sebal algorithm

Evapotranspiration that includes evaporation from the soil surface and transpiration from vegetation, is one of the most important factors of water loss. Also, it is one of the most effective components of the water balance in a catchment in arid and semi-arid regions of the world. Therefore, it is an important physical parameter for water resource management and determining the plant water requirement in the agricultural sector. so far, many experimental methods have been proposed to calculate evapotranspiration, but, they are only suitable at the local scale and cannot be generalized to large areas due to regional dynamics and changes. whereas the accurate estimation of it is also very difficult and expensive, Therefore, in the present study, calculated the amount of evapotranspiration in the irrigated agricultural sector by using of landsat 8 satellite images and Surface Energy Balance Algorithm (SEBAL) in Nahavand Plain. in SEBAL algorithm by estimating all energy components on the earth's surface, including net radiation flux, soil heat flux, and sensible heat flux and using the energy balance equation, evapotranspiration is calculated. Remote sensing also has the ability to show evapotranspiration spatial distribution in addition to estimating the amount of its, because, it is the only technology that extracts factors such as surface temperature, albedo coefficient and plant index in a way compatible with the environment and is also economically affordable.

in this research, in order to estimate daily actual evapotranspiration of the irrigated agricultural and gardens of Nahavand Plain, extracted irrigated agricultural land use map by using of Sentinel 2 satellite images, Then, by using of Landsat 8 satellite images (13 images, from 13 April to 22 October during the growth period of the irrigated crops) and Surface Energy Balance Algorithm (SEBAL), evapotranspiration maps were obtained during the irrigated crops growth period in 2021. These Landsat 8satellite images are obtained by the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) onboard the satellites and are widely used for water resource applications. The OLI sensor has 9 bands and the TIRS has two bands (10th and 11th are the thermal bands). Landsat images are at intervals of 16-days with a spatial resolution of 30 m. In all images, the imaging time was 7:21. Then, due to FAO- Penman monteith method is one of the most important and reliable reference methods in evapotranspiration calculations, in this research, this method was used as a basis for evaluation and comparison. Finally, in order to evaluate the efficiency of SEBAL method in estimating the actual evapotranspiration of irrigated crops and gardens in Nahavand Plain used RMSE function (Root Mean Squares of Errors).

According to the results of the SEBAL algorithm, the highest mean of actual evapotranspiration was related to images 2021.09.04 and 2021.08.19 which fall in the middle of the growing period of irrigated crops. In addition, the surface albedo is noted to be relatively low for these days with the high NDVI values indicating high absorption of radiation by the vegetation during this period. Net solar radiation is directly contingent upon the incoming longwave and shortwave radiations, both of which directly influence the surface temperature. Therefore, areas with higher surface temperatures have higher net solar radiation. The net radiation flux has a direct relationship with NDVI, Greenness, and wetness parameters and is inversely related to albedo, Brightness, and Ts. The vegetative moisture and sensible heat flux are higher on days with high NDVI. Higher NDVI values are an indication of an increase in vegetation greenness, therefore essentially an increase in evapotranspiration is expected to be observed. The lowest mean of actual evapotranspiration is related to the northeast of the case study; due to lack of sufficient surface and ground water resources and consequently the reduction of agricultural lands in this region. Finally, in order to investigate the accuracy of SEBAL method in calculating evapotranspiration, compared the results of SEBAL method with the results of FAO- Penman monteith method. The results of this comparison showed that the SEBAL method with RMSE 0.82 has appropriate efficiency for estimating evapotranspiration.

Due to an increase in population and shortage of water resources, especially in the agricultural sector, researchers are looking for ways to better manage the available water resources. Evapotranspiration rate is one of the most important components of the global hydrologic cycle and has a significant influence on energy balance and climate. the using of indirect methods such as remote sensing can be an important step for estimating the water need of agricultural products, planning and management the country, s water resources. Therefore, according to the position of Nahavand city as the agricultural hub of Hamedan province, in this study, the actual evapotranspiration of the irrigated agricultural land use using of landsat 8 satellite images and SEBAL Algorithm was investigated in this area. According to the results of the SEBAL algorithm, the highest mean of actual evapotranspiration in all of the investigated images is related to the southeast and center of the studied area. that the reason of this matter is location of this area in the main branch of the Gamasiab River and focused the irrigated agricultural and gardens in this area. The final results of this research indicated high precision of SEBAL algorithm in estimating evapotranspiration. Thus, the high accuracy and low error indicate that the SEBAL method could be aptly used to estimate evapotranspiration on a regional scale, in the respective time range. Also, the results obtained from the SEBAL method assisted in understanding the spatial and temporal changes in different stages of plant growth.

The remote sensing technique is a suitable method for estimating actual evapotranspiration (ETa) at the large-scale due to spatial and temporal resolution. The present study aims to assess the ETa using the SEBAL and different algorithms to survey the effect of the LST and their impact assessment on the ETa fluctuation. Field measurement, including canopy temperature and the volume of inflow and outflow of water consumption was done based on lysimeters during 2018-2019. After the necessary pre-processing on the satellite images, the Land Surface Temperature (LST) was estimated using Planck's and split window algorithms. The result showed that the performance of Split window was better than to the Planck algorithm. Also, ETa was estimated by the SEBAL algorithm based on two temperature scenarios including the Planck and split window. The results showed, the cold pixel of SEBAL algorithm had compliance with the Lysimetric measurement. Moreover, the cold pixel of the split window algorithm with RMSE=0.56, NRMSE=0.084 and NS=0.992 (mm/day) had the highest consistency with the lysimeter data. Also, the rMBE index of the split window algorithm was associated with underestimation in the range of -4.07 to -3.22%, while the Planck function algorithm fluctuated with overestimation in the range of 4.76 to 12.65%. This research has been verified to the cold pixel of satellite for crop with no stress conditions and for better investigation at crop stress condition, precise instruments are needed.

Estimation of crop water requirement and evapotranspiration by lysimeter is costly and time-consuming and could not be applied to larger field scale. Remote sensing technology can overcome this limitation. The goal of this research was to estimate alfalfa actual evapotranspiration using satellite imagery and compare it with the in-situ measurement by lysimeter. The study was carried out from 2017 to 2020 in the agricultural lands of Alborz and Charmahal and Bakhtiari provinces employing Surface Energy Balance Algorithm for Land (SEBAL) method. Lysimeter has been implemented under standard conditions. The cold pixels of each satellite image were extracted to estimate net alfalfa crop water requirement. In-situ net crop water requirement for Alborz and Charmahal and Bakhtiari provinces were obtained as 1383 and 1087 mm, respectively. The coefficients of determination (R2) were 73% and 76%, respectively, for the two studied provinces. The statistical analysis showed that there were small deviations from the mean values. The standard evapotranspiration measurements using lysimeter were higher than the satellite estimations. This technique can be useful for the estimation of crop water consumption since it is simple, cheap, fast, and can be used for large areas.

Land surface temperature is a significant variable involved in land surface energy and water balance and is a substantial component in many aspects of environmental research. The land surface temperature is usually calculated based on thermal bands. Landsat 8 satellite thermal bands are the newest infrared thermal bands, included two adjacent thermal bands with a spatial separation of 30 meters. There are several methods for calculating land surface temperature. These methods are of three groups: Methods that only need satellite data, methods that require satellite data and leaf area index (LAI), and Methods that require satellite data and meteorological data. In this study, the land surface temperature simulated by the Planck Inverse Function, SEBAL algorithm, Statistical Mono-Window algorithm, Split Window Algorithm, Mono-Window Algorithm, Radiation Transfer Equation, Sabrino Split Window Algorithm, National Oceanic and Atmospheric Administration Joint Polar Satellite System, And the Single-Channel Algorithm and compared with the surface temperature measured in the LPT2 construction area of ​​Soleimanshah irrigation network during the growing season of nut sunflower in 2020 based on two criteria of R 2 and RMSE. The results showed the Planck Inverse Function, SEBAL algorithm Statistical Mono-Window algorithm, Split Window algorithm, and Mono Window algorithm respectively have high accuracy (Those approaches are not dependent on meteorological data). Among them, the Planck Inverse Function with values of R2 and RMSE of 0.6 and 4.2 ° C, respectively has the highest accuracy. The Sabrino Split Window algorithm, National Oceanic and Atmospheric Administration Joint Polar Satellite System, and the Single-Channel algorithm, respectively have low accuracy.

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.

Increasing population growth necessitates more food production, which requires more activities in the agricultural and industrial sectors, which has necessitated the presentation of effective strategies for water resources management. One of the strategies that has been considered in water resources management in recent years, especially in the agricultural sector, is the measurement or estimation of evapotranspiration that The monitoring and evaluation of changes in certain periods of time can be important in determining the amount of water consumed by the plant and planning irrigation, and thus determining the irrigation systems capacity. Also, Conventional ET measurements using lysimeters, the Bowen ratio, and eddy covariance systems (EC) are mainly based on site (field)-measurements. Although they can directly or indirectly measure turbulence fluxes (latent and sensible heat flux; LE and H), conventional techniques likely entail substantial observation errors during bad weather and other conditions. These methods, therefore, cannot represent large-scale terrestrial ET. Since direct and indirect methods of calculating this parameter have limitations; recently, remote sensing methods for calculating evapotranspiration are used. However, remote sensing data combined with some meteorological data provide a means to estimate regional ET, given the advances in remote sensing technology. Some land surface variables related to ET, such as surface albedo, surface emissivity, and land surface temperature, can be estimated directly by remote sensing data. Actual ET can be estimated by a set of equations hierarchically, which converts spectral radiances derived from satellites or airplanes images into actual ET. One of the models based on remotely sensed data is the Surface Energy Balance Algorithm for Land (SEBAL) model, in which the land surface temperature, albedo, emissivity, and normalized difference vegetation index (NDVI) are of significance to estimating regional ET. In the present study, Lenjan County in Isfahan province, which was under rice cultivation, was selected and Surface Energy Balance Algorithm for Land (SEBAL) was implemented on 8 satellite images of Landsat 8 from June to September 2017 (growing season). For this purpose, the main components of the energy balance equation, including net radiation flux, soil heat flux and sensible heat flux to the air for each image, have been calculated and the instantaneous evapotranspiration flux for each pixel is estimated as the residual energy balance equation. To improve the non-dependency on ground data, a general equation was therefore used. The Net Radiation is the electromagnetic balance of all incoming and outgoing fluxes reaching and leaving a flat surface. The amount of shortwave radiation (RS↓) that remains available at the surface is a function of the surface albedo (α). Surface albedo is a reflection coefficient defined as the ratio of the reflected radiant flux to the incident radiant flux over the solar spectrum. It was calculated using satellite image information on spectral radiance for each satellite and the incoming shortwave radiation (RS↓) was computed using the solar constant, the solar incidence angle, a relative earth-sun distance, and a computed atmospheric transmissivity. The incoming longwave radiation (RL↓) was computed using a modified Stefan-Boltzmann equation with atmospheric transmissivity and a selected surface reference temperature. Outgoing longwave radiation (RL↑) was computed using the Stefan-Boltzmann equation with a calculated surface emissivity and surface temperature. Surface temperatures were computed from satellite image information on thermal radiance. The surface emissivity is the ratio of the actual radiation emitted by a surface to that emitted by a black body at the same surface temperature. Soil heat flux was empirically calculated using vegetation indices, surface temperature, and surface albedo. Sensible heat flux wass computed using wind speed observations, estimated surface roughness, and surface to air temperature differences. Sensible heat flux is the part of internal energy of a substance that is proportional to the substance’s temperature. Also, empirical methods of evapotranspiration including Blaney- Criddle, Hargraves- Samani, FAO- Pennman- Monteith and Kimberly- Pennman were also evaluated in the study area. Accordingly, the SEBAL model in the study area has the maximum and minimum daily evapotranspiration in the pictures of June 25 and September 11, equal to 7.95 and 5.88 mm/day. Among the various parameters affecting the SEBAL algorithm, the net radiation flux parameter with consideration of other parameters such as vegetation indices, surface albedo, and incoming and outgoing of radiation, had the most effect on the results of SEBAL algorithm. Among different empirical methods, Hargraves- Samani and Blaney- Ciddle had less percent error which has a suitable correlation with SEBAL results. SEBAL algorithm has been able to estimate the temporal and spatial variation of evapotranspiration in the study area with an acceptable accuracy. Alternatively, this algorithm can be used to replace time-consuming and costly methods of calculating evapotranspiration at different surfaces.

Estimation of evapotranspiration is,one of the main components of the hydrological cycle, and is essential for irrigation scheduling, water balance, irrigation system design and management and crop yields simulation. Unlike conventional methods remote sensing estimates evapotranspiration in different spatial scales. Therefore, the aim of this study is to estimate evapotranspiration using OLI sensor images and SEBAL algorithm in the Dasht-e-Naz area, Sari, Iran. To evaluate the results, evapotranspiration estimated by the reference method, Penman-Monteith-FAO, was compared with SEBAL algorithm. Strong and significant correlation was obtained between these two methods (R2=0.93), Low error of estimation by SEBAL compared with reference method (RMSE=1.14) and low absolute difference between the SEBAL and Penman-Monteith-FAO (MAE=0.96) indicates that there is a good match between estimated values by SEBAL algorithm and Penman-Monteith-FAO standard method. This study showed that OLI sensor images and SEBAL algorithm could satisfactorily estimate actual evapotranspiration in the Dasht-e-Naz area, Sari.

Introductionin current situation when world is facing massive population, producing enough food and adequate income for people is a big challenge specifically for governors. This challenge gets even harder in recent decades, due to global population growth which was projected to increase to 7.8 billion in 2025. Agriculture as the only industry that has ability to produce food is consuming 90 percent of fresh water globally. Despite of increasing for food demand, appropriate agricultural land and fresh water resources are restricted. To solve this problem, one is to increase water productivity which can be obtain by irrigation. Iran is not only exempted from this situation but also has more critical situation due to its dry climate and inappropriate precipitation distribution spatially and temporally, also uneven distribution of population which is concentrate in small area. The only reasonable solution by considering water resources limitation and also restricted crop area is changing crop pattern to reach maximum or at least same amount of income by using same or less amount of water. The purpose of this study is to assess financial water productivity and optimize farmer’s income by changing in each crop acreage at basin and sub-basin level with no extra groundwater withdrawals, also in order to repair the damages which has enforce to groundwater resources during last decades a scenario of using only 80percent of renewable water were applied and crop area were optimize to provide maximum or same income for farmers.
Materials and methods The Neyshabour basin is located in northeast of Iran, the total geographical area of basin is 73,000 km2 consisting of 41,000 km2 plain and the rest of basin is mountains. This Basin is a part of Kalshoor catchment that is located in southern part of Binaloud heights and northeast of KavirMarkazi. In this study whole Neyshabour basin were divided into 199 sub-basins based on pervious study.Based on official reports, agriculture consumes around 93.5percent of the groundwater withdrawals in Neyshabour basin and mostly in irrigation fields, surface water resources share in total water resource withdrawals is about 4.2percent, which means that groundwater is a primary source of fresh water for different purposes and surface water has a minor role in providing water supply services in the Neyshabour basin. To determine crop cultivation area, major crops divided into two groups. two winter crops (Wheat and Barley) and two summer crops (Maize and Tomato). To accomplish land classification by using supervised method, a training area is needed, so different farms for each crop were chosen by consulting with official agricultural organization expert and multiple point read on GPS for each crop. The maximum likelihood (MLC) method was selected for the land cover classification. To estimate the amount of precipitation at each 199 sub-basins, 13 station data for precipitation were collected, these stations are including 11 pluviometry stations, one climatology station and one synoptic station. Actual evapotranspiration (ETa) is needed to estimate actual yield (Ya). Surface Energy Balance Algorithm for Land (SEBAL) technique were applied on Landsat 8 OLI images. To calculate actual ETa, the following steps in flowchart were modeled as tool in ArcGIS 10.3 and a spreadsheet file. To estimate actual crop yield, the suggested procedure by FAO-33 and FAO-66 were followed. Financial productivity could be defined in differently according to interest. In this study several of these definition was used. These definitions are Income productivity (IP) and Profit productivity (PP). To optimize crop area, linear programing technique were used.
Results and discussion average actual evapotranspiration result for each sub-basin are shown in context. In some sub-basins which there were no evapotranspiration are shown in white. And it happens in those sub-basins which assigned as desert in land classification. In figures 8 and 9 minimum amount of income and profit productivity for wheat and barley is negative, this number means in those area the value of precipitation is higher than value of evapotranspiration, so lower part of eq. 21 and 22 would be negative and in result water productivity would be negative. Since most of precipitation occurs during cold season of the year these numbers are expected. Two sub-basins of 43 and 82 has the value of negative, it means in these two sub-basins groundwater are recharging during the year 2014-2015.The maximum value of income and profit productivity belong to wheat and barley which are winter crops and mostly rain fed, so amount applied water would be so low and in result productivity increased. Among the summer crops maize has the most income and profit income which can be interpret due to their growing period and the crop types. Maize has around 110 days to reach to maturity and harvest, on the other hand tomato needs 145 days to harvest. Some plant is C3 and some are C4. C4 plants produce more biomass than C3 crops with same amount of water which leads to more productivity. The results showed that tomato should have the most changes in area reduction (0.2) and maize should have no changes in both scenarios. Crop area should reduce to 66percent of current cultivation area to maintain ground water level and only 6percent reduction in cultivation area would result in 20percent groundwater recharging.
Conclusion to save groundwater resources or even retrieve the only water resource, cultivation area must reduce if the crop pattern will not change. In this study only four crops were studied. It seems best solution is to introduce alternative crop.

Evapotranspiration of orchard crops is often determined by indirect method and based on meteorological data due to the difficulties inherent in direct measurement instruments such as installation of large lysimeters or precision equipment, and long growing seasons of tree crops. Evapotranspiration estimation with sufficient accuracy is not feasible due to spatial variability of meteorological parameters and, sometimes, due to inappropriate distribution of meteorological stations. Therefore, using methods based on remote sensing, which account for these variations, is much more desirable. In this research, evapotranspiration of olive trees at different phonologic stages was measured using direct and indirect methods in Tarom district of Zanjan Province. In the direct method, actual evapotranspiration was determined by measuring moisture balance components, whilst in the indirect one, it was specified with the help of satellite imagery, the SEBAL algorithms, and Penman-Monteith equation. Olive crop coefficient was subsequently calculated and evaluated by determining reference crop evapotranspiration. The results indicated that evapotranspiration calculated by the remote sensing method at different stages of the growth had acceptable conformity with soil moisture balance data and evapotranspiration values obtained from the Penman–Monteith equation (the respective correlation coefficients were 0.95 and 0.88) and both evapotranspiration curves along the growing season had a similar increasing and decreasing trend. Moreover, crop coefficient obtained by the SEBAL algorithm and the water balance methods were well correlated (R2=0.86) and the remote sensing method with the aforementioned advantages can be used in predicting evapotranspiration.

Accurate estimation of evapotranspiration plays an important role in quantification of water balance at awatershed, plain and regional scale. Moreover, it is important in terms ofmanaging water resources such as water allocation, irrigation management, and evaluating the effects of changing land use on water yields. Different methods are available for ET estimation including Bowen ratio energy balance systems, eddy correlation systems, weighing lysimeters.Water balance techniques offer powerful alternatives for measuring ET and other surface energy fluxes. In spite of the elegance, high accuracy and theoretical attractions of these techniques for measuring ET, their practical use over large areas might be limited. They can be very expensive for practical applications at regional scales under heterogeneous terrains composed of different agro-ecosystems. To overcome aforementioned limitations by use of satellite measurements are appropriate approach. The feasibility of using remotely sensed crop parameters in combination of agro-hydrological models has been investigated in recent studies. The aim of the present study was to determine evapotranspiration by two methods, remote sensing and soil, water, atmosphere, and plant (SWAP) model for wheat fields located in Neishabour plain. The output of SWAP has been validated by means of soil water content measurements. Furthermore, the actual evapotranspiration estimated by SWAP has been considered as the “reference” in the comparison between SEBAL energy balance models.
Surface Energy Balance Algorithm for Land (SEBAL) was used to estimate actual ET fluxes from Modis satellite images. SEBAL is a one-layer energy balance model that estimates latent heat flux and other energy balance components without information on soil, crop, and management practices. The near surface energy balance equation can be approximated as: Rn = G H λET
Where Rn: net radiation (Wm2); G: soil heat flux (Wm2); H: sensible heat flux (Wm2); and λET: latent heat flux (Wm2). Simulations were carried out by SWAP model for two different sites in Faroub and Soleimani fields. The SWAP is a physically based one-dimensional model which simulates vertical transport of water flow, solute transport, heat flow and crop growth at the field scale level. The period of simulation covered the whole wheat growing season (from 1st of December2008 to 30th of July2009. 16 MODIS images was used to determine evapotranspiration during wheat growing season. Inverse modeling of evapotranspiration (ET) fluxes was followed to calibrate the soil hydraulic. While SWAP model has the advantage of producing the right amount of irrigation and evapotranspiration at high temporal resolution, SEBAL can estimate crop variables like leaf area index, NDVI index, net radiation, Soil heat flux, Sensible heat flux and evapotranspiration athigh spatial resolution.
Actual and potential evapotranspiration were estimated for SWAP Model during the whole wheat growing season around669.5 and 1259.6 mm for Farub field and 583.7 and 1331.2 mm for Soleimani field, respectively. In contrast with NDVI and net radiation,spatial distribution of SEBAL parameters indicated that soil heat flux, sensible heat flux, and surface temperature of land have the same behavior. At the planting date, evapotranspiration was low and about 1 mm/day, but at the peak of plant growth, it was about 9 mm/day. Moreover, evapotranspiration declined at late growing season to about 3 mm/ day. SWAP model has been calibrated and validated with meteorological data and the data of field measurements of soil moisture. The amount of RMSE of 0.635 and 0.674 (mm/day) and MAE of 0.15 and 0.53 (mm/day) and also coefficient of determination (R2) of 0.915 and 0.964 obtained from comparison of SEBAL algorithm with SWAP model for Farub and Soleimani fields showed that no significant differences was seen between results of two models.
The present study supports the use of SEBAL as the most promising algorithm that requires minimum input data of ground based variables. Results of comparison of SEBAL and SWAP model showed that SEBAL can be a viable tool for generating evapotranspiration maps to assess and quantify spatiotemporal distribution of ET at large scales. Also, it feels that SEBAL and SWAP models can be applied in a wide variety of irrigation conditions without the need for extensive field surveys. This helps significantly in identifying performance indicators and water accounting procedures in irrigated agriculture, and to obtain their likely ranges.

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 for potential 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.

Spatial estimation of evapotranspiration (ET) rates is essential for agriculture and water resources management. This study aimed to estimate ET v an ET estimation algorithm called Surface Energy Balance Algorithms for Land (SEBAL) and also by using TM June 2009 satellite data in Damaneh region of Isfahan province. To calculate the ET, all the energy balance components and related parameters including net radiation, surface albedo, incoming and emitting shortwave and longwave radiation, surface emissivity, soil heat flux, sensible heat flux, NDVI vegetation index, Leaf Area Index(LAI), and surface temperature were extracted from the geometrically and radiometrically corrected TM images. Results showed that ET rate was about 7.2 mm day-1 in agricultural areas, which was almost equal to 6.99 mm day-1 extracted from the FAO Penman-Monteith method in the synoptic weather station of Daran. Results here indicate that the extraction of ET rate which is almost equal to plant water requirements from remote sensing data can be used in selecting appropriate plants for agriculture and rehabilitation purposes in extensive arid and semi-arid regions of Isfahan province where severe droughts and water shortage are major problems.

Accurate estimation of evapotranspiration plays an important role in quantification of the water balance at regional scale for better planning and managing water resources. Evapotranspiration can be obtained from either estimation of potential ET using data of meteorological stations or، directly، from field measurements. ET is subject to rapid changes in time and space، attributable to the wide spatial variability of precipitation، hydraulic characteristics of soils، and vegetation types and densities. Therefore، it is nearly impossible to determine its spatial and temporal distributions over large areas only from lysimeter and precise measuring instruments. Thus، researchers have used remote sensing data to estimate areal actual ET. In this study، actual evapotranspiration variations of Neyshabour plain was investigated by algorithm of energy balance of the earth since 2000 to 2013 by using MODIS images and meteorological data. Also، the determined evapotranspiration was compared and evaluated with Penman-Monteith and Hargreaves-Samani models. Low amount of coefficients of the error between Penman-Monteith model and SEBAL algorithm showed the accuracy of SEBAL model in the estimation of evapotranspiration and its parameters. The results derived from comparison of evapotranspiration and NDVI vegetation index indicated a good correlation between vegetation and evapotranspiration (R2=0. 908). Also، the variations of NDVI index، land surface temperature، and evapotranspiration in the studied fields showed that evapotranspiration increased by lower land surface temperature and higher densities of vegetation.

In the present research، irrigation management and water productivity on irrigation and drainage networks of Mirza koochak khan agro Industrial Company is evaluated by vegetation index and soil surface moisture maps resulted from SEBAL algorithm and TERRA-MODIS satellite images. In the first stage، reflection coefficient of ground surface، ground radiation coefficient، vegetation indexes such as NDVI، SAVI، GVI and BGR were determined by data preprocessing obtained from satellite images during growth period for 2 years of 1384 and 1385. Then cultivation area and density were estimated. Using the SEBAL algorithm، elements of energy balance on ground surface and Real evapotranspiration were computed and assumed as the water consumption and relative supply of water. Biological production of sugarcane obtained using the average daily solar radiation and fraction of photosynthetic absorbed radiation is determined in form of BGR index and assumed as a symbol of production. Water productivity is resulted from the ration of biological production to real evapotranspiration. Process of seasonal variation is gained for biological production، vegetation index and evapotranspiration. Studying of water supply and demand presents no lack of water in this network and the fraction of overflow is used for salt leaching in soil profile. Average water productivity for biological production in this network for two years of 1384 and 1385 is 4. 24 and 5. 32 kg/m3. In addition، relative water supply for these two years are 1. 67 and 1. 73 respectively. Results of this study، suggests the application of this method for water productivity evaluation in large scale irrigation and drainage networks.

Irrigation scheduling means applying the right amount of water at the right time. In this research, the current irrigation scheduling implemented in the Mirza-kuchak-khan Sugarcane Agro-Industry farms located in Ahwaz was compared with the actual sugarcane crop water requirement using SWAP model. Due to lack of field measured data, the SWAP model was calibrated and evaluated using LANDSAT 7 ETM+ images taken over the study area at twelve different dates during the sugarcane growing season. According to the results simulated by the SWAP model, the seasonal irrigation depth applied (2640 mm) was much more than that required by the crop and, as a result, a significant amount of irrigation water was wasted in the form of deep percolation. The water productivity was estimated to be 0.35 ton/ha.cm. In order to decrease deep percolation and improve water productivity, six irrigation plans including the possibility to reduce irrigation depth or eliminate some irrigation events, or change irrigation times were evaluated by the SWAP model. For every plan studied, an improved irrigation scheduling was suggested. According to the SWAP simulations, implementations of the suggested irrigation scheduling by eliminating some irrigation events or changing irrigation times can reduce seasonal irrigation depth by 27% and improve the water productivity by 30%. Reducing irrigation depth by 20% can also increase the water productivity by 26%. In case both scenarios mentioned can be applied together, the irrigation depth would decrease 42% and water productivity would increase 68%.

Precise estimate of evapotranspiration (ET) or crop water requirements crucial for improving water resources management and eventually increasing water use efficiency. Considering the vast varieties of crops cultivated on large scales, calculating precisely the crop water requirement from ordinary methods of estimating reference evapotranspiration is difficult and in some cases even impossible. Therefore, it is necessary to develop methods to compute crop water requirement in large areas with sufficient accuracy. One of such methods is to use remote sensing data which covers a much larger areas as compared with methods depending on field weather data. One of the most well known methods to compute actual ET from remote sensing data is SEBAL method (Surface Energy Balance Algorithm on Land). In SEBAL method, all fluxes of the energy balance at the earth's surface including net radiation, soil heat flux, and sensible heat flux are calculated from satellite images and finally actual ET is computed based on the energy balance at the earth's surface. It is intended to evaluate spatio-temporal variation of daily ET in Mirza Kochakkhan sugarcane Agro-Industry farms located in southern part of the Khosetan province using MODIS images for 10-day periods during a growing season in 2006-2007. The results obtained indicated that the SEBAL method was an efficient method to estimate actual ET during the growing season. Maps of actual ET generated by ERDAS and GIS software showed that fields which were well irrigated or had a good crop cover had the highest values of ET. However, actual ET decreased for areas with less crop cover or dry fields. The data obtained from the generated maps of actual ET were closer to the data obtained from the field with a coefficient of determination of R2=0.77.

Estimation of evapotranspiration (ET) is essential for agricultural water resources management. Usually it is estimated indirectly based on (daily) weather data at the weather stations because of difficulties in its direct measurement. Considering the spatial variations of the weather parameters and subsequently of ET، application of the methods which can consider these variations are interested. Developed algorithms to estimate ET based on satellite images are among those interested methods. Surface Energy Balance Algorithm for Land (SEBAL); a remote sensing-based technique for estimation of evapotranspiration has been used in some regions in the world. The aims of the current study were 1) to test the accuracy of estimated ET based on SEBAL algorithm using LANDSAT5 TM images and 2) estimation of regional evapotranspiration over a plain. The study area was Mahidasht plain in Kermanshah، Iran. For this purpose، the ET of maize and regional ET was estimated based on four images of LANDSAT during the growing season of maize in the year 2010. Maximum difference between maize simulated ET and measured data was less than 10%.

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.