The aim of this study was to evaluate the remote sensing algorithm (SEBAL) for estimating actual evapotranspiration in Vardij area in Tehran province. For this purpose Landsat 8 satellite images on June 10th, July 12th, August 13th and September 14th, 2018 and July 15th, 2019 were used. The reference evapotranspiration value (alfalfa plant) was calculated using evapotranspiration obtained from the SEBAL algorithm for the three points where alfalfa was previously cultivated. Using the meteorological station data, reference evapotranspiration was estimated by the experimental methods of FAO Penman-Monteith, Penman-Wright, Hargreaves Samani and Blani Cridel. The values obtained from SEBAL algorithm were compared with the mentioned methods and it was observed that in the study area the Hargreaves Samani method (MAE = 0. 472 and RMSE = 0. 62) was closer to the remote sensing method. Next in rating is the FAO Penman Monteith method (MAE = 1 and RMSE = 1. 26). Finally, the evapotranspiration obtained from SEBAL algorithm was compared with the value obtained from portable lysimeter and the results showed good correlation, so that absolute difference value was 0. 81 (mm/day), and it can be concluded that the remote sensing method is suitable for estimating evapotranspiration in the study area.

With the increase in world population، industrialization and improvement in the standard of living، there has been a continuous increase in consumption of energy. In the recent years، a new resource of energy، gas-hydrates، is drawing worldwide attention. Detection and identification of suitable areas of shallow geothermal energy، using remote sensing data is one of the new methods in many applications. In areas of anomalously high heat flow، geothermal systems transfer heat to the Earth’ s surface often forming surface expression such as hot spring، heated ground، and associated mineral deposits. Geothermal systems are increasingly important as sources of renewable energy، or as natural wonders of protected status attracting tourists، and their study is relevant to monitoring deeper magmatic processes. Thermal infrared (TIR) remote sensing provides a unique tool for mapping the surface expressions of geothermal activity as applied to the exploration for new geothermal power resources and long term monitoring studies. Airborne and space borne TIR data supports long-term monitoring of geothermal systems by providing a rapid and repeatable method of inventorying surface geothermal features. In addition، methods for relating the temperatures of surface geothermal phenomena to estimates of near-surface heat loss provide important inputs to the monitoring of geothermal activity and as applied to geothermal resource assessment and modeling. A geothermal resource can be simply defined as a reservoir inside the Earth from which heat can be extracted economically (cost wise less expensive than or comparable with other conventional sources of energy such as hydroelectric power or fossil fuels) and utilized for generating electric power or any other suitable industrial، agricultural or domestic application in the near future. Geothermal resources vary widely from one location to another، depending on the temperature and depth of the resource، the rock chemistry and the abundance of groundwater. Utilization of geothermal resources can broadly be classified into electric power generation and non-electric use. The type of the geothermal resource determines the method of its utilization. This research is based on applications of remote sensing as a decision support system that focused on the exploration of geothermal energy and environmental management. The aim of this study is to identify suitable areas for Shadow geothermal energy detection by integrating of land surface temperature (LST) anomalies and the energy flows of surface energy balance algorithms for land (SEBAL) algorithm using data LDCM data، ، has been evaluated and analyzed in the North West of Iran. To this end، and because of at least the effect of solar radiation، two examined the scenes of LDCM data was used for dates October 13، 2016. Then، using two single-band algorithms (Radiative Transfer Equation (RTE) and SCJM&S) to calculate the LST and the LST anomaly maps of were identified. In addition، using the SEBAL Algorithm was calculating the amount of net radiation received by the Earth's surface (Rn)، the amount of heat flow between the different layers of soil (G) and the amount of radiation absorbed by the solar surface (Rsolar). By assessing and combining this information layers with the LST anomaly maps the shadow geothermal prone areas were identified and determined. The results showed that the areas between the cities of Marand and Tasuj as well as between Gator and Khoy cities prone shadow geothermal areas، the existence of large natural spa in the region، the possibility of geothermal resources increases and this is confirmed. Also، similar results were obtained in areas south of the city of Urmia and west of Oshnavieh. These obtained areas have the maximum distance that the location of energy consumption (in Urmia، Khoy، Marand، Tasuj، Sharafkhaneh and Oshnavieh) equal to 30 km، which is economically justified and it can provide a large part of the clean energy used in industry and cities and brings a healthy environment.

The world has finite water resources, which are under increasing stress as the human population and water demand per capita both increase. These problems are not new but are now becoming more widespread and their impacts more devastating. This has provided additional impetus for the search for solutions to problems arising from the mismatch between demand and supply in terms of water quantity, quality and timing. Increasing water productivity has been identified as one of the global challenges that require urgent attention.Agricultural water productivity (WP) between water and food politicians is so important on watershed scale. This concept is a measure of performance expressed as the ratio of dry biomass to actual evapotranspiration in watershed. Remote sensing techniques which used mostly in research on last decade make its use more necessary. This research was conducted on farms which are located on lower part of Qazvin irrigation network. Evapotranspiration and dry biomass were determined using MODIS images in SEBAL algorithm. In this study, five cloudless images were selected which coincided to lysimeter data. Then ENVI model and ILWIS software were used in SEBAL execution of images. Results obtained from of SEBAL algorithm with five images was compared with lysimeter data in each of five days (R2=0.80). This correlation indicates good agreement between SEBAL estimated WP directly derived from MODIS images and lysimeter data. SEBAL estimated WP in Qazvin plain w/o crop consideration was ranged from 0.12 to 1.3 kg/m3. This value for wheat as an indicator in Qazvin plain was 0.76 kg/m3 by SEBAL and 0.9 kg/m3 by lysimeter.

Estimating actual evapotranspiration in river basins is necessary to use water resources optimally and to improve river basin management. SWAT hydrologic model and SEBAL remote sensing algorithm are among the known methods which have addressed this issue. In the present study, in the first step, the actual evapotranspiration of Karkheh river basin was estimated in dry, normal, and wet years (2008, 2012, and 2015, respectively), using the SWAT model calibrated based on runoff and crop yield and SEBAL algorithm. SWAT model was calibrated and validated using six hydrometric stations for the periods of 1993-2009 and 2010-2013, respectively, in which the 𝑅 2, NS and RMSE values were obtained between 0. 45 to 0. 7, 0. 52 to 0. 67 and 12. 64 to 25. 02 (m 3 /s) for the calibration period and between 0. 4 to 0. 6, 0. 3 to 0. 56 and 11. 08 to 23. 17 (m 3 /s) for the validation period, respectively. Further, the average observed and simulated yield of the strategic crop (wheat) in the basin were equal to 4. 70 and 5. 01 (ton/ha), respectively. In addition, the results of SEBAL algorithm and SWAT model were compared together based on the water year status, which the correlations between the results of those methods were equal to 0. 74, 0. 60, and 0. 52 for normal, dry, and wet years, respectively. In the second step, based on the ground data and MODIS, which has a suitable temporal resolution, and OLI which has a suitable spatial resolution, the results of SEBAL algorithm and the variation ranges of main parameters are presented for Pole-dokhtar and Ravansar plains. Ravansar plain has more cultivation areas and lower topography changes compared to Pole-dokhtar plain. The simulation of crop yield by SWAT gave a better result in Pole-dokhtar plain. Based on the results of this study, the values of evapotranspiration obtained from SEBAL algorithm and SWAT model can be reliable and close to the actual values of evapotranspiration in the river basin.

Extended abstractIntroductionSegzai plain, 40 kilometers from Isfahan city, with an area of about 40,000 ha, is considered a serious threat to this historical city. This plain, which until a few decades ago was a relatively prosperous reed and meadow, has now become a huge danger in terms of nature destruction and environmental pollution. Two natural and human factors play a role in the desertification of this region. Among the natural factors are low rainfall, high evaporation, the presence of limiting layers in the soil and strong winds and from human factors, excessive grazing and overgrazing of livestock as well as bush-cutting, rapid population growth and excessive exploitation of existing resources decline Underground water and most importantly, exploitation of surface mines, especially gypsum mines, can be mentioned. The main goal of this research was to evaluate the effectiveness of the SEBAL model for estimating the actual evaporation and transpiration of the Segazi Plain, considering the arid and semi-arid location of the region using the landsat 8 image. Materials and methodsTo do this research, first, landsat 8 images were processed. Extraction of required information from satellite images in this research was done during three main stages, i.e. pre-processing, processing and post-processing. In other words, in the pre-processing stage, after performing atmospheric, geometric and other necessary corrections, the image was referred to the ground. In the area of data processing, different highlighting methods and statistical analyzes and remote sensing were done in order to achieve the information layers of the plan. In order to evaluate the results in the image processing stage, the post-processing of the data based on various analyzes was used to evaluate the reliable layers in terms of accuracy and precision. After that, the SEBAL algorithm was implemented.  first the amount of net radiation (Rn) was calculated according to the temperature of the earth's surface and vegetation and the amount of energy reaching the earth, then the heat flux of the soil (G) was obtained to determine the amount of transfer capability The heat into the soil was determined, then it was determined to calculate the amount of sensible heat flux (H), which determines the loss of energy from the soil to space. Finally, after determining the sensible heat flux, evaporation and transpiration were calculated. The SEBAL algorithm calculates the energy balance equation in order to calculate the actual evaporation and transpiration of the plant. Results and discussionSurface albedo parameters (the highest and lowest weighted values are around 0.85 and 0.16), soil surface temperature (the highest and lowest weighted values are around 326 and 299 degrees Kelvin), NDVI vegetation index (the highest and lowest weight values related to areas with good vegetation close to +1 and related to water and water bodies close to -1), the amount of net energy reaching the surface of the earth (the highest and lowest weight values are about 703 and 210 Wm-2, soil heat flux (the highest and lowest weight values are about 130 and 35 Wm-2), sensible heat flux (the highest and lowest weight values are about 323 and 23 Wm-2 , momentary evaporation and transpiration (the highest and lowest weight values are about 0.842 and 0.225 mm) and daily transpiration evaporation (the highest and lowest weight values are about 20.2 and 5.4 mm) are among the most important effective parameters in this Sabal algorithm which were investigated in this research. Changes in actual transpiration evaporation (the highest weight values about 0.85 mm and the lowest weight values about 0.16 mm). The obtained results showed that the SEBAL model has well predicted evaporation and transpiration in areas that have vegetation, mostly agriculture and gardens, so that the amount of water loss through evaporation has been predicted close to the values found in the eastern synoptic station of Isfahan (airport Shahid Beheshti) is registered. ConclusionThe amount of error obtained in SEBAL calculation was 0.1%. The amount of real momentary evaporation and transpiration has been calculated in the range between 0.22 and 0.84 mm, according to the weather conditions of the region and the temperature of the air near the surface (27 to 50 degrees) and the amount of evaporation and transpiration recorded by the Penman-Monteith equation (30.0 mm in the east of Isfahan synoptic station), this value is in a reasonable range. Comparing the outputs of Sabal model with the amount of evaporation and transpiration obtained in the same station, which shows the root mean square error (RMSE) value of 0.1, indicates the suitability of this algorithm in calculating evaporation and transpiration in Segazi region. Considering the growing need of the country to prevent the wastage or excess consumption of water in the agricultural sector, either through changing the cultivation pattern or changing the irrigation methods, the application of the developed tool of the Sabal algorithm in this research can provide valuable information to the experts and managers of the water sector put agriculture. The results obtained from this implementation of this research showed that remote sensing has a good potential for estimating actual evapotranspiration (ETA) by having different algorithms such as SEBAL algorithm and minimum ground information.

Background and objectives: One of the key stages in water management involves accurately estimating water budget components. Proper estimations of the plant ET and water requirements of plants are very important for improving water management and increasing the water consumption efficiency. Although ground-based ET measurement methods provide high-accuracy point measurements, regional ET maps are needed for monitoring water resources. In this regard, satellite ET estimation models such as SEBAL can be useful. Of course, the efficiency of this model is different in various climates and crops. Therefore, The aim of this study is to calculate ET rates using the SEBAL model with Landsat 8 satellite imagery On the Google Earth Engine platform and assess the model's accuracy against FAO–Penman-Monteith method (ET0) and crop evapotranspiration (ETc).Materials and methods: This study was conducted in Jangah area of Torbat-e Jam city located in Razavi Khorasan province, from 2013 to 2023. A Java program was developed using the provided equations in Google Earth Engine for this algorithm. Daily evapotranspiration images were acquired for the study area, and evapotranspiration data were extracted using QGIS software. The prediction performance of the SEBAL model against the reference ET0 and ETc was evaluated using widely accepted statistical indices such as the correlation coefficient (CC), relative bias (RBIAS), root mean squared error (RMSE), and mean absolute error (MAE).Results: Results revealed a strong correlation between the model and ETc estimates (R²=0.85). The model slightly overestimated daily total ET values by only 0.016 mm (positive bias). Validation of the model against ETc indicated relatively minor errors, with daily mean absolute and root mean square errors of 0.76 mm and 0.97 mm, respectively. Conclusion: The growing accessibility of open-access satellite data and advancements in remote sensing technologies are opening the door to systems capable of monitoring water usage by different stakeholders in near-real-time across various spatial scales. In this regard, satellite ET estimation models such as SEBAL can be useful. Of course, the efficiency of this model is different in various climates and crops. Based on the research findings, it was observed that the SEBAL method calculates actual evapotranspiration values with acceptable results. These results indicate that the use of this method can be suitable for the studied area. In summary, the findings indicate that the SEBAL algorithm is a suitable approach for estimating crop evapotranspiration and can serve as an effective tool for water resource management in farms, and other similar contexts.

Evaluation of evapotranspiration is one way to prevent water loss and to manage water resources. Therefore, in this study, an attempt has been made to calculate the actual evapotranspiration rate in the east of East Azerbaijan province using the SEBAL algorithm. For this purpose, first, based on two Landsat 8 satellite images dated 2017/08/22 and 2017/08/09, the values of Net radiation, soil heat flux, and sensible heat flux are estimated. Then, based on the difference, the amount of instantaneous heat flux was calculated and a 24-hour evapotranspiration was obtained for each image. Finally, the amount was compared with the values obtained from the Penman-Monteith method. Also, for processing and analyzing images ENVI4. 8 software was used. The results indicated that the amount of evapotranspiration in the Penman-Monteith and SEBAL method on 2017/08/22 was about 6. 15 and 7 mm per day, and on 2018/08/09, respectively, about 7. 38 for Penman-Monty and 7. 94 mm per day for SEBAL. Overall, the amounts of SEBAL actual evapotranspiration and Penman-Monteith potential evapotranspiration have a mean absolute difference (MAD) 0. 705 mm per day which indicates that the estimated values are consistent with the SEBAL algorithm and the Penman-Monteith method.

Accurate quantification of ET in irrigated agricultural lands is crucial for planning for water allocation, optimizing crop production irrigation management, evaluating the effects of changing land use on water yields. For this purpose in the research, evapotranspiration calculated by three kind of different methods remote sensing, agro-hydrological model and computational method for maize field located in Neyshabour plain. Methods of evapotranspiration determination consist of Surface Energy Balance Algorithm for Land (SEBAL) algorithm and Modis product satellite in the long of during growth, SWAP agro-hydrological and computational methods of FAO Penman-Monteith and Hargreaves-Samani and FAO Blany Criddle. Correlation coefficient 0.67 to 0.91 between SEBAL algorithm with SWAP model and computational method showed high potential for SEBAL algorithms in the evapotranspiration estimation. In between methods forpotential evapotranspiration determination, FAO Blany Criddle method has better result in comparison with Penman-Monteith and Hargreaves-Samani methods. Also, this study found that the SEBAL algorithm could be used to determine evapotranspiration in areas with shortages of data and for evaluation of computational methods and hydrological models.

Background and Objective: Over the past 100 years, the country has lost about 90 percent of its per capita renewable water. About 90% of the country's renewable water resources are allocated to the agricultural sector. With the increase in the area of pistachio orchards and the increase in demand for water on the one hand and the limited water resources in the region, on the other hand, the imbalance between supply and demand for water is sharply increasing. In this regard, the most important step to prevent water loss is the uniform distribution of water on the field, optimal at each stage of growth. About 99% of the water absorbed by the plant is used for evapotranspiration. Therefore, studying this phenomenon can play an important role in determining the water needs of plants. It is difficult to measure the actual evapotranspiration outside the laboratory. Many experimental methods have been developed to estimate actual and potential evapotranspiration using meteorological and climatic data. But most of these methods are only able to estimate potential evapotranspiration and do not estimate the actual amount of it. In contrast, remote sensing methods have been developed that are a good solution for estimating the actual evapotranspiration. Satellite imagery with global coverage and repetitive Acquisition has made it possible to monitor evapotranspiration at the field level and during plant growth. Various studies have been conducted to estimate the actual evapotranspiration of agricultural areas using satellite images, which indicate the acceptable accuracy of these methods. However, most of this research is related to agricultural fields and no significant research has been done to estimate evapotranspiration at the orchards. Vegetation at the farms is uniform and homogeneous compared to orchards, so the estimation of vegetation index, which is one of the inputs of the SEBAL model in orchards is more difficult than agricultural fields, which can affect the final accuracy. Therefore, the main purpose of this study is to estimate the amount of evapotranspiration in the pistachio orchard using the SEBAL algorithm and evaluate the accuracy of estimation. Also, this research has been Materials and Methods: The present research has been carried out in pistachio orchards in Zarandieh city of Markazi province. The gardens had three different irrigation systems including flood irrigation systems, surface, and subsurface drip irrigation systems. Actual evapotranspiration is estimated using water balance and SEBAL algorithm. Meteorological data from Imam Airport Synoptic Station and Landsat8 satellite imagery has been used to estimate evapotranspiration using the SEBAL algorithm. Actual evapotranspiration is estimated at satellite overpass times during the growing season. To select hot and cold pixels in the SEBAL algorithm, the semiautomatic method proposed by Oldmo is used, which minimizes user participation in the selection of hot and cold pixels. To evaluate the accuracy of evapotranspiration estimation, the information of soil moisture sensors in the orchard has been used. 28 sensors measure soil moisture in different parts of the orchard. Using the soil moisture values, the actual evapotranspiration was estimated using the water balance method and used as a reference value. Results and Discussion: A comparison of the results of the SEBAL algorithm and water balance method showed that the SEBAl algorithm was able to estimate the actual evapotranspiration in different parts of the orchard with an RMS error of 0. 57. In addition, the correlation between the values estimated by the two methods was equal to 0. 82, which indicates the appropriate capability of the SEBAL algorithm in estimating evapotranspiration values. The correlation between the actual evapotranspiration estimated from the SEBAL model and the reference evapotranspiration is 0. 76. In addition, in the research, changes in the evapotranspiration in different parts of the garden and also gardens with different irrigation systems including flood, surface, and subsurface drips have been investigated. The results show that the orchard with subsurface irrigation had the lowest average of evapotranspiration on different dates. Considering that evapotranspiration is equal to the sum of evaporation from the soil surface and transpiration from the plant, this decrease can be attributed to the decrease in evaporation from the soil surface. In addition, evapotranspiration heterogeneity can be observed in all parts of orchards with the same irrigation system on all dates. For example, in the orchard with a flood irrigation system, parts of the garden show low evapotranspiration, which can be due to the lack of smoothing of the surface and lack of proper moisture in these areas. Obviously, the same amount of moisture accumulates in other parts of the garden and is inaccessible through deep percolation. This uneven distribution is also observed in the garden with a surface drip irrigation system. For example, the middle part of the garden with surface drip irrigation always shows a higher amount of evapotranspiration, which can indicate the loss of water in this part, due to the miss-operation of the dripper. To evaluate the difference in evapotranspiration in different irrigation systems, the average, minimum, maximum, and standard deviation values of evapotranspiration in orchards related to three different irrigation systems have been calculated. The results showed that in all dates, the ranges and standard deviation of evapotranspiration in the flood irrigation system were higher than in other systems, which indicates the lack of uniform irrigation in the orchard. Also, on all dates, the average amount of evapotranspiration in the orchard with a surface drip irrigation system has been more than flood irrigation system. Vegetation in orchards with drip irrigation systems (surface and subsurface) was denser compared to the flood irrigation systems. Conclusion: In this study, the actual evapotranspiration of pistachio orchards has been estimated using satellite imagery and the SEBAL algorithm. The results of the study indicate the appropriate accuracy of the SEBAL algorithm in estimating the actual evapotranspiration of the orchards. Compared with the water balance method, the correlation coefficient was 0. 82 and the root means the square error was 0. 57. In addition, comparing the moisture situation in different parts of the orchard and in orchards with different irrigation systems has shown that by estimating the actual evapotranspiration using satellite imagery, appropriate information can be obtained on how to distribute moisture in the garden. This information provides valuable information on the optimal management of water resources and increases irrigation efficiency. Other results of this research include the significant difference between surface and subsurface drip irrigation methods. The results show that using subsurface irrigation methods can effectively reduce irrigation water loss due to evaporation from the soil surface. The results show that in areas where there is no access to information from soil moisture sensors or direct measurements of evapotranspiration, the use of the SEBAL algorithm and remote sensing methods can provide appropriate information for optimal water management.

Rangelands are the most important plant ecosystems in Iran that have multiple and vital roles in economic stability and food security in the country. In recent decades, with increasing population, increasing forage consumption, climate change and rainfall fluctuations, most of the country's pastures have been destroyed or faced with a degradation trend. Therefore, in order to strengthen this vital ecosystem in the country, rangeland management components have a special priority and importance. One of the main components of rangeland management is the study on the water requirement of rangeland plants. The water required by the plant is equivalent to its evapotranspiration. Estimation of evapotranspiration using meteorological station data can be done at different time intervals,But determining its spatial distribution on a large scale is not possible. Remote sensing techniques and evapotranspiration estimation algorithms based on the surface energy balance of the earth are among the methods that are able to produce maps with appropriate temporal and spatial coverage. In this study, 4 images of LANDSAT 5 processed (from June to July of 2010), and evapotranspiration of the Mahidasht region, Kermansh province, Iran were estimated. Then, Sentinel 2 images were used to identify the existing rangelands in the Robat Mahidasht region by Maximum Likelihood classification method. The evapotranspiration of the rangelands was obtained uing Surface Energy Balance Algorithm for Land (SEBAL) maps. Based on the results, it was observed that the difference of estimating the actual evapotranspiration between the SEBAL algorithm and lysimetric measurements was a maximum value of 9. 7%, which is acceptable. The coefficient of determination between SEBAL and lysimetric data was (R2=0. 99) and mean absolute difference was 0. 53 mm/day. The estimated evapotranspiration rates of rangeland cover at the four Landsat imaging dates were 2. 1, 3. 46, 3. 4, and 3. 44 mm/day. Also, the results showed less rangeland evapotranspiration than other coverings like forest and agriculture, which is due to the dryness of the topsoil profile and shallow depth of most of the rangeland plants, especially annuals species. It was concluded that SEBAL algorithm is a suitable method for estimating the evapotranspiration of rangeland cover with acceptable accuracy.