR model and MOD16 product, during the years 2021 and 2022 in the Moghan Plain located in Ardabil Province. The results of each model are compared with the FAO-56 method, which is a standard approach for estimating evapotranspiration in different areas. The results indicate that the Py_SEBAL algorithm shows the highest correlation with the FAO-56 method, with an R value of 0.97 and an RMSE (mm/month) of 1.88. Next, the METRIC algorithm demonstrates the highest correlation with an R value of 0.89 and an RMSE (mm/month) of 1.5. To further validate the performance of the estimation models in different areas, the WaPOR database is also utilized. The obtained outputs indicate that among the irrigated lands covered by the water network, the Py_SEBAL algorithm exhibits the highest correlation with the values derived from WaPOR, with an R2 value of 0.77. After Py_SEBAL, METRIC demonstrates a relatively suitable correlation with an R2 value of 0.55. Considering the land use map of the region, more than 60% of the area is covered by the irrigation network. Since Py_SEBAL yields the best results in the conducted investigations for these lands, the estimation of evapotranspiration volume is focused on the entire region. The results indicate that the volume of evapotranspiration is approximately 4/5 times higher per hectare in irrigated lands compared to drylands.

This study was conducted to determine the actual evapotranspiration values and water supply status of different cultivation pattern in 341 hectares of agricultural lands of Deryas and Tut-Aghaj of Mahabad plain in West Azarbaijan, with pressurized irrigation systems using remote sensing tools and SEBAL algorithms. To carry out this research, 7 different images of Landsat 8 satellite were downloaded in the period from May to September of 2022 (plant growth season) and the amount of crop water requirement on the basis of the cultivation pattern was estimated in the study area and compared to the amount of water consumed in the irrigation network. On the basis of obtained results, the highest amounts of evapotranspiration in the growth season relates to apple, Peach, alfalfa, cherry, sour cherry, apricot, plum stencil, grape and wheat, respectively. Also, the amount of water consumption during the growing season on the 341 hectares of the study area was estimated to be 2060000 m3, while the water input into the network in the previous agricultural year was more than 2500000 m3, representing an 82% efficiency of the pressurized irrigation network in Daryas. It is mentioned that in most of the study area, unauthorized wells are also used for land irrigation. Based on the obtained results in this study, it is inevitable to control the condition of the irrigation network and the amount and method of water distribution.

Due to the location of Iran in arid and semi-arid regions and according to the quantitative and qualitative limitations of water resources, optimal management and volumetric delivery of water is important in irrigation and drainage networks. In this regard, it is necessary to estimate the water requirement of crops accurately and provide adequate water to farmers. Remote sensing technology provides facilities that can be used to obtain different layers of information at the lowest cost in the fastest time. Accordingly, many researchers have used remote sensing data to monitor vegetation cover, provide land use maps, estimate crop evapotranspiration and have declared this technology as appropriate tool for such studies. Based on the previous studies, it is observed that low researches has been conducted to investigate the crop evapotranspiration considering the crop water requirement. Therefore, the most important objectives of this study are: provide the cropping pattern and land use maps using Sentinel 2 satellite images, determination of the water requirement for the delivery points of irrigation network, determination of the actual evapotranspiration of the crop cover using SEBAL algorithm and Landsat 8’s images and finally evaluation of the water supply and management in the Mahabad irrigation and drainage network. In order to determine the cropping pattern of the Mahabad irrigation and drainage network, Sentinel 2 images have been used related to the 2018-2019 crop year. The images were examined in terms of the region of syudy and the percentage of cloudiness and after selecting the appropriate images, pre-processing operations including radiometric and atmospheric corrections were applied on them. Then, the NDVI index was calculated based on selected images. On the other hand, after determination of the classification classes, the phenological cycle of crops were examined for each class and spectral pattern of crops was determined during the growing season. Training samples were selected for supervised classification using the existing maps, Google Earth images, creating images with false color composites and considering the growth pattern and some of them were also considered for validation of the classified map. Then, the cropping pattern map was obtained by using the SVM classification algorithm. After generating the crop classification map, the water requirement of the different classes was determined based on the Penman-Montith evapotranspiration method, applying plant coefficients and irrigation application efficiency at the volumetric water delivery points. Finally, the actual evapotranspiration rate of the study area calculated based on the SEBAL algorithm and compared with the net water requirement map. Based on the results, kappa coefficient and overall accuracy of the classified map were determined to be 0.953 and 91%, respectively. The area of the planted agricultural farms was equal to 10594 hectares and 1576 hectares of farms were without planting. The area of orchard farms was equal to 6786 hectares and the area of sugar beet, wheat, alfalfa and corn lands were obtained to 998, 1839, 693 and 278 hectares, respectively. Thus, the net irrigation water requirement was equal to 71 million cubic meters and the gross irrigation water requirement was calculated equal to 161.36 million cubic meters, considering the irrigation efficiency of 44%. On the other hand, the evaluation of the SEBAL evapotranspiration maps during the growing season indicated that the total amount of evapotranspiration was equal to 79.78 million cubic meters, and this amount was 14% higher than the net irrigation water requirement. Finally, according to the crop classification map and based on the comparison of the net irrigation water requirement and evapotranspiration maps, the water consumption in the Mahabad irrigation and drainage network was evaluated. It turned out that in the upstream farms of the network or close to the Mahabad River, the Water consumption was more than net water requirement and downstream areas were faced to deficit irrigation due to lack of sufficient water.Finally, based on the results of this study, it was observed that by using the capabilities of satellite images and remote sensing, it is possible to monitor and evaluate the condition of agricultural farms on a large scale with acceptable accuracy. Also it is possible to improve the management of water supply and water use efficiency in irrigation and drainage networks by creating up-to-date land use maps, determining net and gross irrigation water requirment and comparing with actual evapotranspiration maps.

Evaporation and Transpiration (including soil water evaporation and transpiration in plants) account for about 60% of the annual atmospheric precipitation on the earth. This process is the backbone of the hydrological cycle and a key element in Water Resources Management and the agricultural sector. The Soil and Water Assessment Tool (SWAT) and the Surface Energy Balance Algorithm for Land (SEBAL) are specialized models that address this problem. A SEBAL model is a tool for estimating the spatial distribution of actual evaporation and transpiration using satellite images such as MODIS, and Landsat 8. Although Landsat 8 images have a higher Spatioal resolution than MODIS images (30 m vs. 1000 m), their Tempoural resolution is lower than that of MODIS images (every 16 days vs. every day). On the other hand, daily MODIS images may not always be usable because of problems such as cloudiness. In addition, the interpretation of numerous satellite images is very time-consuming. In the current study, the evaporation and transpiration values in the catchment area of Karun River were obtained in three dry, normal and wet years (2019, 2020, 2021) using the recalibrated SWAT model based on runoff and crop yield and the SEBAL algorithm. Then the results of the SEBAL algorithm and SWAT model were compared with each other based on the water year conditions. In the second stage of the research, the results of the SEBAL algorithm and the range of changes of the main parameters of this algorithm in the plains of Ahvaz-Mlathani and Al-Baji basins were presented according to the ground data and using the MODIS images from the Terra satellite with a suitable time resolution and OLI sensor from Landsat8 satellite, which has a good spatial resolution.

Precipitation is considered one of the most important components of hydrological cycle, and its effective and usable amount for plants is of great importance in the agricultural sector, especially rainfed cultivation. In this research, the effective precipitation (EP) in dry wheat fields of Khomein city was estimated by using RS and SEBAL on 28 available images from Landsat8 in the crop years 2014 to 2022. Penman-Monteith-Fao method was used to evaluate the accuracy of SEBAL. Then, a model of EP estimation was developed with ANN and meteorological data. For this purpose, the correlation between meteorological data and Growin Degree Days (GDD) with EP was investigated by Pearson's correlation method. the meteorological data of three stations from the closest synoptic stations to the study area were used and The meteorological data of the study area were interpolated using the Inverse Distance Weighting method (IDW). According to the results of the correlations, the average temperature parameter with a correlation of 0.92 and the GDD and the maximum relative humidity respectively with a correlation of 0.86 and -0.77 as effective variables in estimating EP. In the next step, the most effective parameters were used for modeling. the networks were trained under different scenarios, and the performance of the networks was evaluated using the RMSE and MBE error criteria. The results showed that by using the BR learning algorithm and having the variables of daily temperature and GDD, it is possible to predict the amount of EP for the target area with very good accuracy. The RMSE value of this model was 0.1899 mm and MBE was estimated as -0.0115 mm. By using the presented model, with simple meteorological variables, the actual evapotranspiration and finally the EP of the desired area can be determined with appropriate accuracy without the need to solve complex algorithms.

Data assimilation is a scientific method which integrates information from the actual measurements and the model predictions within a defined framework to enhance the accuracy of the estimations of variables or parameters under investigation. This process comprises of two phases called prediction and update. In the prediction phase, the model estimations are computed using the Monte Carlo simulation method. This process continues until observation data (measurements) becomes available. In the update phase, model estimations and observations are combined, taking into account the confidence level associated with each one of the data sources (observations, and model estimations), resulting in posteriori estimates (updated outcomes). This study focuses on improving the accuracy of the estimates of the soil moisture contents at root zone depth using the ET estimation model suggested for non-standard conditions by FAO 56 (Allen et al., 1998) through the utilization of two data assimilation methods namely the Ensemble Kalman Filter (EnKF) and the Particle Filter (PF). ET was calculated according to the Surface Energy Balance (SEBAL) algorithm using Landsat 8 satellite imagery as observation data for the data assimilation system. In order to ascertain the effectiveness of the data assimilation methods applied, results obtained from a data assimilation system (referred to as BL) which was implemented in two sugar beet fields and two corn fields in the Jovein region were utilized. In the BL system, simulated soil moisture contents of the root zone layer obtained by numerically solving the Richards equation were combined with the soil moisture measurements taken at specific points in the fields using soil moisture sensors (TDR). 51 TDR access tubes were installed in the fields to measure soil moisture contents at various depths using Time Domain Reflectometry (TDR) sensors. Soil moisture measurements were recorded from Khordad to Aban 1399 (write in English calendar).The essence of data assimilation methods lies in the amalgamation of homogeneous information about the studied phenomenon obtained through different mechanisms. In this study, the observations utilized included ET calculated based on the SEBAL algorithm. In the FAO-56 model, evapotranspiration and soil moisture content of the surface and root zone layers were computed. Since soil moisture contents of the surface and the root zone layers serve as the initial conditions for subsequent simulation steps, data assimilation was applied to the soil moisture content instead of the ET. To achieve this, ET obtained from the SEBAL algorithm was converted into soil moisture content and subsequently used in the data assimilation process.The average standard deviation of the simulated soil moisture contents (σ) in the PF and EnKF approaches was 36% and 32% lower, respectively, compared to the open-loop (OL) approach. Throughout the growth period, PF and EnKF consistently resulted in lower σ compared to OL in the three fields which were irrigated by center-pivot irrigation systems. However, following each irrigation event in the field M, which was irrigated by a drip irrigation system, σ suddenly increased and became nearly equivalent to OL. This was attributed to the greater depth of irrigation water in this field as compared with the other three fields. On the average, the magnitude of σ change (σΔ), representing the reduction in σ before and after the update, was 0.032 and 0.037 for PF and EnKF, respectively. Consequently, the results suggested that data assimilation reduces the uncertainty of simulation results.The research findings indicate that data assimilation significantly reduced the BIAS and nRMSE indices compared with the OL approach. The average BIAS for EnKF, PF, and OL was 0.018, 0.020, and 0.028, respectively, while the average nRMSE for the three methods was 17.3%, 17.5%, and 18.9%, respectively. In other words, the use of ET observations obtained from Landsat 8 satellite imagery and the SEBAL algorithm significantly improved the accuracy of the estimation of ET with the FAO-56 model. Therefore, the obtained results suggest that data assimilation can be employed to enhance the accuracy water consumption estimates and to improve irrigation management.

In recent years, we have witnessed the unsustainable use of water resources, which has led to short-term and long-term water crises. The diversity of water and soil resources, along with climate change, has made the scientific management of agricultural water inevitable. In a such scenario, managing scarce water resources to meet ever-increasing needs is challenging. To make the best use of water resources, it is important to know the amount of water needed for economic production.Determining the water requirement of crops, especially the evapotranspiration potential, indirect ways and in different climates for agricultural and orchard plants is one of the basic strategies of each region. Evapotranspiration (ET) is a process that includes two parts: evaporation (evaporation of water from the surface of soil and vegetation and surface water) and transpiration (evaporation of water from plant organs due to plant physiological activities). The purpose of estimating evapotranspiration is to determine the crop’s water requirement, and irrigation planning, and to evaluate the sensitivity of crops’ performance to water deficiency in different stages of plant growth. Sugar beet is one of agricultural crops that is placed in the cultivation pattern and is cultivated in a wide area of the world due to the need for sugar consumption. Determining the evapotranspiration of this crop and planning its irrigation is of particular importance. Numerous studies showed that the water requirement of sugar beet, based on the variety and climate, differs. Various techniques have been proposed to measure ETc, and each method has advantages and limitations. Some of the widely used methods are lysimetric experiments, eddy covariance, Bowen ratio, energy balance method, and soil water balance method. In recent years, new technologies are also used to estimate evapotranspiration, among them, the Surface Energy Balance Algorithms for Land (SEBAL) can be mentioned. This research aims to compare ET, water requirement, and water productivity of sugar beet in lysimetric data to SEBAL algorithm using Landsat 5 satellite images, from 1996 to 1998.

This experiment was carried out at the Chahar-Takhteh research station (Shahrekord, Iran) at latitude 50 ̊56ʹ and longitude 31̊ 11ʹ, 2066 m above sea level.In the spring of 1996, 1997, and 1998, before planting the crop, the soil inside the lysimeter was irrigated to reach the saturation level. Two days after irrigation and at field capacity, monogram seeds of sugar beet, at the rate of 120,000 crops per hectare, were cropped. The row spacing in the field around the lysimeter was similar. Irrigation was based on the discharge of about 35 to 45 % of the moisture content at field capacity. The required amount of water was calculated by the neutron probe and added to the lysimeter. simultaneously, the surrounding area was also irrigated.Remote sensing data included Landsat 5 satellite images for the years of experiment, path 164, and row 38. The temporal resolution of the satellite was 16 days. Spatial resolution for visible, near, and mid-infrared bands was 30 and 120 m for thermal infrared. The 25 cloud-free images were downloaded (6, 9, and 10 images) for research years. These images were retrieved from the website (https://earthexplorer.usgs.gov) as geometrically and radiometrically corrected and processed in ERDAS Imagine 2022 software. To estimate actual evapotranspiration, the energy balance equation is used, λET=Rn-G0-H. In this equation, Rn is the net incoming radiation flux, H is the sensible heat flux, G0 is the soil heat flux, and λET is the latent heat flux of evaporation (W/m2 ). The statistical indicators include mean absolute error (MAE) which is unsigned, mean bias error (MBE), root mean square error (RMSE), normalized root mean square error (NRMSE), and Coefficient of Determination (R2 ).

Evapotranspiration of sugar beet in lysimeter and in SEBAL on the days of satellite passage in 1996 to 1998 (25 overpasses without clouds) showed that the difference of evapotranspiration in the two methods was -1.20 % and -0.13 mm d -1 which showed high accuracy. The negative sign means that the SEBAL estimates were lower than the corresponding values in the lysimeter. The statistical indices values of RMSE, NRMSE, MAE, and MBE for 25 pairs of evapotranspiration values were 0.7031 mm d -1 , 0.1102, 0.5552, and -0.1312, respectively. The RMSE, NRMSE, MAE and MBE statistical indices for 18 pairs of monthly evapotranspiration were 54.1155 mm month-1 , 0.3225, 40.9462, and - 28.7955, respectively. The total values of evapotranspiration in lysimeter were equal to 1096.6, 1022.6, and 906.3 mm during the growth period (total mean equal to 1040.6 mm) from 1996 to 1998, respectively. The total values of evapotranspiration in the SEBAL were equal to 1004.6, 831.6, and 666.4 mm during the growth period, total mean of 834.2 mm. The mean difference was around 19.8%. The results of mean water productivity were 5.02 kg m-3 in lysimeter and 6.26 kg m-3 in SEBAL. Because of lower evapotranspiration values in SEBAL compared to the lysimeter, the water productivity values were higher.

Determining the water requirement of crops is the basis of planning for the sustainable use of water resources and irrigation of crops. The sugar beet has a great amount of evapotranspiration due to its large green cover. Accurate quantification of crop evapotranspiration (ETc) at local and regional scales can help water policy and decision-making in water resources and their management. The results indicated that the SEBAL algorithm using Landsat 5 satellite images with a coefficient of determination (R2=0.9889) in the daily time period and a coefficient of determination (R2=0.9318) in the monthly time period had a good correlation with lysimetric data. In general, results showed that SEBAL has a special capability as one of the widely used remote sensing algorithms to estimate crop evapotranspiration.

Considering the importance of water in the agricultural sector, it is necessary to know the usable or effective amount. Therefore, in this research, using remote sensing and implementing the Surface Energy Balance Algorithm (SEBAL) on 28 images from Landsat 8 for the crop years 2014 to 2022 in During the growth period of dry wheat in fields of Khomein city, the rate of evapotranspiration and effective rainfall were estimated. The accuracy of SEBAL has been evaluated with Penman-Monteith and pan evaporation methods, and then the results obtained with experimental methods of effective rainfall estimation have been compared and their relative error (RE) has been estimated. The results showed that the USDA method with a RE of 12.2% had the lowest error and the FAO with a RE of 60% had the highest error compared to the SEBAL.

Evapotranspiration (ET) is one of the most important parameters in agricultural water management which has spatial and temporal variations. Remote sensing-based algorithms of ET estimation have been applied in several studies. Using MODIS satellite images ET can be estimated on a daily basis at the pixel size of 1000 meters while using Landsat 8 images can be estimated every 16 days in the pixel size of 100 meters. Various methods are available for combining satellite images with low (such as MODIS) and medium (such as Landsat 8) spatial resolution to estimate evapotranspiration on a daily basis in the lower pixel size. Downscaling is one of these methods which was evaluated in this study is one of the plains in Kermanshah province. For this purpose, the actual ET over the study area was calculated during the summer of 2014 using the SEBAL algorithm based on six satellite images of Landsat 8 and MODIS. Although there are several methods for downscaling the estimated evapotranspiration based on MODIS images, in this study two simple downscaling methods (ratio and subtraction). were used to convert the Landsat 8 image scale. Results of downscaled images of MODIS were compared with the results of Landsat 8 images graphically and statistically. Given the fact of small size and mixed land use types in the study area results of downscaling methods were not acceptable.

The crop evapotranspiration (ET) has spatial and temporal variations within the region due to changes in meteorological parameters, phenological stage, and farm management conditions. The main objective of the present study was to compare the trend of temporal and spatial variations of grain maize evapotranspiration in Mahidasht using satellite images with available resources (due to lack of Lysimeteric data). In this study, the daily evapotranspiration for the four maize farms in the different parts of the plain was calculated using Landsat 8 satellite images and the SEBAL algorithm on six dates during the growing period of maize in the year 2014. Since there was no field measurement of ET to verify the results of estimated ET based on the SEBAL algorithm, the final results (evapotranspiration) and intermediate results (Land surface temperature, Albedo, and NDVI) calculated by the SEBAL algorithm were compared with acceptable values in the literature. Results showed a similar trend of estimated evapotranspiration by the SEBAL algorithm and AGWAT software. The trend of increasing and decreasing land surface temperature was rational. The average of NDVI during the growth period in the studied farms was between 0.47 and 0.53 which was acceptable. Albedo with an average of 0.17-0.18 had slight variations between the selected farms. Results showed rising in land surface temperature, evapotranspiration, and NDVI to the middle of the growth period and then reducing of these parameters. The average estimated maize evapotranspiration based on the SEBAL algorithm was between 3.35 to 8.53 mm/day for the six imagery dates.

Iran has a large area under cultivation, and reliable and timely crop yield forecasts are critical for making timely food supply decisions. As a result, several methods for estimating crop yield based on remote sensing observations have been published in recent years. One of the first and most critical steps in this study is to estimate real evapotranspiration using the SEBAL algorithm and high spatial and temporal resolution Landsat8 satellite images. The evapotranspiration obtained from satellite images was compared to evaporation data from the Zanjan synoptic meteorological station's evaporation pan, with statistical indicators (MAE=0/36, RMSE=0/45, R2 =0/94) indicating satisfactory results. The AquaCrop model and its four execution steps were used to simulate the product yield, and the necessary coefficients and data from the model were applied in satellite imagery calculations for each of these steps. For comparing CC data determined by satellite imagery and AquaCrop model, the RMSE, MAE, and R2 indices were respectively 11/06, 9/2 percent, and 0/94, and 0.633 and 0/359 mm/day and 0/95 for transpiration. The average yield was calculated to be 1/189 ton/ha in field pixels and its comparison with agricultural statistics and field studies (1-1/2 ton/ha) showed acceptable estimates for wheat yield in the area of this study.

Evapotranspiration (ET) is one of the key components of the Earth's hydrological cycle and its accurate estimation is very important in the water resources management and planning in agricultural usages. ET as a main hydrological flux, links energy, carbon and water cycles, and has an important role in meteorology, hydrology and water resource management, especially in Agricultural Water Management (AWM). Precise and accurate estimation of ET is an essential for the Integrated Water Resources Management (IWRM). Since ET is considered as water consumption over the agricultural areas, knowledge about ET (water consumption), as one of the most important features of the hydrologic cycle, plays an important role in irrigation planning and agricultural management. Quantification of water consumption in agricultural areas can be carried out using climatic and environmental variables, e.g. reference evapotranspiration (ETo), crop coefficient (Kc) and crop evapotranspiration (ETc).

Over the past few decades, satellite imagery based methods have come to the attention of researchers, which they have developed varieties of remote sensing methods to estimate evapotranspiration. In the present study, the Surface Energy Balance algorithm for Land (SEBAL) and Mapping Evapotranspiration with Internalized Calibration (METRIC)/Earth Engine Evapotranspiration Flux (EEFLux) algorithms were used to estimate ET for every pixel of the Landsat 8 at the Arak maize farm, Iran. Various observed data was used in this study. The meteorological data were first obtained from Markazi Regional Water Authority (sunny hours and solar radiation at selected dates) and Arak synoptic station to calculate actual ET and the selected satellite data and ET models were then evaluated. Moreover, wind speed, dew point temperature and daily temperature at the corresponding dates of the Landsat 8 satellite overpass time were gathered from Iran Meteorological Organization. In order to obtain the Leaf Area Index (LAI) at the overpass time of the Landsat 8 satellite over the study area, field measurements were performed. For this purpose, at each date of field operation (which was at the same date as the overpass time of the Landsat 8 satellite) and based on the Landsat pixel size, a 30*30 m plot was designed for field sampling in the selected maize farmland, and a 1*1 m square sub-plot was then designed in the center of each plot. In order to compare the used models, some quantitative criteria are required to measure model performance. In this study, daily ET from SEBAL and EEFlux were used to compare the relative performance of the algorithms for the eight Landsat images during the growth period. Based on commonly used statistical metrics, percent bias error (PBIAS), root mean squared error (RMSE), Nash-Sutcliffe coefficient of efficiency (NSE) and coefficient of determination (R2) criteria were used to evaluate the models. Taylor diagram was used to provide a visual framework and graphically summarizes how closely a set of patterns matches the observed data. In this study, Taylor diagram was used for visual comparing Evapotranspiration derived from SEBAL and EEFLux algorithms, based on the ET obtained from Lysimeter (as reference data).

The results of estimating daily Evapotranspiration from Surface Energy Balance algorithm for Land (SEBAL) and Earth Engine Evapotranspiration Flux (EEFLux) algorithms indicated that daily ET were low at the beginning of the growing season and then came up until middle of the growing season, and then ET values have been decreased due to decreasing temperature as well as changes in maize cover in the study area. Comparison of SEBAL and EEFLux algorithms showed that SEBAL algorithm has estimated about 7.71% of daily ET more that EEFLux algorithm at Arak maize farm. The results of performance evaluation showed that RMSE, NSE, PBIAS and R2 were obtained 0.711, 0.807, 7.398 and 0.885, respectively based on SEBAL algorithm, and for EEFLux algorithm were equal to 1.046, 0.582, 15.080 and 0.793, respectively. The Taylor diagrams showed that the SEBAL model had a lower RMSE and higher correlation than the EEFLux model. Comparing the standard deviation of both models, it was found that the SEBAL model was more in agreement and closer to measured daily ET values than the EEFLux model. This can also be seen in Fig. 6, where SEBAL has a close standard deviation to the measured data, but EEFLux model has a lower standard deviation than the measured data. This indicates that the EEFLux model cannot predict the daily ET the same as SEBAL model. According to observed data (lysimeter data), evaluating the performance of SEBAL and EEFLux algorithms showed that SEBAL algorithm had higher correlation and less standard deviation than EEFLux algorithm. Therefore, SEBAL algorithm had better estimation than the EEFLux algorithm.

Agricultural water management studies require accurate information on actual evapotranspiration. This information must have sufficient spatial detail to allow analysis on the farm or basin level. The methods used to estimate evapotranspiration are grouped into two main groups, which include direct methods and indirect or computational methods. Basics of the indirect methods are based on the relationship between meteorological parameters, which impedes the use of these data with a lack or impairment. On the other hand, this information is a point specific to meteorological stations, and their regional estimates are another problem of uncertainty of their own. To this end, the use of remote sensing technology can be a suitable approach to address these constraints. Real evapotranspiration can be estimated by satellite imagery that has short and long wavelengths and is estimated using surface energy equations. Examples of such algorithms include SEBAL, METRIC, SEBS. Among the above mentioned algorithms, SEBAL and SEBS have been used. Among the factors of superiority of the SEBAL and SEBS algorithms, in comparison with other remote sensing algorithms, is a satellite imagery analysis algorithm based on physical principles and uses satellite simulation and requires minimum meteorological information from ground measurements or air models.

The exact estimation of evapotranspiration has significant importance in the programming of irrigation development and other distribution systems and water usage. Since the main user of water in the country is the agriculture sector, therefore, the exact estimation of plants’ water demand has been adverted extensively. The assessment methods of reference evapotranspiration are classified in two types of direct and indirect. The calculation of reference evapotranspiration in scientific and in vitro form and with high accuracy is possible by using lysimeter but in comparison to the indirect methods that are based on the climatic data of weather stations, the use of lysimeter is unfortunately inefficient. This is not just for the time consuming and high cost of lysimeter but it is for the limitation of weather stations and spottiness of the estimated values; in this way it is not possible to expand the obtained results to the large scale. Remote sensing is an authentic technique for the assessment of evapotranspiration in large scale which do not consume much time and money. The existence of different satellites by having different spatial and temporal resolution, redouble the importance and usability of this technique

Actual evapotranspiration assessment in the region were done based on SEBAL, SSEB and TSEB algorithms on 46 imageries of MODIS, seven imageries of Landsat7 (ETM+) and seven imageries of Landsat5 (TM) in years of 2001-2003. Multiplicity of imageries of MODIS show the proper time resolution of this sensor and is a reason for less errors in the assessment of reference evapotranspiration. In the evaluation of the three algorithms of SEBAL, SSEB and TSEB in the three satellites.

In the evaluation of the three algorithms of SEBAL, SSEB and TSEB in the three satellites, MODIS shows the least errors (respectively, RMSE=0.856, 1.385 and 2.7 mm/day), then Landsat7 is placed in the second class by having higher spatial resolution (respectively, RMSE=1.042, 1.56 and 2.76 mm/day) and Landsat5 has the highest errors (respectively, RMSE = 1.14, 1.97 and 3.06 mm/day). NDVI was found at the lowest amount in the beginning of cultivation period because of germination and sparseness of vegetation, and increase respectively by increasing temperature and crop canopy. L factor has a significant importance in the assessment of SAVI which is related to the area crop coverage percentage. Amount of L has been estimated as L=0.6 that has the least errors in comparison to the others.

In this study, the proper amount for L factor in estimation of the SAVI amount was about 0.6 which was based on the investigations on soil correction factor, the results of statistical indexes and the type and dispersal of vegetation in the region. The accuracy estimation of evapotranspiration of two single-source algorithms of SEBAL and SSEB and one two-source algorithm of TSEB in Bushehr province were evaluated. SEBAL algorithm presented more exact results based on statistical indexes among two single-source algorithms and the obtained results in 95% level of this algorithm showed significant differences with lysimetric measurements. This algorithm was chosen as the premier algorithm in the region. Two-source algorithm of TSEB showed the highest amount of errors. Satellite imageries by having higher spatial resolution estimated evapotranspiration with higher accuracy, the reason of which is proper choosing of cold and hot pixels. Although, because of having proper time resolution and variation of image numbers and also presenting of more time series in comparison to ETM+ and TM, MODIS was more adverted. ETM+ which is located on Landsat satellite was lied in the second place because of its resolution and having higher spatial resolution.

In this research, the spatial distribution of evapotranspiration and its relationship with remote sensing in contrast with lysimetric data as control was investigated in Arak, Markazi province in Iran. For estimation of actual evapotranspiration amount in the region based on SEBAL, SSEB and TSEB algorithms, 28 imageries of MODIS and Landsat7 (ETM+) were used for the years of 2000-2004. The multiplicity of MODIS images and its high temporal resolution is the reason of least error for ET estimation. According to the statistical results, the SEBAL model with the lowest RMSE in both TERRA and ETM + sensors (0.97 and 1.38 mm/day) was presented as the superior model in the region. Also, TSEB model showed the weakest results among the proposed models, in both MODIS and ETM + sensors (3.57 And 2.53 mm per day). Comparing the performance of two sensors, the ETM+ satellite images are recommended for ET estimation due to increased spatial resolution and improved resolution of images in the Landsat satellite. In addition, the NDVI vegetation index was at its lowest level at the beginning of the growing period due to germination and vegetation thinness, and it is increased by increasing air temperature and vegetation cover. L factor has a significant effect on SAVI and ET estimation and it is depended on the region vegetation. In this study, the L factor for the studied area was estimated to be 0.6 during the maximum growth period, which had the least amount of error in comparison with other values.

The main consumer of water resource in the country is agriculture sector – AS a result, the largest amount of water losses is related to this sector one of the most important processes of agricultural water losses related to real evapotranspiration because during the evapotranspiration process (ET) , a large volume of water of surface soil transferred transpiration ally and also sub soil water is transferred through the vegetation and real transpiration to the atmosphere. In this research using the algorithm SEBAL and MODIS indicator the real evapotranspiration rate was evaluated in Sodlaneh village of Quchan in 2011.Monthly results indicate the highest evapotranspiration was related to the months of April, June, May that with average of 17, 21, 26 mm was topped up respectively. High levels of evapotranspiration in spring due to land use map of the area which dominates annual evaporation and transpiration of the Sodlaneh village is calculation this year as 260mm that its Maximum equal with 644 mm is related to eastern part of the village (such as villages Dadanloo, Dizadiz) and and at least equivalent to 102mm is related to the central and western parts of the village (such as village of davoodli and dollo). The results of this study with the land data of Sodlaneh village shows that there is a significant correlation.

The algorithm of Sufrace Energy BALance (SEBAL) is one of the most widely used methods for determining actual evapotranspiration through remote sensing. However, assuming a linear relationship between temperature difference (dT) and land surface temperature (LST) and the visual identification of hot and cold pixels are the weak points of the method. Modified SEBAL (M-SEBAL) on the other hand, replaces two new concepts as hot and cold edges, with a significant impact on increasing the accuracy and automation of the execution of the algorithm and removing the need for skilled user. Although, determining the warm edge requires high computing and thus increase in computing time and possible errors. In this study, a simplified form of the M-SEBAL (SM-SEBAL) is developed and the results of executing of the three method on MODIS images and comparing the results with the observed data are shown. Results show that SEBAL overstimates the ET values and M-SEBAL and SM-SEBAL underestimate the same values. The highest sensible heat flux calculation error comes from M-SEBAL (33.87%) and SEBAL shows the lowest error rate (11.23%). The highest error in the calculation of the latent heat (LE) retrives from M-SEBAL (11.66%) and the lowest error rate retrives from SEBAL (5.05%). However, the maximum daily evapotranspiration observed error was for SEBAL (3.56%) and the lowest error was retrived from SM-SEBAL (1.81%) and then M-SEBAL (2.47%). In addition, the time required to perform SEBAL algorithm is up to four times more than the time needed to run other two algorithms.

Reference Evapotranspiration is a complex and multivariate phenomenon that depends on several factors and the most accurate way to estimate is lysimeter, though it is costly and time-consuming. Therefore, the main objective of this study was to estimate actual evapotranspiration based on single-source energy balance, i.e. SEBAL and SSEB, and two-source energy balance algorithm, i.e. TSEB, in three sensors MODIS, ETM and OLI & TIRS in three steps. In evapotranspiration estimating by SEBAL, Soil Adjusted Vegetation Index and the correction factor for soil effects (L) are particularly important. For this purpose, this index was used as calibration coefficient that is selected based on percentage of vegetation coverage. According to the results, the actual evapotranspiration with L calibrated (L=0/5) had lower error in each of the three sensors (RMSE=1/76, 0/84, and 1/49 mm/day). For verification of calibration results, 30% of the remaining lysimeter data was used. The results of the statistical indices showed significant difference between the predicted data at the 95% level and also in the predictions. Finally, by comparing the three algorithms in the three sensors i.e. MODIS, ETM , and OLI & TIRS, SSEB algorithm in ETM sensor was introduced as the best algorithm in Qazvin plain area, at 95% significance level and RMSE of 0/41 mm/day.

Actual evapotranspiration (ETa) estimation is a main factor for irrigation scheduling, hydrological balance, design and management of irrigation systems and crop yield prediction. Therefore many researchers have been tried to achieve ETa in different spatial and temporal scales. Remote sensing is one of the newest methods that can be used for this purpose. Therefore, In present study, spatial distribution of daily Eta at a part of Mashhad plain was estimated using Landsat 8 satellite images on 9 June, 25 June, 28 August and 13 September 2013. For this purpose, soil adjusted vegetation index (SAVI) was optimized for SEBAL algorithm. The results showed that there was a correlation between normalized difference vegetation index (NDVI) and ETa in study events. Also, standard deviation of SAVI was the least for L=1 (correction factor of soil background effects). Therefore it can be concluded that optimum amount of L for study region is one. Also the trend of ETr was similar to ETa. It can be concluded that plant cover in agriculture lands had no limitation for accessible of water in study region.

Determination of evapotranspiration in broad zones and within appropriate time intervals is an efficient tool for optimal management of water resources. SEBAL model is one of the algorithms, being paid much attention to in estimating evapotranspiration by use of satellite images. In this paper, we estimated the actual rate of evapotranspiration was estimated by use of fifteen elected images of MODIS sensor, belonging to year 1386, with the obtained results being compared with ground data recorded through region lysimeters. These studies were carried out in an area located on the Shahid Bahonar University farm (latitude and longitude of 30˚15˝N and 56̊58˝E) equipped with electronically weighing lysimeters and other meteorological parameter measuring devices. The acceptable results of RMSE, NOF and EF were obtained respectively about 0/62419, 0/09079 and 0/87636. Also, coefficient of determination (R2) of 0/71 between the observed and computational data was indicative of the fact that, this algorithm benefits from a high accuracy in estimating actual evapotranspiration. Following an estimation of the parameters of the Earth's surface temperature, albedo, vegetation cover indices, surface emissivity and net radiation (according to the separation of lysimeter pixel in the region), spatial distribution map of the daily evapotranspiration in the area was drawn.