Integrating Remote Sensing Techniques and Meteorological Data to Assess the Ideal Irrigation System Performance Scenarios for Improving Crop Productivity

Round 1

Reviewer 1 Report (Previous Reviewer 5)

Comments and Suggestions for Authors

Review of water-2316609, “Integrating remote sensing techniques and meteorological data to assess the ideal irrigation system performance scenarios for improving crop productivity”. It is an original study on agricultural irrigation management that has certain innovations and practicality. The topic is interesting and relevant to the scope of the journal. However, there are many issues that require further elaboration. I have provided the following comments to help improve the quality of the paper.

1.     The introduction is clearly written and organized in general, but it can be further improved. For example, in lines 55-56, the author mentions the importance of international cooperation in water resources management but does not provide specific examples or evidence to support this view. It is recommended that the author quotes some related literature to enhance persuasion.

2.     In lines 126-136, in addition to NDVI and NDMI, can other vegetation indexes or moisture indices be used to monitor crop growth? What are their advantages and disadvantages? The author should include related latest research, as well as a comparison and evaluation of different methods and indicators.

3.     The materials and methods section of the article could be more detailed to explain the source of data, processing methods, statistical analysis methods, etc. For example, how is the Sentinel 2 A/B satellite data obtained, pre-processed, classified, and cut?

4.     In lines 205-206， there are 143 central pivot farms that have been manually selected? What is the reason for choosing these over the other options?

5.     Figure 3 could be improved visually, and the author should check whether the scale of Figure 5 is necessary to add.

6.     In the Crop Water Use and Water Use Efficiency section, besides irrigation, are other factors that may affect crop growth considered?

7.     The conclusion of the article should be more refined, summarizing the main discoveries and contributions of the article, as well as the limitations or uncertainties that exist. For example, what kind of laws or trends has the article discovered? What is the significance of these findings for understanding the performance of the irrigation system?

8.     The suggestion section of the article could be more specific, proposing some highly feasible and innovative solutions or measures, as well as expected results or impact. For example, the article suggests increasing water resources management projects and improving irrigation efficiency.

 

9.     The article needs to be revised for language and format, and errors in terms of grammar, spelling, punctuation, etc., should be checked.

Author Response

Kindly, I am writing to you to submit the revised version of our paper entitled " Integrating remote sensing techniques and meteorological data to assess the ideal irrigation system performance scenarios for improving crop productivity" to your esteemed journal. We appreciate the opportunity to submit this manuscript for publication consideration and are confident that it aligns with the journal's scope and interests.
We would like to express our gratitude to the valued reviewers and the editorial team for their valuable feedback and support throughout the review process. We are confident that the revised manuscript has been improved considerably and meets the high standards of the Water Journal.
We are looking forward to your kind consideration.
Sincerely,  

Comments and Suggestions for Authors

Review of water-2316609, “Integrating remote sensing techniques and meteorological data to assess the ideal irrigation system performance scenarios for improving crop productivity”. It is an original study on agricultural irrigation management that has certain innovations and practicality. The topic is interesting and relevant to the scope of the journal. However, there are many issues that require further elaboration. I have provided the following comments to help improve the quality of the paper.

 

1. Comment: The introduction is clearly written and organized in general, but it can be further improved. For example, in lines 51-55, 61-70,and 159-172 the author mentions the importance of international cooperation in water resources management but does not provide specific examples or evidence to support this view. It is recommended that the author quotes some related literature to enhance persuasion.

 

Response: The reference under the tittle:

Sustainable Water Management in Iraq (Kurdistan) as a Challenge for Governmental Responsibility[1]added, Please see Page2  , Line 51-55, 61-70,and 159-172 Thank you very much!

 

1. Yousuf, M.A.; Rapantova, N.; Younis, J.H. Sustainable Water Management in Iraq (Kurdistan) as a Challenge for Governmental Responsibility. Water (Switzerland) 2018, 10, 1–19, doi:10.3390/w10111651.
2. Qader, S.H.; Dash, J.; Atkinson, P.M. Forecasting Wheat and Barley Crop Production in Arid and Semi-Arid Regions Using Remotely Sensed Primary Productivity and Crop Phenology: A Case Study in Iraq. Sci. Total Environ. 2018, 613–614, 250–262, doi:10.1016/j.scitotenv.2017.09.057.
3. Jaafar, H.H.; Ahmad, F.A. Crop Yield Prediction from Remotely Sensed Vegetation Indices and Primary Productivity in Arid and Semi-Arid Lands. Int. J. Remote Sens. 2015, 36, 4570–4589, doi:10.1080/01431161.2015.1084434.
4. Bolton, D.K.; Friedl, M.A. Forecasting Crop Yield Using Remotely Sensed Vegetation Indices and Crop Phenology Metrics. Agric. For. Meteorol. 2013, 173, 74–84, doi:10.1016/j.agrformet.2013.01.007.

 

2. Comment: /In lines 126-136, in addition to NDVI and NDMI, can other vegetation indexes or moisture indices be used to monitor crop growth? What are their advantages and disadvantages? The author should include related latest research, as well as a comparison and evaluation of different methods and indicators.

 

Response: Please see Page 4  Line  138-145

Yes, There are alternative vegetation and moisture indices available to monitor crop growth. Some of the well-known ones include Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), Leaf Area Index (LAI), Soil Adjusted Vegetation Index (SAVI), Thermal Vegetation Index (TVI), Crop Water Stress Index (CWSI), and Temperature Vegetation Dryness Index (TVDI). The selection of an appropriate index depends on various factors such as the type of crop, growth stage, and environmental conditions. For instance, the best index to monitor crop growth in semi-arid zone areas depends on various factors, such as the specific crop type, growth stage, and environmental conditions. Generally, NDVI and EVI have been widely used in semi-arid areas and have shown good performance in detecting vegetation changes[2][3][4]. However, other indices such as NDWI, LAI, SAVI, TVI, CWSI, and TVDI may also be suitable depending on the specific application and the characteristics of the crop and environment. It is recommended to carefully evaluate the advantages and limitations of each index before selecting the most appropriate one for a specific application. Additionally, recent research has focused on developing new indices that can better capture crop growth dynamics in semi-arid areas, so it may be worthwhile to explore these options as well. The main objective of using NDMI (Normalized Difference Moisture Index) is to monitor and assess the vegetation water content, particularly in areas where water stress is a concern. NDMI is derived from the difference between the near-infrared and mid-infrared bands of satellite imagery, and it has been shown to be a reliable indicator of vegetation water content. By monitoring NDMI values over time, it is possible to detect changes in vegetation water content and assess the level of water stress experienced by crops or other vegetation. This information can be used to optimize irrigation practices and improve crop management, particularly in arid and semi-arid regions where water availability is limited[5][6].(This section added to Introduction)

Thank you very much!

 

5. Ghazaryan, G.; Dubovyk, O.; Graw, V.; Kussul, N.; Schellberg, J. Local-Scale Agricultural Drought Monitoring with Satellite-Based Multi-Sensor Time-Series. GIScience Remote Sens. 2020, 57, 704–718, doi:10.1080/15481603.2020.1778332.
6. Das, A.C.; Noguchi, R.; Ahamed, T. An Assessment of Drought Stress in Tea Estates Using Optical and Thermal Remote Sensing. Remote Sens. 2021, 13, doi:10.3390/rs13142730.
7. Qader, S.H.; Dash, J.; Atkinson, P.M. Forecasting Wheat and Barley Crop Production in Arid and Semi-Arid Regions Using Remotely Sensed Primary Productivity and Crop Phenology: A Case Study in Iraq. Sci. Total Environ. 2018, 613–614, 250–262, doi:10.1016/j.scitotenv.2017.09.057.
8. Jaafar, H.H.; Ahmad, F.A. Crop Yield Prediction from Remotely Sensed Vegetation Indices and Primary Productivity in Arid and Semi-Arid Lands. Int. J. Remote Sens. 2015, 36, 4570–4589, doi:10.1080/01431161.2015.1084434.
9. Bolton, D.K.; Friedl, M.A. Forecasting Crop Yield Using Remotely Sensed Vegetation Indices and Crop Phenology Metrics. Agric. For. Meteorol. 2013, 173, 74–84, doi:10.1016/j.agrformet.2013.01.007.

 

Comment/3.     The materials and methods section of the article could be more detailed to explain the source of data, processing methods, statistical analysis methods, etc. For example, how is the Sentinel 2 A/B satellite data obtained, pre-processed, classified, and cut?

 

Response: Please see Page7   Line  222-247

In this study, the Sentinel 2 A/B satellite data was acquired from the Copernicus Open Access Hub in the form of Level-1C products. These products contain TOA reflectance values in 13 spectral bands, with spatial resolutions ranging from 10 meters to 20 meters. The data for the study area and time period of interest were obtained using the Sentinelsat Python API, and then preprocessed in GEE to obtain surface reflectance values. The preprocessing steps included resampling the data to a common spatial resolution of 10 meters, masking out clouds and cloud shadows using the SCL band, and applying a BRDF correction to account for directional effects caused by surface roughness and slope.To facilitate further analysis and visualization, the classified Sentinel 2 data were cut into smaller tiles. This was done using the ee.data.getTileUrl function in GEE, which allowed individual tiles to be extracted based on their geographic coordinates and zoom level. The resulting tiles were saved in GeoTIFF format, which can be easily imported into GIS software for further processing and analysis.The classified and cut Sentinel 2 data were then subjected to a variety of statistical analyses, including frequency distributions, cross-tabulations, and spatial autocorrelation analysis. These analyses were performed using both GEE and GIS software and were used to quantify the spatial patterns and relationships between different meteorological variables in the study area.

Thank you very much!

 

Comment /4.     In lines 205-206， there are 143 central pivot farms that have been manually selected? What is the reason for choosing these over the other options?

 

Response: Because shallow and deep tube wells are used to provide irrigation water for those 143 central pivot. and the farmers who own pivot sprinklers in this region are experienced farmers, have good capabilities, and have an educational background that differs from those in other regions, and on this basis they are supported by local governments. Thank you very much!

 

 

Comment / 5.     Figure 3 could be improved visually, and the author should check whether the scale of Figure 5 is necessary to add.

 

Response / The Figure 3 is Flowchart of the methodology adopted in this study.

Usually, a flowchart illustrating a methodology does not necessitate a scale.

The flowchart is a graphic portrayal of a process or procedure, where the stages or actions are represented by different symbols and linked by arrows to demonstrate the flow of the process. These symbols employed in a flowchart do not demand a specific scale as they are typically standardized and universally comprehended.

If the study includes gathering data utilizing a measurement scale, it is crucial to use a dependable and valid scale suitable for the variables being measured. The methodology section of the study can include the scale to provide information on how the data was collected and measured. Thank you very much!

 

 

Comment /6.     In the Crop Water Use and Water Use Efficiency section, besides irrigation, are other factors that may affect crop growth considered?

 

Response /Typically, in the Crop Water Use and Water Use Efficiency section, factors other than irrigation that could potentially impact crop growth are taken into account. These factors might encompass temperature, humidity, soil properties, nutrient availability, and the presence of pests or diseases, as well as the genetics of the crop itself. Given that the quantity of water required for the best possible crop growth and yield is influenced by these factors, they are often included when computing crop water use and water use efficiency. Furthermore, these factors can be managed or adjusted to enhance crop growth and water use efficiency, for instance, by employing fertilizers or measures to control pests.

Thank you very much!

 

Comment /7.     The conclusion of the article should be more refined, summarizing the main discoveries and contributions of the article, as well as the limitations or uncertainties that exist. For example, what kind of laws or trends has the article discovered? What is the significance of these findings for understanding the performance of the irrigation system?

 

Response / This section added to conclusion: The NDMI Vegetation Index is a useful tool for detecting moisture deficiencies in crops and identifying under-irrigated areas. With this information, it is possible to divide the field into zones with different water needs and schedule precision irrigation events as needed. It also provides historical and current precipitation data, NDVI index graphs, and and precipitation monitoring graphs. By analyzing seasonal weather and precipitation patterns, it is possible to plan precision irrigation strategies for different fields. In addition to historical data, Crop Monitoring provides a 5-day weather forecast for each field. This information helps farmers decide on the need for watering activities to ensure proper soil moisture for crops. Precision irrigation is a cost-effective method for providing sufficient water supply to crops in areas with limited rainfall. Crop Monitoring tracks changes in the NDVI for individual fields throughout the season. This helps farmers identify weak and strong productivity areas across the field and create special maps for variable-rate applications of seeds and fertilizers. NDVI values can vary throughout the growing season, indicating water stress or waterlogging, and can be visualized through maps and graphs (Figure5 and 6). Crop Monitoring offers cur-rent and historical soil moisture data and NDVI index graphs, which help farmers, track the correlation between rainfall and moisture levels in the field. NDMI values vary throughout the growing season and can be visualized through maps and graphs, indicating water stress or waterlogging. Decrease in NDMI values indicates water stress, while abnormally high values could signal waterlogging ( Figure 10 and 12 ). Visualization of NDMI through maps and graphs helps farmers detect problem areas in the field and save time and resource . Water supply is a critical factor for the growth of plants, along with sunlight, nutrients, and soil temperature. Fields in areas with frequent rainfall receive sufficient water for crop growth, while in semi-arid regions, additional watering is necessary to maximize yields. Thank you very much!

 

Comment/8.     

The suggestion section of the article could be more specific, proposing some highly feasible and innovative solutions or measures, as well as expected results or impact. For example, the article suggests increasing water resources management projects and improving irrigation efficiency.

 

Response: Thank you very much indeed for your comment .Precision irrigation systems that rely on integrated remote sensing and meteorological data can have a significant impact on agriculture and water resource management by providing up-to-date information on crop health and moisture levels. This information can be utilized to design and optimize precision irrigation systems that deliver water and nutrients precisely where and when they are needed. This approach has the potential to increase crop yields and quality, while simultaneously reducing water usage and minimizing negative environmental effects, such as soil erosion, nutrient leaching, and water runoff. Adopting precision irrigation systems based on remote sensing can also help tackle water scarcity problems at the global and local levels. Given that agriculture accounts for the majority of freshwater use worldwide, utilizing water more efficiently in this sector can free up water resources for other purposes.

Additionally , the development and adoption of precision irrigation systems based on remote sensing represents a significant advancement in the field of agriculture and water resource management. This innovation has the potential to enhance food security and environmental sustainability. Thank you very much!

 

Comment/9.  The article needs to be revised for language and format, and errors in terms of grammar, spelling, punctuation, etc., should be checked.

 

Response:/The manuscript Proofreader, multiple times, to catch any errors that may have been missed during the writing process. These are general tips, and each manuscript will have its own unique set of grammar issues that addressed. The English grammar mistakes of the manuscript improved, Thank you very much!

Comments and Suggestions for Authors

Review of water-2316609, “Integrating remote sensing techniques and meteorological data to assess the ideal irrigation system performance scenarios for improving crop productivity”. It is an original study on agricultural irrigation management that has certain innovations and practicality. The topic is interesting and relevant to the scope of the journal. However, there are many issues that require further elaboration. I have provided the following comments to help improve the quality of the paper.

 

1. Comment: The introduction is clearly written and organized in general, but it can be further improved. For example, in lines 51-55, 61-70,and 159-172 the author mentions the importance of international cooperation in water resources management but does not provide specific examples or evidence to support this view. It is recommended that the author quotes some related literature to enhance persuasion.

 

Response: The reference under the tittle:

Sustainable Water Management in Iraq (Kurdistan) as a Challenge for Governmental Responsibility[1]added, Please see Page2  , Line 51-55, 61-70,and 159-172 Thank you very much!

 

1. Yousuf, M.A.; Rapantova, N.; Younis, J.H. Sustainable Water Management in Iraq (Kurdistan) as a Challenge for Governmental Responsibility. Water (Switzerland) 2018, 10, 1–19, doi:10.3390/w10111651.
2. Qader, S.H.; Dash, J.; Atkinson, P.M. Forecasting Wheat and Barley Crop Production in Arid and Semi-Arid Regions Using Remotely Sensed Primary Productivity and Crop Phenology: A Case Study in Iraq. Sci. Total Environ. 2018, 613–614, 250–262, doi:10.1016/j.scitotenv.2017.09.057.
3. Jaafar, H.H.; Ahmad, F.A. Crop Yield Prediction from Remotely Sensed Vegetation Indices and Primary Productivity in Arid and Semi-Arid Lands. Int. J. Remote Sens. 2015, 36, 4570–4589, doi:10.1080/01431161.2015.1084434.
4. Bolton, D.K.; Friedl, M.A. Forecasting Crop Yield Using Remotely Sensed Vegetation Indices and Crop Phenology Metrics. Agric. For. Meteorol. 2013, 173, 74–84, doi:10.1016/j.agrformet.2013.01.007.

 

2. Comment: /In lines 126-136, in addition to NDVI and NDMI, can other vegetation indexes or moisture indices be used to monitor crop growth? What are their advantages and disadvantages? The author should include related latest research, as well as a comparison and evaluation of different methods and indicators.

 

Response: Please see Page 4  Line  138-145

Yes, There are alternative vegetation and moisture indices available to monitor crop growth. Some of the well-known ones include Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), Leaf Area Index (LAI), Soil Adjusted Vegetation Index (SAVI), Thermal Vegetation Index (TVI), Crop Water Stress Index (CWSI), and Temperature Vegetation Dryness Index (TVDI). The selection of an appropriate index depends on various factors such as the type of crop, growth stage, and environmental conditions. For instance, the best index to monitor crop growth in semi-arid zone areas depends on various factors, such as the specific crop type, growth stage, and environmental conditions. Generally, NDVI and EVI have been widely used in semi-arid areas and have shown good performance in detecting vegetation changes[2][3][4]. However, other indices such as NDWI, LAI, SAVI, TVI, CWSI, and TVDI may also be suitable depending on the specific application and the characteristics of the crop and environment. It is recommended to carefully evaluate the advantages and limitations of each index before selecting the most appropriate one for a specific application. Additionally, recent research has focused on developing new indices that can better capture crop growth dynamics in semi-arid areas, so it may be worthwhile to explore these options as well. The main objective of using NDMI (Normalized Difference Moisture Index) is to monitor and assess the vegetation water content, particularly in areas where water stress is a concern. NDMI is derived from the difference between the near-infrared and mid-infrared bands of satellite imagery, and it has been shown to be a reliable indicator of vegetation water content. By monitoring NDMI values over time, it is possible to detect changes in vegetation water content and assess the level of water stress experienced by crops or other vegetation. This information can be used to optimize irrigation practices and improve crop management, particularly in arid and semi-arid regions where water availability is limited[5][6].(This section added to Introduction)

Thank you very much!

 

5. Ghazaryan, G.; Dubovyk, O.; Graw, V.; Kussul, N.; Schellberg, J. Local-Scale Agricultural Drought Monitoring with Satellite-Based Multi-Sensor Time-Series. GIScience Remote Sens. 2020, 57, 704–718, doi:10.1080/15481603.2020.1778332.
6. Das, A.C.; Noguchi, R.; Ahamed, T. An Assessment of Drought Stress in Tea Estates Using Optical and Thermal Remote Sensing. Remote Sens. 2021, 13, doi:10.3390/rs13142730.
7. Qader, S.H.; Dash, J.; Atkinson, P.M. Forecasting Wheat and Barley Crop Production in Arid and Semi-Arid Regions Using Remotely Sensed Primary Productivity and Crop Phenology: A Case Study in Iraq. Sci. Total Environ. 2018, 613–614, 250–262, doi:10.1016/j.scitotenv.2017.09.057.
8. Jaafar, H.H.; Ahmad, F.A. Crop Yield Prediction from Remotely Sensed Vegetation Indices and Primary Productivity in Arid and Semi-Arid Lands. Int. J. Remote Sens. 2015, 36, 4570–4589, doi:10.1080/01431161.2015.1084434.
9. Bolton, D.K.; Friedl, M.A. Forecasting Crop Yield Using Remotely Sensed Vegetation Indices and Crop Phenology Metrics. Agric. For. Meteorol. 2013, 173, 74–84, doi:10.1016/j.agrformet.2013.01.007.

 

Comment/3.     The materials and methods section of the article could be more detailed to explain the source of data, processing methods, statistical analysis methods, etc. For example, how is the Sentinel 2 A/B satellite data obtained, pre-processed, classified, and cut?

 

Response: Please see Page7   Line  222-247

In this study, the Sentinel 2 A/B satellite data was acquired from the Copernicus Open Access Hub in the form of Level-1C products. These products contain TOA reflectance values in 13 spectral bands, with spatial resolutions ranging from 10 meters to 20 meters. The data for the study area and time period of interest were obtained using the Sentinelsat Python API, and then preprocessed in GEE to obtain surface reflectance values. The preprocessing steps included resampling the data to a common spatial resolution of 10 meters, masking out clouds and cloud shadows using the SCL band, and applying a BRDF correction to account for directional effects caused by surface roughness and slope.To facilitate further analysis and visualization, the classified Sentinel 2 data were cut into smaller tiles. This was done using the ee.data.getTileUrl function in GEE, which allowed individual tiles to be extracted based on their geographic coordinates and zoom level. The resulting tiles were saved in GeoTIFF format, which can be easily imported into GIS software for further processing and analysis.The classified and cut Sentinel 2 data were then subjected to a variety of statistical analyses, including frequency distributions, cross-tabulations, and spatial autocorrelation analysis. These analyses were performed using both GEE and GIS software and were used to quantify the spatial patterns and relationships between different meteorological variables in the study area.

Thank you very much!

 

Comment /4.     In lines 205-206， there are 143 central pivot farms that have been manually selected? What is the reason for choosing these over the other options?

 

Response: Because shallow and deep tube wells are used to provide irrigation water for those 143 central pivot. and the farmers who own pivot sprinklers in this region are experienced farmers, have good capabilities, and have an educational background that differs from those in other regions, and on this basis they are supported by local governments. Thank you very much!

 

 

Comment / 5.     Figure 3 could be improved visually, and the author should check whether the scale of Figure 5 is necessary to add.

 

Response / The Figure 3 is Flowchart of the methodology adopted in this study.

Usually, a flowchart illustrating a methodology does not necessitate a scale.

The flowchart is a graphic portrayal of a process or procedure, where the stages or actions are represented by different symbols and linked by arrows to demonstrate the flow of the process. These symbols employed in a flowchart do not demand a specific scale as they are typically standardized and universally comprehended.

If the study includes gathering data utilizing a measurement scale, it is crucial to use a dependable and valid scale suitable for the variables being measured. The methodology section of the study can include the scale to provide information on how the data was collected and measured. Thank you very much!

 

 

Comment /6.     In the Crop Water Use and Water Use Efficiency section, besides irrigation, are other factors that may affect crop growth considered?

 

Response /Typically, in the Crop Water Use and Water Use Efficiency section, factors other than irrigation that could potentially impact crop growth are taken into account. These factors might encompass temperature, humidity, soil properties, nutrient availability, and the presence of pests or diseases, as well as the genetics of the crop itself. Given that the quantity of water required for the best possible crop growth and yield is influenced by these factors, they are often included when computing crop water use and water use efficiency. Furthermore, these factors can be managed or adjusted to enhance crop growth and water use efficiency, for instance, by employing fertilizers or measures to control pests.

Thank you very much!

 

Comment /7.     The conclusion of the article should be more refined, summarizing the main discoveries and contributions of the article, as well as the limitations or uncertainties that exist. For example, what kind of laws or trends has the article discovered? What is the significance of these findings for understanding the performance of the irrigation system?

 

Response / This section added to conclusion: The NDMI Vegetation Index is a useful tool for detecting moisture deficiencies in crops and identifying under-irrigated areas. With this information, it is possible to divide the field into zones with different water needs and schedule precision irrigation events as needed. It also provides historical and current precipitation data, NDVI index graphs, and and precipitation monitoring graphs. By analyzing seasonal weather and precipitation patterns, it is possible to plan precision irrigation strategies for different fields. In addition to historical data, Crop Monitoring provides a 5-day weather forecast for each field. This information helps farmers decide on the need for watering activities to ensure proper soil moisture for crops. Precision irrigation is a cost-effective method for providing sufficient water supply to crops in areas with limited rainfall. Crop Monitoring tracks changes in the NDVI for individual fields throughout the season. This helps farmers identify weak and strong productivity areas across the field and create special maps for variable-rate applications of seeds and fertilizers. NDVI values can vary throughout the growing season, indicating water stress or waterlogging, and can be visualized through maps and graphs (Figure5 and 6). Crop Monitoring offers cur-rent and historical soil moisture data and NDVI index graphs, which help farmers, track the correlation between rainfall and moisture levels in the field. NDMI values vary throughout the growing season and can be visualized through maps and graphs, indicating water stress or waterlogging. Decrease in NDMI values indicates water stress, while abnormally high values could signal waterlogging ( Figure 10 and 12 ). Visualization of NDMI through maps and graphs helps farmers detect problem areas in the field and save time and resource . Water supply is a critical factor for the growth of plants, along with sunlight, nutrients, and soil temperature. Fields in areas with frequent rainfall receive sufficient water for crop growth, while in semi-arid regions, additional watering is necessary to maximize yields. Thank you very much!

 

Comment/8.     

The suggestion section of the article could be more specific, proposing some highly feasible and innovative solutions or measures, as well as expected results or impact. For example, the article suggests increasing water resources management projects and improving irrigation efficiency.

 

Response: Thank you very much indeed for your comment .Precision irrigation systems that rely on integrated remote sensing and meteorological data can have a significant impact on agriculture and water resource management by providing up-to-date information on crop health and moisture levels. This information can be utilized to design and optimize precision irrigation systems that deliver water and nutrients precisely where and when they are needed. This approach has the potential to increase crop yields and quality, while simultaneously reducing water usage and minimizing negative environmental effects, such as soil erosion, nutrient leaching, and water runoff. Adopting precision irrigation systems based on remote sensing can also help tackle water scarcity problems at the global and local levels. Given that agriculture accounts for the majority of freshwater use worldwide, utilizing water more efficiently in this sector can free up water resources for other purposes.

Additionally , the development and adoption of precision irrigation systems based on remote sensing represents a significant advancement in the field of agriculture and water resource management. This innovation has the potential to enhance food security and environmental sustainability. Thank you very much!

 

Comment/9.  The article needs to be revised for language and format, and errors in terms of grammar, spelling, punctuation, etc., should be checked.

 

Response:/The manuscript Proofreader, multiple times, to catch any errors that may have been missed during the writing process. These are general tips, and each manuscript will have its own unique set of grammar issues that addressed. The English grammar mistakes of the manuscript improved, Thank you very much!

  

Author Response File: Author Response.docx

Reviewer 2 Report (Previous Reviewer 4)

Comments and Suggestions for Authors

I found the topic of the study very interesting and in line with the scope of the journal. To improve the overall quality of the manuscript, I have some suggestion/comments as below:

The quality of the figures 1, 5, 6, 7, 8 may be improved, at least in my pdf they are getting a bit distorted.

Lines 252-280: need a better explanation on equation 1. It is hard to understand it.

Need a better explanation in table 1 to 2. It is hard to understand it and you should comment on the values of Statistical indices of measured NDVI Value, Classes Density, and area of each Classes and Statistical indices of measured NDMI Value, and area of each Classes.

English needs to be revised.

Author Response

Kindly, I am writing to you to submit the revised version of our paper entitled " Integrating remote sensing techniques and meteorological data to assess the ideal irrigation system performance scenarios for improving crop productivity" to your esteemed journal. We appreciate the opportunity to submit this manuscript for publication consideration and are confident that it aligns with the journal's scope and interests.
We would like to express our gratitude to the valued reviewers and the editorial team for their valuable feedback and support throughout the review process. We are confident that the revised manuscript has been improved considerably and meets the high standards of the Water Journal.
We are looking forward to your kind consideration.
Sincerely,  

 

Comments and Suggestions for Authors

I found the topic of the study very interesting and in line with the scope of the journal. To improve the overall quality of the manuscript, I have some suggestion/comments as below:

The quality of the figures 1, 5, 6, 7, 8 may be improved, at least in my pdf they are getting a bit distorted.

 

Response / Thank you very much indeed for your comment, The IMAGE have a resolution of 400DPI and are in the TIF format. The journal's guidelines do not allow images with a resolution of less than 300. Nevertheless, we collected 32 images of the NDMI index and 32 images of the NDVI index over 16 weeks of monitoring crop growth. In order to meet the research standards, we had to convert all 16 images into one format.

 

Comments /Lines 252-280: need a better explanation on equation 1. It is hard to understand it.

Need a better explanation in table 1 to 2. It is hard to understand it and you should comment on the values of Statistical indices of measured NDVI Value, Classes Density, and area of each Classes and Statistical indices of measured NDMI Value, and area of each Classes.

 

Response / Please see Page 8-9 the Line 287-299: To better understand NDVI values, they are often classified into different ranges. Typically, values between 0.1 and 0.4 indicate low vegetation density or sparse vegetation, while values between 0.4 and 0.6 indicate moderate density or healthy vegetation. Values above 0.6 indicate high density or very healthy vegetation. These NDVI density classes can be beneficial in monitoring vegetation growth over time or comparing different areas. For instance, if an area consistently has NDVI values within the low density range, it may indicate the need for irrigation or other interventions to promote plant growth. Conversely, if an area consistently exhibits high NDVI values, it may suggest a healthy and productive ecosystem[40].

 

Please see Page 10  Line 388-394:

 

The NDVI Value is a numerical metric used to evaluate the health and growth of vegetation, with higher values indicating denser and healthier vegetation, and it ranges from -1 to +1. Vegetation density is classified into various categories based on the NDVI value, including Dense, Moderate, Sparse, and Open vegetation. The area of each category is measured in hectares (ha), which is a commonly used unit in agriculture. Table 3 presents the Dense Vegetation Classes NDVI Area/ha for different months, including 1-Jan, 6-Jan,11-Jan, 26-Jan, 15-Feb, 25-Feb, 7-Mar, 1-Apr, 11-Apr, 16-Apr, 21-Apr, 1-May, 6-May, 11-May, 16-May, and 21-May. The values of the NDVI area/ha for the Dense category in those months are 1174.49, 386.00, 372.47, 549.33, 3191.68, 4536, 5334.15, 6028.25, 3850.27, 3327.33, 2362.02, 1789.78, 1702.87, 801.12, 171.77, and 58.61, respectively. The Dense category indicates areas with high vegetation density, which suggests very healthy vegetation.

 

 

Please see Page 14  Line 459-462:

The NDMI is a measure of vegetation's water content, and a higher NDMI value suggests a higher level of moisture in the vegetation. Such areas might be irrigated or have access to other water sources. Table 2 lists the NDMI values for particular months and areas, with greater values indicating particularly healthy vegetation owing to the abundant water. Specifically, Table 2 presents NDMI high values Classes Area/ha for the months of 1-Jan, 6-Jan, 11-Jan, 26-Jan, 15-Feb, 25-Feb, 7-Mar, 1-Apr, 11-Apr, 16-Apr, 21-Apr, 1-May, 6-May, 11-May, 16-May, and 21-May. These values are 0.28, 1.68, 3.72, 0.04, 65.96, 115.88, 239.64, 337.28, 877.52, 979.24, 5.12, 367.44, 37.7, 5.48, 2.52, and 1.84, respectively. The High Values in the table indicate areas with abundant water in the vegetation, which signifies particularly healthy vegetation.

 Thank you very much for your comment,

 

Comments /English needs to be revised.

 

Response /The manuscript Proofreader, multiple times, to catch any errors that may have been missed during the writing process. These are general tips, and each manuscript will have its own unique set of grammar issues that addressed. The English grammar mistakes of the manuscript improved,

Thank you very much for the comment!‎

 

Author Response File: Author Response.docx

Reviewer 3 Report (New Reviewer)

Comments and Suggestions for Authors

Integrating remote sensing techniques and meteorological data to assess the ideal irrigation system performance scenarios for  improving crop productivity is presented in this work

A brief summary of the proposed work used in this paper should be included in the abstract

A few more relevant keywords should be included

Key contributions of this work should be included at the end of the introduction part

The novelty of this work should be described in the introduction part.

The key attributes used in this work with its description can be included as a separate table

Many abbreviations used in this work can be tabulated again separately for the better reference

An equation editor can be used for formulas that are written in this work

How the remote sensing data is processed in this work?

Data collection process should be elaborated in the proposed model

The involvement of computing in this work should be emphasized

Many old references can be replaced with recent papers for proving the research is a contemporary one. 

 

Author Response

Kindly, I am writing to you to submit the revised version of our paper entitled " Integrating remote sensing techniques and meteorological data to assess the ideal irrigation system performance scenarios for improving crop productivity" to your esteemed journal. We appreciate the opportunity to submit this manuscript for publication consideration and are confident that it aligns with the journal's scope and interests.
We would like to express our gratitude to the valued reviewers and the editorial team for their valuable feedback and support throughout the review process. We are confident that the revised manuscript has been improved considerably and meets the high standards of the Water Journal.
We are looking forward to your kind consideration.
Sincerely,  

Comments and Suggestions for Authors

Integrating remote sensing techniques and meteorological data to assess the ideal irrigation system performance scenarios for  improving crop productivity is presented in this work

 

Comments /A brief summary of the proposed work used in this paper should be included in the abstract:

 

Response / The Abstract has been Changed and superscript ‎‎. Please see the new Abstract.

An effective way to increase agricultural productivity and ensure food security is by understanding the reasons behind variations in irrigation over time. However, researchers often avoid investigating water productivity due to the challenge of data availability. The study area, located in north of Erbil city, consists of 143 farmer-owned center pivots, and this study aims to assess the performance of the irrigation system for winter wheat crops using high-resolution satellite data from Sentinel 2 A/B, combined with meteorological data and remote sensing techniques based Google Earth Engine (GEE) to analyze the data. The seasonal estimates of high and low-performance farms under the center pivot irrigation system were investigated to understand key variables' spatial and temporal variation during two wheat-growing winter seasons in the drought year 2021. The investigation also thoroughly explored the reasons behind the variance in field performance. The analysis of temporal variability showed that water usage fluctuated significantly across the seasons. On the other hand, yield gradually increased from the 2021 winter season in the selected center pivot farms. The results showed that moisture stress, measured by the Normalized Difference Moisture Index (NDMI) and the Normalized Difference Vegetation Index (NDVI), has a significant impact on increasing yield productivity and reducing the yield gap, particularly during the middle of the growing season (March and April).The correlation between NDVI and NDMI in 143 selected fields revealed that sufficient irrigation water during February, March, and April significantly increases yield production. NDVI is associated with photosynthetic potential and other vegetative characteristics, while NDMI has a significant effect on crop growth. Strong correlation (approximately 0.91) was observed between NDMI and irrigation. NDMI Correlation with NDVI-wheat in 143 Center pivot wheat Field products with a 5-day temporal resolution is approximately 0.879. In conclusion The integrating remote sensing and meteorological data in supplementary irrigation systems can greatly benefit agriculture and water resource management. This method has the capability to boost yields and improve crop quality, as well as decrease water consumption and minimize detrimental environmental impacts. It is a promising innovation that could improve food security and promote environmental sustainability.

Thank you very much for the comment

 

Comment/ A few more relevant keywords should be included

Response: The Key word changed please check page 2 the line 51.

Thank you

 

Comment/ Key contributions of this work should be included at the end of the introduction part

Response / Please see Page 4 Line 159-172: This study provides a new strategy for agricultural resource management by providing consistent estimations of winter wheat water requirements and yield. This information can be used to optimize irrigation practices and improve crop management, particularly in arid and semi-arid regions where water availability is limited. Thank you very much!

 

Comment/ The novelty of this work should be described in the introduction part.

Response / Please see Page4-line 152-158

This study provides a new strategy for agricultural resource management by providing consistent estimations of winter wheat water requirements and yield. This information can be used to optimize irrigation practices and improve crop management, particularly in arid and semi-arid regions where water availability is limited. Thank you very much!

 

Comment/ The key attributes used in this work with its description can be included as a separate table.

Response / Thank you very much for the comment, here is an example table that lists the key attributes used in the work and their descriptions: Can be added in Appendix

 

Attribute	Description
Irrigation temporal variation	The changes in irrigation patterns over time
Yield gap	The difference between the potential and actual yield of a crop
Agricultural productivity	The amount of agricultural output per unit of input
Water management	The management of water resources for agricultural use
Data availability	The availability of data needed for research or analysis
Remote sensing techniques	The use of satellite or airborne sensors to gather information about the Earth's surface
Meteorological data	Data related to weather and atmospheric conditions
Sentinel 2 A/B satellite data	High-resolution satellite imagery used for monitoring land cover and land use
Google Earth Engine (GEE)	A cloud-based platform for analyzing geospatial data
Winter wheat crops	A type of wheat that is planted in the fall and harvested in the spring or early summer

 

 

Comment/Many abbreviations used in this work can be tabulated again separately for the better reference

Response /  certainly, here is a table that lists the abbreviations used in the work and their full forms:

Abbreviation	Full Form
GEE	Google Earth Engine
GPS	Global Positioning System
IR	Infrared
NDVI	Normalized Difference Vegetation Index
NDMI	Normalized Difference Moisture Index
NIR	Near Infrared
RGB	Red Green Blue
SWIR	Shortwave Infrared
C	Center pivot
ETo	Ultraviolet
ETc	Water Content
WUE	Water Use Efficiency
WW	Winter Wheat

Thank you very much for the comment

 

Comment/ An equation editor can be used for formulas that are written in this work

Response /The equation editor used for formulas written in this work

Thank you very much.

 

Comment/ How the remote sensing data is processed in this work?

Response /Please find the :

Figure 3. Flowchart of the methodology adopted in this study.

and section 2.3. Satellite Images data (Sentinel 2 satellite imagery acquisition)

 page 7 Line 252

Thank you very much

 

Comment/ The involvement of computing in this work should be emphasized

Response / Thank you very much for the comment, certainly, computing plays a significant role in this work. The study utilizes Google Earth Engine (GEE), a cloud-based platform for analyzing geospatial data, to process high-resolution satellite imagery and meteorological data. Remote sensing techniques are used to gather information about the Earth's surface, and the data is analyzed using GEE to assess the performance of the irrigation system for winter wheat crops. Additionally, the study may involve the use of computing tools and software to manipulate, analyze, and visualize data, such as statistical software, programming languages, and Geographic Information Systems (GIS). Therefore, computing plays a crucial role in the success of this study.

 

Comment/ Many old references can be replaced with recent papers for proving the research is a contemporary one. 

Response /Many new references Added please check the references list

it is always a good practice to use recent references to show that the research is current and relevant. Here are some examples of how old references in this work could potentially be updated:

Old Reference:

Jin, S.; Sader, S.A. Comparison of Time Series Tasseled Cap Wetness and the Normalized Difference Moisture Index in Detecting Forest Disturbances. Remote Sens. Environ. 2005, 94, 364–372, doi:10.1016/j.rse.2004.10.012.

New Reference: Kumar, S. et al. (2021). "Temporal variation in irrigation and its impact on crop productivity: A review." Agricultural Water Management, 255, 107039.

New Reference:

48- Elmetwalli, A.H.; Mazrou, Y.S.A.; Tyler, A.N.; Hunter, P.D.; Elsherbiny, O.; Yaseen, Z.M.; Elsayed, S. Assessing the Efficiency of Remote Sensing and Machine Learning Algorithms to Quantify Wheat Characteristics in the Nile Delta Region of Egypt. Agric. 2022, 12, doi:10.3390/agriculture12030332.

New Reference:

49. Maimaitijiang, M.; Sagan, V.; Sidike, P.; Hartling, S.; Esposito, F.; Fritschi, F.B. Soybean Yield Prediction from UAV Using Multimodal Data Fusion and Deep Learning. Remote Sens. Environ. 2020, 237, 111599, doi:10.1016/j.rse.2019.111599.By citing more recent references, the study can demonstrate that it is building on the most up-to-date research in the field.

 

Thank you very much for the comment!‎

 

 

 

 

 

Author Response File: Author Response.docx

Reviewer 4 Report (New Reviewer)

Comments and Suggestions for Authors

Manuscript ID: water-2316609
Title: Integrating remote sensing techniques and meteorological data to assess the ideal irrigation system performance scenarios for improving crop productivity.
OVERVIEW
This study provides a new strategy for agricultural resource management by providing consistent estimations of winter wheat water requirements and yield, using high-resolution Sentinel 2 A/B satellite data and vegetation indexes in Erbil Governorate, northern Iraq.   
GENERAL COMMENTS
The subject matter is actual, interesting and within the scope of the Journal Water.
The manuscript complies with the journal template.
The title is adequate.
The English spelling and grammar are fine.
The manuscript is original, and plagiarism was not detected.
The objectives are clearly stated.
The manuscript provides information on its replicability and reproducibility.
The analyses are appropriate and well-described.
The tables and figures are fine.
The interpretation and results are supported by the data.
The conclusions report the major findings of the study.
The strengths and limitations of the study are reported.
The manuscript structure, flow and writing are fine.
The manuscript addresses a very important subject for the efficiency of the use of water.
In conclusion, I believe this manuscript is interesting and worthy of publication after minor changes. Please read the specific comments.
SPECIFIC COMMENTS
Line 626, Figure 10: where reads “NDV”, should read “NDVI”
Line 656, Figure 12: Please correct the symbols in the horizontal axis scale.
Line 653: Please explain the meaning of EXlSTAT.
Line 701: Figure 14 must be introduced and explained in the text before.

Author Response

Kindly, I am writing to you to submit the revised version of our paper entitled " Integrating remote sensing techniques and meteorological data to assess the ideal irrigation system performance scenarios for improving crop productivity" to your esteemed journal. We appreciate the opportunity to submit this manuscript for publication consideration and are confident that it aligns with the journal's scope and interests.
We would like to express our gratitude to the valued reviewers and the editorial team for their valuable feedback and support throughout the review process. We are confident that the revised manuscript has been improved considerably and meets the high standards of the Water Journal.
We are looking forward to your kind consideration.
Sincerely,  

Comments and Suggestions for Authors

Manuscript ID: water-2316609
Title: Integrating remote sensing techniques and meteorological data to assess the ideal irrigation system performance scenarios for improving crop productivity.

 

 

OVERVIEW
This study provides a new strategy for agricultural resource management by providing consistent estimations of winter wheat water requirements and yield, using high-resolution Sentinel 2 A/B satellite data and vegetation indexes in Erbil Governorate, northern Iraq. 

 
GENERAL COMMENTS
The subject matter is actual, interesting and within the scope of the Journal Water.
The manuscript complies with the journal template.
The title is adequate.
The English spelling and grammar are fine.
The manuscript is original, and plagiarism was not detected.
The objectives are clearly stated.
The manuscript provides information on its replicability and reproducibility.
The analyses are appropriate and well-described.
The tables and figures are fine.
The interpretation and results are supported by the data.
The conclusions report the major findings of the study.
The strengths and limitations of the study are reported.
The manuscript structure, flow and writing are fine.
The manuscript addresses a very important subject for the efficiency of the use of water.

 

In conclusion, I believe this manuscript is interesting and worthy of publication after minor changes. Please read the specific comments.

SPECIFIC COMMENTS
Comment/Line 626, Figure 10: where reads “NDV”, should read “NDVI”

Response: Corrected with Thanks :Figure 10. Temporal Variation of the NDVI and NDMI Value Based Vegetation Density Classes of 143 Center pivot wheat Field in 2021.

Thank you very much

Comment/Line 656, Figure 12: Please correct the symbols in the horizontal axis scale.

Corrected With thanks
Response: Line 653: Please explain the meaning of EXlSTAT.

We have corrected. Thank you very much indeed for your comment.

 

EXLSTAT is a software add-on that can be integrated with Microsoft Excel to offer various statistical and data analysis tools. It enables users to conduct sophisticated statistical analyses and generate advanced data visualizations within Excel, eliminating the need for other software. EXLSTAT offers a range of features, including descriptive statistics, hypothesis testing, regression analysis, ANOVA, and other tools. It has extensive applications in academia, research, and industry, helping users to analyze data and make informed decisions.

We have corrected. Thank you very much indeed for your comment.

 

Comment/Correlation coefficients between NDVI, NDMI, Irrigation, ETo, and ETc during the growing season from Jan to May (average of 5 Months) are calculated using the EXLSTAT and are presented in Figures 12, 13, and 14.

Check the line 782

Thank you very much
Response: Line 701: Figure 14 must be introduced and explained in the text before.

Thank you very much indeed for your comment.

Author Response File: Author Response.docx

Reviewer 5 Report (New Reviewer)

Comments and Suggestions for Authors

The work is good and well written.

Reviewer 6 Report (New Reviewer)

Comments and Suggestions for Authors

This paper provides detailed evaluation of the water needs and water situation in the Erbil Plain, a dry but very fertile area in northeast Iraq, watered by small rivers from the Zagros Mountains. Water from the rivers is supplemented by center-pivot irrigation, using ground water. The assessment was done in the drought year of 2021. In the area, 51% of water for agriculture comes from rainfall, 48% from groundwater, and only 1% from the rivers. Irrigation using pumps and center-pivot systems has lowered the water table 50 m, which is very scary. Extremely rapid depletion of groundwater by pumped irrigation is all too typical of the drylands of the world; a recent major study showed depletion of groundwater and lowering of the water table in essentially all farmed drylands, especially the US Southwest, the Middle East, and Central Asia. Some areas are running out of usable groundwater, a fate that will overtake the Erbil Plain eventually if pumping continues at current rates.

This study compares the Normalized Difference Vegetation Index, which assesses how well the plants are growing, and the Normalized Difference Moisture Index, which assesses how much water they are taking up and holding. The findings are as expected--little water use in winter when farming is more or less inactive; much uptake but good water availability in spring; and heavy water needs in the extremely hot and dry summer months. The paper shows that especially in a dry year like 2021 there is a very large need for irrigation. This is leading to groundwater overdraft, and a bleak future as global climate change progresses. The future is distinctly uncertain. Cutting back to rainfall-fed agriculture would be possible, but would considerably reduce Iraq's food production. I might note that Islamic law, going back to the Quran and early interpretations, is quite strict and very reasonable about water and irrigation use, having been developed in Arabia (partly in what is now Iraq). It might provide useful ideas for the future. This is an important paper for showing the extent of water use in a critically important but threatened production area.

The English is very good--the only confusing mistakes are that the caption for Fig. 2 should obviously be "for the year 2021" rather than "for 2021 years" (!) and on p. 15, line 10 from bottom, "phonological" should be "phenological." Otherwise, there are some spelling errors, and some places where repetition could be cut, but nothing very serious.

Round 2

Reviewer 1 Report (Previous Reviewer 5)

Comments and Suggestions for Authors

The authors have answered all my questions adequately

Reviewer 3 Report (New Reviewer)

Comments and Suggestions for Authors

The authors carefully addressed the concerns raised and it can be accepted now.

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The paper addresses the research area related to the efficient irrigation of wheat in Kurdistan. Using high-resolution Sentinel 2 A/B satellite data, the authors intend to provide a scientific method for analysing the spatial and 19 temporal variations of consumptive irrigation system performance at the field level.

It is based on the temporal evolution of NDVI and NDMI trends as effective tools to monitor irrigation and its effects on crop yield and water productivity.

As a general comment, the paper has serious flaws and the research conceptualization does not seem appropriate.

The article appears very confusing and difficult to follow. The Methodology and Results are not well presented or organized. While some sections seem unnecessarily detailed (for example, the collection of soil samples and the subsequent chemical-physical analyses), other parts that would have deserved further study are inadequately exhaustive (such as the calculation of evapotranspiration and irrigation water requirements, which is totally missing). 

The provided analysis of the time series of NDVI and NDMI indices offers little insight, with no effective discussion on the capabilities of the indices to improve irrigation efficiency and the satellite-based estimation of irrigation volumes.

The English have many errors, the tables are unclear, and the figures lack the essential legends and captions for their understanding.

There would be a very long list of errors and misspellings: the article needs to be rewritten entirely. It's a real pity because it's really difficult to collect data about water meter records 

For the above-mentioned reason, please consider rejecting the article.

 

Reviewer 2 Report

Comments and Suggestions for Authors

I appreciate this work that shows professionalism. I advise that it will be accepted in its present form.

Reviewer 3 Report

Comments and Suggestions for Authors

1.      The key problem intended to be solved in this paper cannot be clearly defined in the introduction part. The combination of remote sensing and crop models was not outstanding.

2.      In this paper, only the main growing season of winter wheat from January to May in 2021 was used for relevant research, and the scientific nature and representativeness of the data were not analyzed.

3.      It was recommended that Figure 3 be deleted.

4.      What was the reason for choosing only April for soil samples?

5.      The technology roadmap in Figure 4 was too simplistic and should show the logical relationships between the relevant data.

6.      What was the role of Table 1, which did not seem to be reflected in the text.

7.      The results and discussion sections were unreadable, and the relevant results were aslo not discussed in depth.

8.      The conclusion part was too long, and no clear innovative conclusion had been extracted, so it should focus on remote sensing and central sprinkler irrigation.

Reviewer 4 Report

Comments and Suggestions for Authors

I found the topic of the study very interesting and in line with the scope of the journal. To improve the overall quality of the manuscript, I have some suggestion/comments as below:

 

The quality of the figures 1, 4, 6, 7, 8, 9 may be improved, at least in my pdf they are getting a bit distorted.

Section 2.4.1 Reference evapotranspiration (ETo), and crop evapotranspiration (ETc ) is very dense and requires an expert knowledge of the subject. It would be advisable to use a diagram or graph to clarify this.

Lines 273-291: need a better explanation on equations 1 and 2. It is hard to understand it.

Need a better explanation in table 3 and 4. It is hard to understand it and you should comment on the values of indicators, Statistical indices of measured NDVI Value, and NDMI Value and Statistical indices of measured NDMI Value.

References: bibliographic citations should be reviewed (format of the year, ...)

English needs to be revised.

Reviewer 5 Report

Comments and Suggestions for Authors

The topic is interesting. However, there are many problems that need expatiation. I have provided the following comments to help improve the quality of the paper.

1. It is not recommended that authors use many references in one sentence.

2. Lines 75-76, is this accurate proportion (51%, 48%, 1%) cited from other literature?

3. Please double-check the structure of the full text, at 178 lines, the headings are missing 2.3 and 2.3.1.

4. Figure 5, missing legend, scale bar, north arrow, please add. Figure 8 and Figure 9 Lack the scale bar, the font size of the lower left corner is a bit small, it is recommended to enlarge a line.

5. Line 290, explain the formula letter meaning and the range of NDMI.

6. Line 304, the structure is confusing, whether the header position and the content are correct.

7. Table 1, the authors have done a lot of work on the physical and chemical properties of soils, is this relevant for the analysis of later NDVI NDWI?

8. Why select only 1 image in March 2021?

9. There is a lack of discussion on the limitations of using remote sensing techniques to assess the performance of irrigation systems.

10. There are some typos and grammatical problems. Authors are encouraged to proofread carefully.