A Global Systematic Review of Improving Crop Model Estimations by Assimilating Remote Sensing Data: Implications for Small-Scale Agricultural Systems

Round 1

Reviewer 1 Report (Previous Reviewer 3)

The authors have improved this manuscript. They have made an important effort.

Minor editing of English language required.

Author Response

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Author Response File: Author Response.docx

Reviewer 2 Report (New Reviewer)

In reviewing this manuscript I have followed this outline: general comments to the manuscript,  specific comments to some sections, and minor spelling remarks. Overall, the manuscript is an interesting original review and its analysis and conclusions are valuable to address future research in small-scale African agricultural systems.

General comments to the manuscript:

The manuscript “A global systematic review of improving crop model estimations by assimilating remote sensing data: implications for small-scale agricultural systems” presents a systematic literature review on assimilation of remote sensing data into crop growth and yield models aiming at: i) to describe how the application of data assimilation has varied across different countries, crops, and agricultural systems and, ii) highlight the implications of the adoption of process-based crop models and data assimilation in small-scale agricultural systems.

The authors review a total of 123 publications and present a thorough picture of remote sensing datasets and their assimilation procedures along with the geographical location of the studies, the crops studied, the crop models utilized fulfilling successfully the first aim of the manuscript (objectives 1 to 4). As for the second aim (objective 5), in my view, the authors have highlighted the research gaps in this domain rather than addressed the implications, understood as effects or consequences, of using PBCMs and data assimilation in small-scale African agricultural systems.

Comments to the Abstract:

It can be appropriate adding a motivation for this review. Explain in the abstract, maybe between the first and second sentences, why it is important to perform this review. A growing interest in the subject seems to be not enough to justify this work. Does it have economic, social, or food security implications? I’m sure it has but must be stated, possibly with some indication of magnitudes.

Comment to the Keywords:

Crop models, Remote sensing, and Systematic review keywords are also contained in the title. Please reconsider alternative keywords that are not contained in the title.

Comments to the Discussion:

Consider stating the URLs of the Python Crop Simulation Environment (PCSE) and the WOFOST open source repository.

Minor spelling remarks or suggestions:

line 513: need to the compare performance…

line 517: … for example, consist of multiple crop… substitute with “consists” or “consisted”

Lines 575-578: However, the application of data assimilation followed agricultural technology and innovations the technology advancement trend, with highly technologically advanced countries having more studies than those less developed (i.e., African countries).

Author Response

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Reviewer 3 Report (New Reviewer)

There have been increasing efforts of data assmilation to improve crop growth accuracy and yield estimations. This study aims to systematically review the data assimilation research and summarize how its application varies among countries, crops, and agricultural systems. However, the basic introduction and benefit of data assimilation is deficient. More analysis and details should be added to highlight the challenge and progress of the application in small-scale agricultural systems. Thus, major revision is recommended.

Major comments:

1.   It is recommended to give a basic introducition and benefit of data assimilation by a schematic diagram to intuitively understand how data assimilation works.

2.     I notied that many studies could not be classified in Figure 4. Considering that all studies state their study area, so I wonder if we can use some auxiliary data to categorize each study, such as irrigation maps and crop distribution.

3.     The definition of small-scale agricultural systems is unclear. What does small-scale mean? Total area of the arable land or refers to the fragmentized field.

4.     The challenge for small-scale agricultural systems should be more specific. Is this due to the absence of remote sensing imagery or to immature assimilation methods. 

Author Response

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Round 2

Reviewer 3 Report (New Reviewer)

Revised as suggested. Good to Go.

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

This review mapped the research that integrate remote sensing into crop model. It has touched on many important aspects such as the geographic distribution of the research, remote sensing (RS) data, crop model, etc. It can be improved by including a general evaluation of model performance with different RS data source, e.g. higher vs. lower spatial resolution; optical vs. radar images. The following are more specific comments/recommendations:

Line 13, Consider remove timely

Line 15, consider remove when combined

Line 24, please spell out WOFOST for those who don’t know what it stands for.

Line 26, only leaf area index? There should be a group of remote sensed data that can be integrated into crop models,

Line 111, please rephrase this sentence: the numbered references usually are not placed at the beginning of a sentence.

Line 111-112, please briefly summarize the existing reviews: what was their focus, and what is missing in their review that this paper can provide.

Line 159, since there is only one criterion, there is no need to use (i),

Line 177, here why use “system review” instead of system mapping?

Line 181, why only consider process-based models?

Line 198-200, not sure this information is relevant to the topic of the mapping/review.

Line 244, what is this PCSE platform, please spell out the acronym

Line 245-246, the DSSAT is the algorithm for yield; presumably SAFY and AquaCrop are models that implement the DSSAT algorithm? But still please make this more explicit.

Line 247, remove better

Line 274, change high to higher

Line 275, change low to lower

Line 275, cloud cover affects all optical remote sensing images,

Line 269-281, how is the model performance by integrating different remote sensing data, e.g. which data type enable more accuracy result in crop yield?

Author Response

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Reviewer 2 Report

This study provide a beneficial and rich review on assimilating RS data into crop models varies among countries, crops, data assimilation methods, and agricultural systems. The manuscript falls well within the scope of this journal. This paper is well structured, the main part of the methods and analysis is good readable. I think it needs some minor modifications before it can be accepted for publication in this journal.

1.     The monitoring accuracy of the model should be concerned and discussed. The model accuracy comparison for different data assimilation methods and parameter inputs should be considered in this study.

2.     In small-scale agricultural monitoring, UAV remote sensing data has good application prospects. It is suggested to discuss the advantages and development prospects of UAV data in small-scale agricultural monitoring.

3.     Line 85-86: delete (Camino et al., 2018; Berger et al., 2020)

4.     Line 86-87: delete (Elarab et al., 2015; Clever et al., 2017)

5.     Line 94-95: “Additionally, RS data cannot look into the future or evaluate various management scenarios”.  Corresponding references should be added.

6.     Line 250: table 3 should be table 2.

7.     Line 280: muiltispectralàmultispectral

8.     Line 282: Error in indenting the first line.

9.     Line 363: isàare

10.   Line 438:cloud-based

Author Response

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Reviewer 3 Report

I have doubts that this work is suitable for publication in Remote Sensing. The contributions expected in this journal are of a different kind as the authors can see.
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Comments for author File: Comments.pdf

Author Response

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Reviewer 4 Report

In this study the authors aim to systematically map how the research that exclusively focuses on assimilating remote sensing (RS) data into crop models varies among countries, crops, data assimilation methods, and agricultural systems. The results presented in this paper indicate that over 81% of the studies were conducted in countries associated with high socio-economic and technological advancement. The application of data assimilation in resource- and data-limited regions like Africa remains limited. This is a comprehensive and interesting paper. Therefore, the subject of the paper is clearly inside the scope of the journal, and it could be interesting for other researchers worldwide. The structure of the paper is correct, and the results and conclusions are significant. The manuscript is clear, relevant and presented in a well-structured manner.

Just have general comments:

Page 1, line 4: “, Lammert” shoud be “ and Lammert”

Tables should be editable; do not use uneditable tables.

The abstract should be a paragraph of about 200 words maximum.

Page 14, line 457: “DIO” should be “DOI”

Author Response

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Round 2

Reviewer 3 Report

I believe that the issues I raised in the previous review have not changed substantially. I think it is a good contribution but it is not in line with the works published in this journal. I think I would contribute more in another less specialized magazine.