Remote sensing techniques are currently used in different fields including irrigation and water management fields. One of the important fields is the calculation of water consumption (WC). Remote sensing techniques can be used to estimate actual evapotranspiration and it can also be used to estimate crop coefficients based on spectral reflectance of vegetation indices (VIs), and from a relation with normalized difference vegetation index (NDVI), which is an indicator for the absorption and the reflection ratios by the green plant. The current study used remote sensing data to calculate WC for El-Bostan irrigation district. Crop coefficient values were calculated from NDVI and reference evapotranspiration values were calculated using CROPWAT program. The obtained results were verified by comparing them with field measurements. There was a strong linear correlation between the measured and calculated values with r2 of 0.90, while the root-mean-square error (RMSE) was 0.68. The results were also verified by comparing irrigation efficiencies obtained from remote sensing and from field measurements. The results were very close to each other. The study illustrated the importance and the reliability of using remote sensing techniques in calculating WC values, and which could improve water management and water use efficiency.

  • Proper irrigation management requires an accurate estimation of crop water requirements.

  • Calculating water consumption values based on traditional techniques is not valid.

  • Calculation of crop water consumption using remote sensing technique at the irrigation district level.

  • The study depended on comparing calculated and measured water consumption values.

  • Using remote sensing technology can improve water management.

Irrigation water is limited and scarce in many regions of the world. Egypt has limited water resources with a gradual increase in water demand due to the rapid increase of the population. The gap between water supply and water demand is currently reaching 20.0 BCM/year. This gap is currently filled through the reuse of water losses and any more reduction in the resources will have a serious impact on the sustainability of agriculture.

For the previous reasons, the precise calculation of crop water consumption (WC)/requirement is essential for effective irrigation management and water conservation. In addition, the precise calculation of crop WC/requirement and in consequence, the irrigation water use could reduce the necessity for desalination and industrial wastewater treatment, which could be associated with a huge cost.

Crop evapotranspiration (ETC) represents crop water requirements and is affected by weather and actual crop conditions. According to FAO irrigation and drainage, paper No.24 and No.56 ETC can be obtained from reference crop evaporation (ET0) and using a stage-dependent crop coefficient (KC) from the relation (ETC = ET0*KC). Calculating KC values is currently depending on collecting the cropping pattern manually. Such an approach is adversely affected by many factors such as the fragmentation of property and the difference in planting and harvesting times, which makes the accurate calculation of water requirements in this way practically difficult. Water Management Research Institute (WMRI) has previous experience in calculating the discharge required at the head of different canals (Matching Model), based on collecting cropping pattern data from the agricultural departments and using the Penman–Monteith equation and FAO KC values. The model faced problems in its application, and one of the main problems was the inaccuracy of the collected cropping pattern data. The work with this approach could be achieved at the field level, and it is hard to use this approach for forecasting and water management. Therefore, remote sensing is the best technique to calculate all these parameters at the command area level (Bellvert 2018). This technology is attractive for modeling KC since it provides a synoptic coverage at fixed time intervals and can therefore monitor changes over time (Rozenstein et al. 2018). Remote sensing data are useful for monitoring and mapping vegetation cover changes and have been used extensively for identifying, assessing, and mapping such changes in different regions (Almalki et al. 2022). The normalized difference vegetation index (NDVI) was one of the most successful attempts to simply and quickly identify vegetated areas and their condition. Studies have demonstrated that NDVI is effective to estimate various vegetation properties, including the leaf area index (LAI) (Tian et al. 2017), biomass (Zhu & Liu 2015), chlorophyll concentration in leaves (Guzman et al. 2015), plant productivity (Vicente-Serrano et al. 2016), fractional vegetation cover (Dutrieux et al. 2015), and plant stress (Chavez et al. 2016). Several studies have been established for local regression functions between NDVI and crop coefficient value (KC). Using NDVI to calculate different agricultural elements including crop coefficients is a simple and considerably reliable way to calculate water requirements and in consequence, it could improve water management (Tan et al. 2021). Numerous models have been proposed for estimating evapotranspiration by using remotely sensed data, e.g., the Surface Energy Balance Algorithm for Land Model (SEBAL), Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC), the Simplified Surface Energy Balance Index (SSEBI), and the Surface Energy Balance System Model (SEBSM). Garcia-Santos et al. (2022) reviewed different models, the validation of use for each model, and the advantages and disadvantages of each model. They also performed modifications and improvements for these models. The authors concluded that the new trend of using hybrid ET models, which raised over the recent 5 years is of key importance for best ET retrievals. Results showed that three-source Surface Energy Balance (SEB) models can be of great utility for irrigation management of patchy surfaces as a vineyard (Levine et al. 2022). With the progress of developing new techniques for using remote sensing techniques, work is being carried out on the development of increasingly higher resolution multispectral thermal sensors which will allow further progress in the applications of this parameter in the estimation of crop water requirements, among other applications. Different international models were developed for the calculation of water requirements elements including reference evapotranspiration and crop coefficients. The famous model might be the CROPWAT model. CROPWAT 8.0 for Windows software is developed by FAO with the help of the National Water Research Center Egypt and the Institute of Irrigation Development Studies of Southampton of UK. CROPWAT windows calculate crop evapotranspiration using the FAO-56 Penman–Monteith equation.

The main objective of this study is to investigate the feasibility of applying remote sensing techniques in the field of water management. WC will be calculated from satellite image information. The study will first compare a few equations for the relation between NDVI and KC. The results were validated and the best equation was used to define the average crop coefficient value for the irrigation district. The calculated WC values will be checked through field measurements. Most of the previous remote sensing studies were applied at the field level. The current study aims to use the techniques to operate an irrigation system by calculating water requirements for each branch canal.

Study area

The study was applied to the irrigation district of El-Bostan 1 & 2, which is one of the districts of the Al-Nasr Irrigation directorate (west of the delta region) and is located east of the desert road and north of Sadat City. The main feeder of El-Bostan irrigation districts 1 & 2 is El-Bostan Canal, which takes from the El-Nobariya canal at km 53.37-LHS. The only water source for the district is Branch #1 Left canal, which takes off directly from Al-Bostan Canal at km 14.1 behind the third lifting station. It is located between 30° 12′ 33′ E longitude and 30° 42′ 46′ N latitude (Figure 1). The Branch #1 left canal is 19.15 km in length and it has 34 sub-branches in different levels. The current study investigated the total catchment of El-Bostan irrigation district, which is served by Branch #1 left canal. This area is 50,000 feddan. The catchment has special characteristics as around 80% of the cultivated area is citrus gardens, modern irrigation is applied for the entire district region, and drip irrigation is the dominant system. Also, this catchment is isolated as it has one inlet without any additional water resources, and it has two outlets (drains) that collect all the runoff of the area.
Figure 1

Location map of the study area.

Figure 1

Location map of the study area.

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Steps for the study

Figure 2 presents the flow chart of the study and the next sub-sections describe in details the items of the flow chart.
Figure 2

Study flow chart.

Figure 2

Study flow chart.

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To perform the previous steps, different programs were used including Arc GIS 10.8 for processing the data and developing the shape files, CROPWAT 8.0 for calculation reference evapotranspiration, and MATLAB-based program to calculate crop coefficient and WC values. The SPSS program was used to develop stage-flow relationships to calculate water supply and drainage runoff to calculate the actual WC.

Downloading satellite images

In this study, a series of sentinel-2 image data was downloaded for the summer season of 2019 as field measurements were done during the period from May to September 2019 (Figure 3). These images were downloaded from the USGS website https://earthexplorer.usgs.gov/sentinel data and are available at the website for free download. The images were selected to be almost cloud-free.

Sentinel-2 contains 12 bands and calculating NDVI depends on Bands 4 (red) and 8 (near-infrared). Wave lengths were 665 and 842 m for the two bands, respectively. The pixel size was 10 × 10 m for both bands.

Processing the data and developing the shape files

To perform the previous steps, different software programs were used. Arc GIS version 10.8 was used for pre-processing the images (calculating NDVI for different pixels) and to define the boundaries (shape files) for the district and the fields. The boundary for the district was already lifted by the Ministry of Water Resources and Irrigation (MWRI). The boundaries for fields and residential areas were defined using Global Positioning System (GPS) stations. The results were verified with Google Earth and they were lifted almost identical. Figure 4 presents examples for the shape files of the district and some fields inside it.
Figure 3

Sample of the selected images from May and August 2019.

Figure 3

Sample of the selected images from May and August 2019.

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Figure 4

Boundary of El-Bostan irrigation district and the selected fields.

Figure 4

Boundary of El-Bostan irrigation district and the selected fields.

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Calculating reference evapotranspiration

CROPWAT 8.0 (Figure 5) was used to calculate reference evapotranspiration (ET0) from the metrological data. The metrological data include temperature, relative humidity, wind speed, and solar irradiance, and the data were obtained from a metrological station in Wadi El-Natron experimental station, which is related to WMRI. The station is around 10 km from the study area. Microsoft Excel Worksheet was used for representing the results. Figure 6 presents the daily reference evapotranspiration during the summer of 2019.
Figure 5

Calculating ET0 using the CROPWAT program.

Figure 5

Calculating ET0 using the CROPWAT program.

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Figure 6

The values of daily reference evapotranspiration during the summer of 2019.

Figure 6

The values of daily reference evapotranspiration during the summer of 2019.

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Calculating crop coefficient and WC values

Crop coefficient values KC for different pixels and the average values for the district and the fields were defined using a MATLAB-based program (Figure 7). The program was developed by WMRI and it is also used to calculate total WC and WC for a unit area. The program worked as follows:
  • The program read the file with the names of the images from a text file. The user defines the name of this text file and the maximum number of images inside this file.

  • For each image, the program reads Tiff files related to the district or the fields with NDVI values. It defines the values for different pixels.

  • Based on the relationship between NDVI and KC that was embedded inside the program, KC values were calculated for different pixels

  • Using ET0 that was entered by the user, actual evapotranspiration is calculated for each pixel.

  • The program calculates the total values of all pixels and the average value for the image.

Figure 7

The interface of the input data in the program.

Figure 7

The interface of the input data in the program.

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Calculating NDVI and crop coefficient relationship

As described before, KC values were defined based on a relationship with NDVI. NDVI was derived from remote sensing data using two bands (red and near-infrared). It depends on the reflectance of the plant on these two bands. Mathematically, NDVI is driven by the following equation:
formula
(1)

NDVI is calculated for different pixels using Arc Map program as described before.

Selecting the best relationship between NDVI and KC

The current study verified different equations to select the one to be used for calculating KC from NDVI values. The investigated equations were

Six satellite images were downloaded and KC values were calculated using the three different equations for each image. Water supply and runoff values were calculated as the average of 5 days around each satellite image date.

The accuracy of calibration and validation was examined using two performance statistics, which are the root-mean-square error (RMSE) and normalized objective function (NOF), as follows:
formula
where Pi and Oi are the calculated and measured values, respectively; O is the mean of measured values, and N is the number of measurements. Model predictions are acceptable for NOF values in the interval from 0.0 to 1.0 (Gikas 2014).

Defining actual WC

Actual WC values were calculated as the difference between water supply and drainage runoff and these values were used to verify the calculated WC values.

Table 1

Model summary for the regression analysis between Q & USWL and diff

Model summarya
ModelRR squareAdjusted R squareStd. error of the estimate
0.842b .709 .665 1.518646 
Model summarya
ModelRR squareAdjusted R squareStd. error of the estimate
0.842b .709 .665 1.518646 

aDependent variable: discharge.

bPredictors: (constant), diff, USWL.

To calculate water supply and drainage runoff values, a stage–discharge relationship was developed for both Branch # 1 canal and both drains. For the relationship at the head of Branch # 1 canal, the discharge was the dependent variable and both the upstream water level and the head difference were independent variables. SPSS statistical program was used to develop the relationship. The results refer to a significant relationship as presented in Tables 13. The developed relationship was as follows:
formula
(5)
where USWL refers to the upstream water level and DSWL refers to the downstream water level.
Table 2

ANOVA results for the regression analysis between Q & USWL and diff

ANOVAa
ModelSum of SquaresdfMean SquareFSig.
1. Regression 73.204 36.602 15.871 0.000b 
Residual 29.982 13 2.306   
Total 103.185 15    
ANOVAa
ModelSum of SquaresdfMean SquareFSig.
1. Regression 73.204 36.602 15.871 0.000b 
Residual 29.982 13 2.306   
Total 103.185 15    

aDependent variable: discharge.

bPredictors: (Constant), diff, USWL.

Table 3

Coefficients for the regression analysis between Q & USWL and diff

Coefficientsa
ModelUnstandardized coefficients
Standardized coefficient stSig.
BStd. errorBeta
1 (Constant) −169.037 47.487  −3.560 .003 
USWL 8.695 2.258 .624 3.850 .002 
Diff −9.492 1.799 −.855 −5.277 .000 
Coefficientsa
ModelUnstandardized coefficients
Standardized coefficient stSig.
BStd. errorBeta
1 (Constant) −169.037 47.487  −3.560 .003 
USWL 8.695 2.258 .624 3.850 .002 
Diff −9.492 1.799 −.855 −5.277 .000 

aDependent variable: discharge.

Table 4

Average WC, ET0 and WC/fed for El-Bostan irrigation district during summer 2019

17 May 20196 Jun 201926 Jun 201910 Aug 201930 Aug 201914 Sep 2019
Average Kc values 0.729 0.701 0.683 0.712 0.716 0.664 
ET0 (mm) 5.22 5.71 5.88 5.33 5.12 4.41 
Average WC (m3/fed/day) 15.98 16.80 16.87 15.94 15.39 12.05 
17 May 20196 Jun 201926 Jun 201910 Aug 201930 Aug 201914 Sep 2019
Average Kc values 0.729 0.701 0.683 0.712 0.716 0.664 
ET0 (mm) 5.22 5.71 5.88 5.33 5.12 4.41 
Average WC (m3/fed/day) 15.98 16.80 16.87 15.94 15.39 12.05 
For Umom El-Bostan drain, the equations for both drains were:
formula
(6)
For the # 11 drain, the equations for both drains were:
formula
(7)

Normalized difference vegetation index

NDVI values were calculated based on Equation (1) and using sentinel-2 images. Figure 8 depicts NDVI values in the El-Bostan irrigation district.
Figure 8

The NDVI values for the six satellite images.

Figure 8

The NDVI values for the six satellite images.

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Comparing different empirical equations for calculating KC

For calculating KC values, Equations (2)–(4) were compared, and RMSE and NOF values were calculated for each equation. Figure 9 shows calibration results. From the table mentioned in Figure 9, the correlation coefficient between calculated and measured values for the best equation (Equation (2)) was 0.92. RMSE and NOF values were 0.68 and 0.042, respectively.
Figure 9

The regression analysis between the measured and calculated WC for the three investigated equations.

Figure 9

The regression analysis between the measured and calculated WC for the three investigated equations.

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Applying the selected equation to calculate WC and irrigation efficiency at the district level

After testing different equations, the best equation (Equation (2)) was used to calculate WC and irrigation efficiency for an irrigation district. This was an example of using remote sensing techniques in the water management field. On the other hand, this was a second verification for the equation as calculated WC was compared with the difference between total water supply and total drainage runoff.

Calculating WC for El-Bostan irrigation district

Table 4 presents detailed information about the calculated WC for the command area of Branch # 1 left from sentinel-2 data. The detailed information included the average KC values for all cells in the district, the daily ET0, and average WC per feddan. Figure 10 presents the average monthly WC values for the district.
Figure 10

Average monthly WC values for El-Bostan irrigation district.

Figure 10

Average monthly WC values for El-Bostan irrigation district.

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Table 5 presents the data of the WC which was calculated as the difference between the water supply at the head of the El-Bostan canal and the runoff from both drains. These data were compared with the calculated WC and the difference was very small between 1 and 17% as shown in Figure 11.
Table 5

The measured WC for the El-Bostan irrigation district during the summer of 2019

PeriodsActual WC (m3)
Ave daily WC (m3/fed/day)
Water Supply at the head of the branchRun off from El-Bostan drainRun off from drain #11Net water consumed
May 2019 883,618.90 55,230.92 24,915.53 803,472.45 16.07 
Jun 2019 1,006,043.96 48,839.05 28,648.50 928,556.41 18.57 
Jul 2019 986,678.95 44,074.48 38,680.43 903,924.03 18.08 
Aug 2019 995,598.92 42,104.26 41,492.50 912,002.16 18.24 
Sep 2019 758,773.33 23,491.04 14,355.30 720,926.98 14.42 
PeriodsActual WC (m3)
Ave daily WC (m3/fed/day)
Water Supply at the head of the branchRun off from El-Bostan drainRun off from drain #11Net water consumed
May 2019 883,618.90 55,230.92 24,915.53 803,472.45 16.07 
Jun 2019 1,006,043.96 48,839.05 28,648.50 928,556.41 18.57 
Jul 2019 986,678.95 44,074.48 38,680.43 903,924.03 18.08 
Aug 2019 995,598.92 42,104.26 41,492.50 912,002.16 18.24 
Sep 2019 758,773.33 23,491.04 14,355.30 720,926.98 14.42 
Figure 11

Average monthly measured and calculated WC values for El-Bostan irrigation district.

Figure 11

Average monthly measured and calculated WC values for El-Bostan irrigation district.

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Calculating water supply and runoff values for El-Bostan irrigation district

Water supply was calculated from recorded upstream and downstream water levels based on the developed equations (from Equation (5) to Equation (7)). The results are presented in Figure 12. The values increased from 9.3 m3 /s (17.82 m3/fed/day) in May to be between 20.04 m3/s (10.5 m3/fed/day) in July and 10.7 m3/s (20.59 m3/fed/day) then the values decreased to 8.3 m3/s (15.99 m3/fed/day) in September.
Figure 12

Average monthly water supply at the head of Branch # 1 Left.

Figure 12

Average monthly water supply at the head of Branch # 1 Left.

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The runoff values were calculated at the outlets of Umom El-Bostan and no. 11 drains based on developed equations and recorded water levels Figure 13.
Figure 13

Average monthly runoff values at the outlets of Umom El-Bostan and # 11 drain.

Figure 13

Average monthly runoff values at the outlets of Umom El-Bostan and # 11 drain.

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Calculating overall irrigation efficiency in the El-Bostan irrigation district

After testing the equation, and verifying the best equation at district irrigation level, the equation was used to define overall irrigation efficiency at the irrigation district level. The values were calculated as a ratio between water demand (calculated WC values) and water supply at the head of the district.

Figure 14 presents the average irrigation efficiency and the values were between 86% in August 2019 and 94% in May 2019. The results are consistent with the expected values in the district as a modern irrigation system is applied in the entire district and the drainage runoff values were considerably low compared to water supply values.
Figure 14

Average monthly irrigation efficiency of El-Bostan irrigation district as the ratio between water supply and WC.

Figure 14

Average monthly irrigation efficiency of El-Bostan irrigation district as the ratio between water supply and WC.

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Discussion

The previous results have two main parts: the first part is the selection of a suitable equation for the relationship between NDVI and KC. Three different empirical equations were tested by using them to calculate WC for five different periods and compare the results with field measurements. RMSE and NOF were calculated for the three equations, and there was a significant difference between one of them and the other two. It should be noted that the winner equation was developed as a general equation for different crops, which was the case in this study as it calculates the average KC for an irrigation district. The other two equations were developed for specific crops.

The second part of the study used the selected equation for calculating WC and irrigation efficiency for the El-Bostan irrigation district, which could be considered as a second verification for the equation as well as an example of using remote sensing techniques in water management. WC was calculated for the entire district and the results were compared with the difference between water supply and drainage runoff, which is considered as actual WC. At the same time, calculated WC values were compared with the total water supply to calculate the average irrigation efficiency for the district. The results for both items supported the accuracy of calculating WC based on the remote sensing technique. The results were compatible with the results of Kharrou et al. (2021) who conducted that irrigation managers would likely appreciate using operational tools based on the remote sensing information, which could allow for a reliable assessment of irrigation delivery performance at the scale of irrigation schemes and provide an enhanced irrigation advisory service by delivering customized and near real-time information on crop development status and irrigation needs to farmers or Water Users Associations. These tools enable the improvement of irrigation scheduling for efficient and profitable agricultural water management, particularly in water-limited areas.

The current study aimed to investigate the feasibility of applying remote sensing techniques in the water management field by using satellite images to calculate WC values to use the calculated values to define the required water supply at the heads of different irrigation canals.

The current approach of calculating WC values based on collecting the cropping pattern manually and using fixed crop WC values is adversely affected by many factors that diminish its accuracy. These factors include land fragmentation, stopping the crop cycle, and the difficulty of collecting cultivation dates and crop life cycles. The suggested technique provides a better alternative for calculating WC, which could positively improve water management and, as a consequence water use efficiency. The previous results presented the calculated remote sensing-based WC. The study applied three different empirical equations that connect NDVI with crop coefficient value (KC). Field measurements were performed to calculate the water supply at the head of the main canal and the outlets of the main drains, which gave the chance to calculate actual WC accurately and therefore use such WC data to test the three empirical equations. RMSE, NOF, and correlation efficiency were calculated for different equations and it was concluded that one of these equations (Equation (2)) is outperforming other equations. In this equation, RMSE was 0.68, NOF was 0.042, and the correlation coefficient value was 0.92. The selected equation, which was developed as a general equation for different crops was used to calculate average monthly irrigation efficiency values for the El-Bostan irrigation district. Average monthly irrigation efficiency values were also calculated based on sentinel 2 WC values and water supply at the head of the district. The values, which were between 86% in August 2019 and 94% in May 2019, were consistent with the expected situation in the district as a modern irrigation system is applied in the entire district and drainage runoff values were considerably low compared to water supply values.

The current study is important due to the following:

  • The current situation in Egypt with limited water resources requires calculating water requirements precisely.

  • There is difficulty in calculating WC based on the traditional technique due to land fragmentation and the diversity of the cropping pattern and cultivation dates.

  • The remote sensing technology provides a suitable alternative that can overcome the difficulties of the previous approach by calculating crop coefficient values based on NDVI.

The study selected the proper empirical equation after testing different equations. The selected equation was verified based on actual measurements at the irrigation district level, and then it was used to define the irrigation efficiency for this district. The results showed a strong relation between calculated and measured values, which refer to the accuracy of calculated WC values and the reliability of this approach, and its ability to overcome the disadvantages of the current approach that depend on collecting cropping pattern manually.

It is recommended to use the current approach as the main tool to distribute water supply between different irrigation canals, by developing a model that incorporates remote sensing-based WC values with other information to define the required water supply at the head of different branch canals and different control structures.

The suggested model could be a remarkable step in improving water distribution in the Egyptian irrigation system, and it could be the main step to sustaining agriculture in Egypt with limited water resources.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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