PONE-D-22-11912Using Sentinel-1, Sentinel-2, and Planet Satellite Data to Map Field-Level Tillage Practices in Smallholder SystemsPLOS ONE

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Additional Editor Comments:

A fairly well presented work. Please ensure the technical queries of the reviewers are addressed before final acceptance.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: Introduction:

There is relevance and clarity in what authors wish to investigate.

Materials and Methods:

The appropriateness of duration of Ground truth data, which were collected during April to May 2018 (after harvesting of the season being investigated), for winter crop may be justified (realising fully well that it is one of the most difficult information to get).

There is clarity and correctness in the method adopted.

Results: Line 304 (“... in many cases reduced overall accuracy compared to individual sensor models that...”) – Plausible reasons may be forwarded. Is it because field data is not adequate (enough)? If one were to have more field data, would such things happen (a scenario similar to Hughe’s effect starting to become evident)?

Similar comment for Table 4 wherein Sentinel - 2 accuracy is greater than Sentinel 1 + Sentinel 2 (The reason for this decrease in accuracy may be provided / speculated.).

Line 320: What causes blue band of PlanetScope to come out as the most valuable for discrimination (“...PlanetScope data, band 1 (blue) from October 8th was always the most important feature,...”) needs to be discussed. Could it be due to (relatively) poor atmospheric correction of blue band (lowest wavelength)?

Discussion and conclusion:

The results are logical, corrigible and supported by a comprehensive analysis.

Reviewer #2: This is a very nice paper assessing the usefulness of Planet, Sentinel-2 and Sentinel-1 satellite images for classifying zero tillage (ZT) vs conventional tillage (CT) field management practices in a region in the Indo-Gangetic Plains in India. The authors have done a good job in collecting a large number of satellite images and in situ data, which allows to draw solid conclusions. My only major comment is that the authors should stress the limitations of the study even more than already done. In the end, the study just considers wheat fields in the winter season 2017-18 in this region. The amount of crop residue left from the monsoon season seems to be the main indicator based on which it is possible to distinguish ZT and CT. This might be different in other regions, seasons or crops.

Specific comments

The paper is overall very well written. Yet sometime it is used the same phrases in a repetitive manner: e.g. “We used …” three times in the lines 239-243, or “results suggests …” also three times from lines 355 to 363. But there are many more examples. So please go through the text and try to reduce these repetitions.

Line 164: “Full range of” what?

Figure 3: Explain in the accompanying text already here why NDVI is higher for ZT than CT in October and November.

Line 272: Delete “conducted analyses that”

Line 402: “Who” instead of “which”

Line 237: It is problematic to state “We believe that other ML algorithms would produce other results”. Personally, I think you are right but you cannot know until you do it.

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Reviewer #1: Yes: Markand Oza

Reviewer #2: No

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Using Sentinel-1, Sentinel-2, and Planet Satellite Data to Map Field-Level Tillage Practices in Smallholder Systems

PONE-D-22-11912R1

Dear Dr. Jain,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Jaishanker Raghunathan Nair, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments: