如何写好 Response to review——发表SCI文章实战

     发表文章有不少步骤，走走停停，有时候会因为得到审稿人的赏识和认可开心不已，当然也会因为意见尖锐，无法修改而苦恼不已，下面我总结了一些例子，看看如 何回答 review report 里面的问题，所有内容均是自己文章投稿的真实过程，希望对大家有所帮助。

1. 关于 Cover letter

     整理了一份一般的格式，大体都是这样，呵呵

Dear Editor                     

Dr. Yinon Rudich                                                 Nov. 25, 2009

JGR

 

Manuscript. Number: 2009JD013023,

“Gross primary production estimation from MODIS data with vegetation index and photosynthetically absorbed radiation in maize”

 

Enclosed is the revised version of the paper entitled “Remote estimation of gross primary production in maize, coniferous forest and grassland using MODIS images”. We appreciated the thorough reviews provide by the journal and the positive response of both two reviewers that found the research of this manuscript. is suitable for JGR. Below is our response to their comments resulting in a number of clarifications.

 

Regards

 

 

Dr. Chaoyang Wu

hefery@163.com

2. 关于 Response  细节

        最根本的一个要求是事实就是，有什么说什么，不要企图遮遮掩掩，也不要回避，对意见一般先要礼节性的感谢或者同意，然后再做出修改。格式一般要求对不同的 审稿人的意见作出一一回答，一定要细致，千万不要以为能够蒙混过关，自己把不能解决的问题删掉，这样的回复估计就要被拒掉了。还是老老实实的回答，即使暂 时不能回答的，如一些方法改进之类的，委婉的说一下，如今后的实验会注意等等。 对于粗心的错误，自己就痛快承认了，没什么大不了的。哈哈，坦诚一点，给 人的印象好一点。

下面是一个列子，希望能对大家有所帮助。

Manuscript. Number: 2009JD013023 

Manuscript. Title: Gross primary production estimation from MODIS data with vegetation index and 

photosynthetically absorbed radiation in maize 

------------------------------------------------------------------------------ 

Associate Editor (Remarks to Author): 

Three reviewers provide reasonably consistent views about your manuscript, although their choices 

of the category differ. I believe that the paper is worthy of publication in JGR as the correlations

 between GPP and VIs are significant and could be useful for arid region crop growth estimation. 

However, these empirical relationships would have limitations, and these limitations are not clearly

 stated. In areas where radiation is variable, GPP may depend on not only vegetation greenness

 but also meteorological variables. The limitations should be stated clearly in the revision. You

 should revise your manuscript. according to suggestions of these reviewers. 

Response: We appreciate the positive comments about the manuscript. We also consider it is very 

important and necessary to state the limitations of this method. With help of Prof. Anatoly Gitelson, 

we decided to add a further validation of our method in forest and grassland ecosystems 

in the manuscript. Although this decision was not suggested by the reviewers, we think that by 

applying the method to the three species, our method can be better evaluated and compared with 

other publications. This new validation part may also suggest some explanations to some concerns 

of the review report. For example, the relationship between GPP and VI*VI*PAR shows species 

specific. Regretfully, we did not get enough auxiliary data in the forest and grassland sites, and 

these two sites are used for model validation. We can modify the manuscript. just following the 

suggestions in review report, but we think it will be better and more interesting by adding this part.

 Reviewer #1 (Highlight): 

The cross-product analyses of remotely-sensed VIs for improved GPP estimations in Maize fields. 

Reviewer #1 (Comments): 

Overall this is an interesting paper with some nice findings about cross-multiplying VI's to better 

relate remotely sensed vegetation information with tower measures of GPP. The main weakness

 is that there seems to be excessive use of "correlations" of many separate relationships which are

 then combined. A more rigorous evaluation of the VI x VI approach would have been preferable 

and more worthy. However, there are still interesting results presented. 

My specific comments are as follows: 

1. In the Abstract, PAR should be "...active radiation" and not "...absorbed radiation". 

Response: we followed the suggestions.

2. The equation provided and used applies to "SAVI" and not "MSAVI". 

Response: we changed the MSAVI to SAVI throughout the paper, including in the text and figures.

3. Note that Sims et al. (2006) had an earlier paper in which they utilized both NDVI (for fPAR) and 

EVI in some combined fashion to predict GPP. This VI x VI case should be discussed and evaluated, 

as this study has also tested the product (NDVI x EVI). 

Response: we have tried to find the reference the reviewer suggested but failed. Instead, we think it 

may probably the paper of “A new model of gross primary productivity for North American 

ecosystems based solely on the enhanced vegetation index and land surface temperature from 

MODIS, RSE, 2008” which already listed in our reference. In that paper, a model (TG Temperature 

and Greenness) of EVI×LST was proposed for the estimation of GPP (below name Fig.6) because

 the MODIS LST correlated well with PAR (below name Fig.1). We find two more papers of Sims

 et al., 2006 (Parallel adjustments in vegetation greenness and ecosystem CO2 exchange in response 

to drought in a Southern California chaparral ecosystem, RSE and On the use of MODIS EVI to 

assess gross primary productivity of North American ecosystems, JGR), but no method of VI×VI 

was found. We think Our VI×VI approach validated TG model indirectly because we used the in 

situ measured PAR (in TG model, the LST was used as a proxy of PAR), the VI×VI constitutes 

a non-linear stretch of a single VI, increasing its sensitivity at high vegetation green biomass. We 

added some explanations in the discussion part.

4. In the Eddy covariance methods, there is no mention of what portion and what averaging of the

 diurnal data was used in this study? 

Response: we agree with this suggestion and provided more detail information about the EC and PAR 

data used. First, we got the time of MODIS overpass time. Then five readings of NEE/T and 10 

readings of PAR around the time were selected. The averaged values were used for GPP calculation.

5. In the MODIS methods section, how were the clouds and thin clouds identified and removed. 

There should be mention of the use of MODIS quality assurance data information provided with the

 MOD09 reflectances. There is no mention as to whether the 3x3 or 9 pixels were averaged. 

Response: Yes, we added the method of cloud detection and relevant reference was also included. 

Also the average values of nine pixels were averaged for later analysis.

6. Using MODIS LAI/fPAR product to derive an independent LAI- fPAR relationship is very questionable 

and needs to be better assessed. In the first place, one should better define what was used, which may 

have been MOD15 500m product. Secondly, for the aims of this study, the authors need to establish the 

LAI- fPAR relationship with more independent, and maybe in-situ data, and not with satellite data, as

 MODIS LAI/fPAR products are just more VIs and the fPAR product has been shown to have problems 

in agriculture and other areas (just as stated in Xiao's and Zhang's papers). There is a lot of biophysical 

information available over maize canopies (see Gitelson articles referenced), and the authors should first 

evaluate use of MOD15 LAI/fPAR products through cross-comparisons with in-situ measurements from

 maize fields. If the authors believe that MOD15 is suitable for use as is, then these 2 "indices" can 

perhaps be directly used in their GPP model study, i.e., fPAR is directly available from satellite (MOD15), 

so why not use this and avoid use of NDVI and EVI. 

Response: we are very appreciated with these important suggestions by the reviewer and agree with this. 

We made some corresponding changes in the manuscript. and below are our clarifications. First, the 

LAI-fAPAR relationship was used for calculation of LUE, which is a variable for cross validation of our 

model. Due to the experiment design (see paper of Li et al., 2009, JGR, WATER experiment), some 

biophysical information were not observed, especially the fAPAR. However, it will be important in our

 paper to explore the potential of VIs in the estimation of both LUE and fAPAR. Therefore, we used an 

indirect method to calculate these two variables. As suggested by the reviewer, we used the in situ 

measurements of LAI (details of LAI measurement was also added) for fAPAR calculation with equation 

of fAPAR=0.95(1-Exp (-0.5LAI)). This method demonstrated to be workable with other publications 

(Ruimy et al., 1999; Xiao et al., 2004). The LUE was calculated by the following equation of 

GPP=LUE*fAPAR*PAR, GPP were from EC measurements, fAPAR from in situ LAI, and PAR from 

in situ meteorological measurements. We think that it was not appropriate to use MODIS product for 

calculation.

7. Section 3.2. "...we displayed the NEE and T in the daytime when the MODIS data acquired..." Again,

 the authors do not provide how "daytime" is defined and how NEE, GPP, PAR were averaged to generate

 a daily comparison with the satellite. Have the authors considered that satellite overpass time varies day 

to day across time zones (hourly intervals)? 

Response: we added the data processing information in section 2.3 and the daytime here is the MODIS

 satellite overpass time.

8. There needs to be a minimal level of aggregation of the fluxes in order to be able to compare with

 satellite data, and the authors need to provide this information. 

Response: we consider it is necessary to give more detail information about the data used. In section 

2.3, with the MODIS overpass time, we used 5 readings of NEE around and 10 readings of PAR for 

aggregation and averaged in later analysis.  

9. Section 3.3. How were LUE values computed and derived? is this information provided? 

Response: see our explanations with concerns of No.6.

10. Section 3.4.1. "NDVI had the least potential for LUE assessment in this paper as NDVI was largely 

affected by background information..". There is no data nor bases to support this? NDVI may be affected

 by different soils, but the authors did not establish that (1) there are soil variations of concern within this 

study and (2) and that these were responsible for the poorer NDVI results. 

11. Same applies to: "First, EVI is an index that can better overcome the background disturbances and 

sky conditions than other indices." The authors have not shown that sky conditions were a factor in this 

study and are merely guessing why the indices yield different results. As it was not objective of this 

study, such interpretations should be removed from Results; although one is free to conjecture such

 ideas in the Discussion section (so move such comments to discussion section). 

Response: we checked throughout the manuscript. and removed these parts into a new discussion part.

12. Conclusions section: "In this paper, our method worked well for the wheat that was a relatively 

homogenous canopy," As far as I know this is a study about Maize?? 

Response: We are sorry for this mistake.

13. There should be some error bars in Figs 3-5. 

Response: we added the error bars as suggested.

14. It was annoying that no page numbers were used. 

Response: page numbers were added in this revision.

Reviewer #2 (Comments): 

This paper investigates the relationship between gross primary production (GPP) and four vegetation

 indices (NDVI, EVI, MSAVI, and WDVI), using data from a maize site. Authors aim to seek a simple

 relationship between GPP and vegetation indices, which is an important effort in linking satellite 

observations with CO2 flux tower measurement. However, I have a number of concerns on the

 manuscript. 

1. English writing needs substantial improvement, and I would suggest that authors seek a help from a 

native English speaker to work on it. The manuscript. does not have page number and line number, 

which makes it difficult to write comments for the manuscript. The reference citation in the text does 

not follow JGR format. 

Response: we got help from a native speaker to proofread the paper and the reference citations were 

changed to follow the JGR format.

2. Page 2 Introduction: 1st paragraph needs to be re-written and expanded to include more scientific

 justification on their study of GPP. 

Response: we added more information about the GPP definition and current mechanism of GPP 

estimation based on LUE model.

3. Page 4 literature review on the VPM model. Note that Yan et al., 2009 used the VPM model to simulate 

GPP of both winter wheat and maize crop in a study site in North China. The paper might be of interest

 to the authors. 

Yan, H., Fu, Y., Xiao, X., Huang, H., He, H. and Yu, G., 2009, Modeling gross primary productivity of 

winter wheat and maize double-cropping system, using MODIS time series imagery and CO2 eddy flux 

data, Agriculture, Ecosystems and Environment, 129(4): 391-400. 

Response: yes, we added this reference to give a better introduction of both VPM model and GPP 

estimation.

4. Page 5. It mentioned " The LUE could be estimated by spectral VIs ..'. it needs to specify it. 

Response: We added three VIs (PRI, NDSI and MTCI) as examples for LUE estimation to specify 

the use of VIs. 

5. Page 5 last paragraph, "The paper is organize as follows ...", it would read better if authors start it 

in a new paragraph. 

Response: we followed this suggestion in the manuscript.

6. Page 6. Section 2.1. It is too short, and needs to expand for providing more info about the site, 

particularly vegetation and soils. 

Response: additional information were added to better description of this site.

7. Page 6, Section 2.2, how frequently does LAI is measured? 

Response: The LAI was measured everyday during the experiment time.

8. Page 7, section 2.4. Is the daily MODIS data used in the study? Is the MODIS onboard Terra or the 

MODIS onboard Aqua satellite used? When 3x3 pixels (1.5km) are used? What is the fraction of maize

 within the 1.5 km radius? 

Response: we used the MODIS/Terra daily surface reflectance data in this paper. The 3x3 pixels were 

used for calculation of reflectance covering the EC sites. In the maize site, the fraction of maize within t

he 1.5 km radius would be higher than 95% because only small ridges were inside. For forest and 

grassland sites, the fraction could be almost 100%. 

9. Section 2.5, page 7 - Page 8, it needs to expand and provide detailed description on how LUE was 

calculated. Maize is a summer crop, why authors used data from 11/1/2007 to7/12/2008 to derive a 

relationship between LAI and fapar? 

Response: in section 2.5, we provided a more detail information of LUE and fAPAR determination. 

Furthermore, we also consider it was not appropriate to use MODIS LAI/fAPAR product to derive 

the relationship. Instead, we used a method suggested by reviewer1 to use the in situ measured LAI 

for the calculation of fAPAR with equation of fAPAR=0.95(1-Exp (-0.5LAI)). This method demonstrated 

to be workable with other publications (Ruimy et al., 1999; Xiao et al., 2004). The LAI used was data 

in the same time of other observations.

10. Page 8, Section 2.6, authors need to specify what indices were used the work of Inoue et al. (2008). 

Response: some VIs were added in the part to avoid confusion.

11. Page 10, Section 3.1, why only data (NEE and T) from 22:00pm to 3:00am next day are used in 

calculated? The night time definition needs to revisit and authors should use appropriate definition of 

night time? In short, authors need to provide much better and consistent way to describe how to 

partition NEE data into GPP and ecosystem respiration. 

Response: we added an explanation of nighttime selection. Because high altitude in western China, 

sunlight time may from 05:00 to 21:00 in summer, therefore, we selected the time from 22:00 to 

03:00 in the night to acquire the relationship of NEE/T. 

12. Page 11, Section 3.3., Authors need to provide much more detailed information on how they 

calculate LUE. 

Response: information was added in section 2.5.

13. Page 12, section 3.4.1, while vegetation indices are correlated with LUE, it does not mean that one 

can use vegetation indices to replace LUE. One must realize that vegetation indices and LUE are two

 different terms and biological processes. In that same paragraph, authors also found good correlation

 between vegetation indices and fapar. 

Response: we agree with the suggestions by the reviewer, perhaps because we authors are not native 

speakers, and we made mistakes in the use of “replace”. These VIs just could be indicators or proxies

 of the variables. The revision will be proofread by a native speaker and we are trying to make it clear

 and consistent.

14. Page 15, it stats "", if that is the case, what is the value of this paper? Does that relationship change 

by biome? Or does that relationship change by pixel?