Exploring the Regional Dynamics of U.S. Irrigated Agriculture from 2002 to 2017

Round 1

Reviewer 1 Report

This study investigated regional and periodic patterns in the amount of change in irrigated agriculture as well as linking gains and losses to proximal causes and consequences in US. Overall, this research is meaningful and interesting. Please make the below revisions:

1. The Methods Section is not very clear. Please draw a workflow to include the data and methods (step by step) you used to help readers follow easily.
2. Sections 3.1 & 3.2 are repeated. Please double check!
3. The figures are not very clear especially for the US maps, legends & scale bars. Please consider improving the resolution. In Figure 3 legend, the irrigated area unit is missed?
4. For Discussion Section, there is only one subsection. If so, you do not need 5.1 Irrigated Area Dynamics. You may consider offering one more subsection to help readers follow easily.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 2 Report

The study adds some very useful metrics on the dynamics of the areas irrigated in recent phases  of irrigation in the US. The study period was one of exceptional financial and economic volatility. The first decade of the 21st century witnessed increases in the international food commodity prices from about 2002 and the financial crisis of 2008. These events in the financial industry had impacts on farm incomes. By 2011/12 farmers were enjoying farm incomes that enabled them to invest in farm infrastructures - including on irrigation equipment. It would be very useful to include these contexts. 

The English language is generally good but another copy edit would make some improvements

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Reviewer 3 Report

The paper deals with the assessment of irrigation area dynamic over the US in almost twenty years based on MODIS dataset.

Interesting results are obtained; some points are below raised to improve the paper.

 

1. L144: Please define eMODIS
2. (L145) Among the founding hypotheses of the model, the issue of multi-crop areas is not addressed. Having different crops in the same county could create problems, as some crops tend to reach higher NDVI peak values than others because of different density patterns among the cultivations. In that case, higher-peak-NDVI crops would be “privileged” in the attribution of irrigated land by the algorithm with respect to (high but) lower-peak-NDVI ones. A brief mention of this is made at L151, but it is unclear how the issue is tackled
   
   
3. Generally, an analysis of the algorithm performance as sorted by crop type data (if and where available) could be useful to identify possible vulnerabilities of the process. Provided the great heterogeneity of the analyzed regions, it would be helpful to further characterize the results

 

4. Chapter 3.2 accuracy assessment is repeated twice (L165-215, L228-252)
   
   
5. (L322, and 458) Could the accuracy differences among the years be attributed to different climatic conditions such as yearly rainfall or average temperature? This may also be true when comparing the different areas (California 94 % accuracy while HPA-TX,NM,OK 82%), given the wide range of climatic regions available in CONUS.
   
   
6. L254: isn’t there a national-database number similar to that displayed in Figure 1c. so as to provide a reference term for the MIrAD result?
   
   
7. L347: could you provide some considerations about the difference between Omission and Commission Errors? What do you think causes these?

 

8. I think that it would be interesting to include table.s1 into the paper as well as one of the three table.s2 to better quantify the changes and differences among years and areas.

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

This revised version solved my questions. Thank you!