The determination of maize water requirement and crop coefficient in Babol agrigultural research station in Mazandaran province using Landsat images and SEBAL algorithm was considered in the current study. Therefore, 11 satellite images of Landsat8 and Landsat7 during maize growth period in 2014 and 2015 were prepared. For using SEBAL, each band data was calibrated according to their corresponding coefficients. Then, net radiation flux on the ground surface and soil heat flux was calculated based on input and output radiation fluxes and computation of albedo, surface emissivity, ground surface temperature, and crop indices such as NDVI, SAVI and LAI. Sensible heat flux is also calculated by computation of friction velocity, aerodynamic resistance to heat transport and consideration of cold and hot pixels and atmospheric stability conditions. Finally, maize water requirement maps during growth period were prepared which had RMSE equals to 0.99, 1.09 and 0.65 mm/day compared to Reference Book, National Water Document and FAO56. Then, by computing reference evapotranspiration, maize crop coefficient in different growth stages was determined. This coefficient was 1.24 for mid stage which had 28, 8 and 3 percent difference with Reference Book, National Water Document and FAO56.

Preparation of maize water requirement maps in Mazandaran province was considered in the current study. The 10-day maize water requirement firstly was computed in the weather stations postitions using the FAO 2- stage approach. Reference evapotranspiration (ET0) was computed by FAO modified Penman-Monteith method and climate data from 51 weather stations. Crop coefficients were considered in 4 satages during growing season. To evaluate the spatial structure of the data, Trend and Aanisotropy in the data were analyzed. After computing experimental semivariances and selecting the best semivariogram model on the basis of the lower Residuals Sums of Squares (RSS), data were interpolated by deterministic (Inverse Distance Weighting, Global Polynomial Interpolator and Local Polynomial Interpolator) and geostatistical (Ordinary Kriging and Universal Kriging) methods. The interpolation accuracy was compared based on cross validation technique and showed that Global Polynomial Interpolator and Ordinary Kriging had the same results and were better than the other methods. Maps showed that the highest amount of ET0 occurs in August (Mordad) and 4.05 to 4.78 mm/day and the lowest amount occurs in January (Dey) and 1.05 to 1.27 mm/day. Also, the highest amount of silage maize water requirement occurs in third decade of June and two first decade of July (Tir) and 4.27 to 5.15 mm/day. The highest value of maize water requirement occurs in third decade of July (Mordad) and 4.41 to 5.21 mm/day, too. The computed and predicted silage maize water requirement for Dasht-e-naz station in the current study is lower 45.8 and 45 percent than the Reference Book results. Also, the computed and predicted maize water requirement for Dasht-e-naz station in the current study is higher 71.9 and 75 percent than the National Water Document results.

Soybeans are usually grown in warm weather and water supply is necessary for them. Therefore, preparation of soybeans water requirement maps in Mazandaran province was aimed in the current study. The needed information including the characteristics of climate, crop and the study area was formed in GIS. Reference evapotranspiration was computed by using FAO Penman-Monteith method and climatic data from 51 weather stations. By determining crop coefficient curve in the different growth stages, the 10-day soybean water requirement was obtained. After conducting trend and anisotropy analysis, spatial autocorrelation of the data was determined by calculating experimental semivariograms and fitting semivariogram models. Data were interpolated by using different deterministic and geostatistic methods. Interpolations error were determined based on Root Mean Square Error (RMSE) and Root Mean Square Standardized Error (RMSSE) from cross validation technique. Maps showed that the highest soybean water requirement happened in August which was 4.45-5.26 mm/day. Total soybean water requirement was obtained 511 to 613 that was 511 to 560 mm for the western region, followed by 560 to 580 mm for the eastern coastal, 580 to 595 mm for the eastern central and 595 to 613 mm for the eastern south. Resuts had significant difference compared to studied stations in Reference Book and National Water Document.

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.

Water is the most important component for the sustainable production of agricultural products. Droughts and the increase in plants' water requirement show the necessity of proper and accurate plant irrigation planning. Lack of water is one of the important economic and social factors in most developing countries, especially countries located in arid and semi-arid regions of the world. One of the most important agricultural products in the world is wheat. This product is always a part of macro-agricultural policies, and the analysis of the productivity situation and related challenges plays an important role in agricultural planning. Considering the high cultivated area of this plant and in order for irrigated cultivation to be a sustainable cultivation, it is necessary that its management, modification and methods of water consumption in agricultural lands should be given more attention by agricultural experts and managers. In order to be able to generalize the parameters measured at a point to the regional scale and to determine the water requirement at the regional scale, methods that, in addition to the quantity of data, also measure the location should be used. Therefore, this information should be converted from a point mode to a regional mode and establish the spatial integrity of the point data.

In the current research, the indicators of irrigation planning in wheat production (water requirement, irrigation depth and irrigation interval) in Darab Plain lands in Fars Province have been investigated.In order to create an optimal management, an irrigation database including soil, climate and plant information layers was formed in the GIS, and this information and data was obtained from the Agricultural Jahad Organization. The desired spatial variables are the water requirement of the wheat crop and the net depth of irrigation water. The weather data used to determine the water requirement, such as air temperature and soil parameters, are spatial data and are dependent on spatial characteristics. Then, based on the information provided in the GIS, the location map of the farms was prepared in the first stage. According to the size of the area, after the field visit and information about the condition of the farms, 30 farms that had a pressurized irrigation plan were selected. Due to considering proper dispersion and non-overlapping of information, a number of points were removed, and due to the fact that in order to use the kriging model, it is necessary for the points to have proper distribution, so finally 15 points (farms) were selected. The whole plain is scattered. Based on the selected points, the condition of the soil type was determined. The required information on water requirement, net and gross depth of water irrigation and soil texture were formed in the Geographic Information Systems. So that in the points without data, an estimate of the desired data was made and the required maps were prepared. In order to estimate the irrigation interval in each of the studied lands, using spatial analysis in the GIS and irrigation water depth maps and wheat water requirements, the irrigation interval map was prepared separately for each month.The purpose of the research is spatial analysis and the studied data is of the spatial data type, and geostatistics models were used to prepare spatial distribution maps of water requirement and net depth of water irrigation, and interpolation of data were done using Kriging method.

First, the condition of the soil texture in the study area was investigated. Based on the soil texture in the selected areas and with the help of the Thyssen method, the soil texture map of the study area was prepared. The texture of the soil in the northeast, east, southwest and west is loamy and in the northwest, center, south and southeast it is clay loam. The north of the studied area is sandy loam. The growing season of wheat cultivation in the study area is from December to June. The maps prepared for water requirement and irrigation depth showed that in most months, the highest amount of water requirement is related to the north and northeast parts and the lowest amount is related to the northwest and west parts. Also, the water requirement maps showed that during the wheat growth period in the study area (November to June), the month of May has the highest monthly water requirement with an average of 86.45 mm/month. The irrigation depth maps also showed that the irrigation depth in the center of the plain has the highest value and the northern parts of the plain have the lowest value. Also, the irrigation depth maps showed that the irrigation depth in the whole area varies from 8.6 cm to 13.8 cm. According to the water requirement maps, since the changes of water requirement in the study area are large and also the net irrigation depth map shows a high depth, as a result, the irrigation interval is affected by both. The lowest of wheat irrigation interval in the study area (1 day) is in the months of April and May for lands in the north, northeast and southeast, which increases from the east to the center, and in the northwest and southwest, the maximum irrigation interval is 3 days. Considering that the minimum water requirement of wheat in the growth period is in January and the range of its changes in different parts of the study area is between 6.6 and 18.05 mm/month. Therefore, the longest irrigation interval (5 to 18 days) is related to this month. According to the obtained results, the estimation of water requirement, net irrigation depth and irrigation interval based on regional changes and spatial analysis is a suitable method in irrigation planning. The use of spatial analysis and advanced geostatistics models can be effective in estimating irrigation parameters and productivity indicators and help different land managements and determine the amount of water requirement and the appropriate irrigation interval.

Suitable water management in irrigation and drainage networks without mapping water requirement of different plants in macro levels is not possible. Iran was located in arid climate and has Drought problems and management of Drought, need to macro management of its water resources. In this study using meteorological data in Khuzestan, reference evapotranspiration and Wheat water requirement was determined in pilots. Finally different methods of mapping for example regression method, Inverse Distance Weighted and Co- Kridging were evaluated. In calibration stage power and logarithmic regression presented suitable results and have 8 and 16percent error respectively. Result show that ET0 and water requirement have significant relation with height. So these parameters have spatial continuity. In evaluating stage,  The result show that Co- Kridging method with  0.82 R2 (regression coefficient) , 0.66 d(Agreement index)  , 22.9 Root Mean Square Error  and 0.05 Normal Root Mean Square Error  and  5 percent Normal error  has lowest error in estimation of water requirement  of wheat and using this method can mapping water requirement with well accuracy. So Co- Kridging method is the best method between other methods. Use map obtained can has suitable conclusion from wheat water requirement changes in Khuzestan and manage crop pattern base of maps.

The purpose of this study is to accurately measure the watershed potential for constructing rainwater harvesting structures. To carry out this research, the DEM digital-elevation model layer was used to prepare slope, hydrological soil groups and land use maps in the Arc GIS 10.3 software. The drainage density map was prepared by combining the DEM layer, topographic map, drainage density and hydrography data. A map of precipitation levels was prepared by using the pluvial method. CN map was prepared by combining land use and vegetation maps with hydrological soil groups. Runoff calculations were used to prepare runoff potential and coefficient maps. By combining them with the maps of drainage density, slope, and using analytical hierarchy process (AHP), a map of potential runoff production was prepared. The underground water fluctuation map was prepared using the base data and the water demand map was produced using (AHP). By combining this map with runoff production potential map, a water demand map was prepared. According to the obtained results, about 72 percent of the area has medium potential for the implementation of water extraction structures. It also shows about 22 percent of the area of low potential and about 6 precent of good potential.

The knowledge of spatial and temporal distribution of water requirement provides a relationship between land use, water allocation and consumption that can lead to an appropriate management of water resources. In the present study, based on mapping of spatial and temporal water requirement distribution and applicability of METRIC Algorithm was assessed for estimating daily evapotranspiration and seasonal water requirement, as well as crop coefficient in Qazvin highland desert. For this purpose, five high resolution (30m×30m) Landsat 7 ETM images were used during April to November 2000, as the agricultural activity period. The latent heat flux of evapotranspiration and instantaneous evapotranspiration were obtained based on the algorithm. The instantaneous evapotranspiration was extended to a daily scale and to the whole growing season through two extrapolation procedures. The accuracy of the maps was analyzed using lysimetric of alfalfa and maps of the produced crop coefficient were evaluated by using lysimetric of alfalfa and grass. Results indicated that relative error in crop coefficient estimation was between 0.053 and 0.138. Comparison of estimated and measured evapotranspiration demonstrated an appropriate consistency between results (r=0.92 and RMSE=0.60 mm/day). The amount of evapotranspiration at the pixel of the lysimeter location was estimated 1232 mm during the studied period. The findings revealed that METRIC algorithm can be applied as an effective, practical and affordable approach in accurate estimation of actual evapotranspiration at semi-arid and arid highlands area.

The aim of this paper is to adapt a water quality index for individual samples and to compare the results with that of the original GIS-based approach. Thirteen water quality parameters observed in 97 wells from the Shahrekord aquifer were used. In GIS-based method, quality parameters maps are difference-normalized, ranked and GWQI map is drawn. In derived method, observations from individual wells were separately and similarly treated to obtain WQI for each well. Both GWQI maps displayed similar trends and were highly correlated (R=0.91). While the minimum and mean GWQI for both methods were identical (respectively 81 and 84) the derived method estimated the maximum GWQI slightly lower (7%) and showed up to 6% difference in water quality class coverage. Overall, the derived method GWQI is more correlated with observations and performs better than the GIS-based method, and therefore, can be used for determining the overall quality of individual water samples and without the requirement of samples being spatially distributed.

For the purposed water resources management, irrigation scheduling, water usage optimization and gaining maximum yield two step FAO method was applied to determine the water requirement (evapotranspiration) of sugar beet. In this paper reference evapotranspiration were computed from recorded meteorological data at 38 weather stations using the hargreaves-samani method; and then through applying proper crop coefficient the amount of sugar beet potential evapotranspiration were calculated. Sugar beet potential evapotranspiration at 3045 points (3300*5200 m) distancing from weather stations were estimated Using three interpolation methods (cokriging, kriging and inverse distance weighted) and based on derived data the spatial distribution maps for sugar beet potential evapotranspiration in Tehran province were developed through geographic information system (GIS). Evaluation of developed maps based on mean square error (MSE) which was computed by cross-validation showed that the map of cokriging interpolation method was the best. In Tehran, the minimum and maximum sugar beet evapotranspiration during July were 5.5 and 10 mm/day respectively, which equals 0.64 and 1.2 lit/sec/hectare. The acquired minimum and maximum sugar beet evapotranspiration at growth period in Tehran province were 600 and 1200 mm, respectively.