National Scale Maize Yield Estimation by Integrating Multiple Spectral Indexes and Temporal Aggregation

Round 1

Reviewer 1 Report (Previous Reviewer 2)

Thank you for the opportunity to review the paper entitled "National scale maize yield estimation by integrating feature knowledge of biophysical variables and temporal aggregation".

I strongly recommend authors to have a professional English edit of the paper.

The paper is marred by inconsistencies and incomprehensible English.

Author Response

Response to Reviewer 1 Comments

Thank you for the opportunity to review the paper entitled "National scale maize yield estimation by integrating feature knowledge of biophysical variables and temporal aggregation".

I strongly recommend authors to have a professional English edit of the paper.

The paper is marred by inconsistencies and incomprehensible English.

Response: Thank you for your valuable suggestions. Thank you very much for spending your precious time in reviewing our manuscript.

We revised the English spelling, terminology, tense, grammar, sentence structure, and the back-and-forth logic between sentences in the manuscript. And the research ideas and logical structure of the paper were sorted out. After we revised the content of the entire manuscript according to the review comments, we invited two editors to revise the English. The English review was conducted using a two-stage process in which two editors reviewed the file. Both editors are native English speakers.

Inconsistencies：

(1) In the original manuscript, we wrote that the vegetation index was deficient and we used indicators reflecting water content, pigmentation, nitrogen content and biomass. However, such expression may cause misunderstanding to the readers, and we seem to deny the vegetation index, and it was not used in this study. We may not have expressed our research strategy clearly. So, we revised the relevant expressions in the Abstract, 2.3 Method, and other content.

[On line 14-19] “Commonly applied vegetation indexes (VIs) are mainly used in crop yield estimation, as they can reflect the greenness of vegetation. However, the environmental pressures of crop growth and development is difficult to monitor and evaluate. Indexes for water content, pigment content, nutrient elements, and biomass have been developed to indirectly explain the influencing factors of yield, with extant studies mainly assessing VIs, climate, and water content factors.”

[On page 6, line 219-225] “At present, the commonly applied VIs is being widely used in yield estimation. The main influencing factors of yield are climate, soil, genotype, and management. Clearly, commonly applied VIs is insufficient for yield estimation. In this study, the indexes closely related to yield, such as moisture content, pigment content, biomass, nitrogen content, and climate factors, were calculated. These indexes could also either directly or indirectly reflect the climate, soil, genotype, and management factors.”

 

（2）There are inconsistencies in the use of vegetation indices and biophysical variables, probably due to a lack of clarity about what they mean.

[On page 2, line 67-70] “Biophysical variables include leaf area index (LAI), vegetation cover, fraction of absorbed photo synthetically active radiation (fAPAR), pigment content(chlorophyll, carotenoid, anthocyanin), nitrogen content, canopy water content (CWC) and biomass [1-4]. ”

[On page 6 line 221-231] The commonly applied VIs are widely used to monitor crop cover, growth status and yield. However, the commonly applied VIs mainly reflects the greenness of vegetation, but it is difficult to fully capture the environmental pressure of crop growth and development. The factors influencing yield include climate, soil, genetics and management. Indexes reflecting biophysical variables (water content, pigments, nutrient elements and biomass) could be monitored using remote sensing technique. And these indexes could reflect to climate, soil, genetics and management. Therefore, we could monitor indirectly climate, soil, genetics and management by monitoring biophysical variables.

In addition, indexes such as those reflecting water content and pigmentation are VIs. Most VIs are belong to biophysical yields. We used not only the commonly applied VIs, but also some indexes reflecting water content, pigment content, nutrient elements and biomass, which could reflect the influencing factors of yield from the side.

Since biophysical variables are not clearly defined. We revised the title of this manuscript, we revised “National scale maize yield estimation by integrating biophysical variables and temporal aggregation” to “National scale maize yield estimation by integrating multiple spectral indexes and temporal aggregation”. And the expression of the relevant content was revised.

 

(3) There are some inconsistencies in some English terms and sentence expressions. We revised the entire manuscript. This includes revising grammar, spelling and sentence expressions. Such as, Full terms with acronyms/abbreviations need not be set to the uppercase. In addition, for consistency in terminology, we used the phrasings “whole-growth,” “vegetative growth,” and “reproductive growth” in all instances.[On page 7-8, line 288-294] The formulas and the corresponding textual descriptions are expressed inconsistently, and we have modified them.

 (5) There were inconsistencies between the content on some figures and the textual descriptions. We meticulously revised Figure 2 to clarify the relationship between biophysical variables and vegetation indices, the relationship between biophysical variables and yield influencing factors, etc. And we revised Figure 4, in which there were indicators that were mislabeled, and after detailed checking, we corrected all the labels. In addition, we also corrected other figures.

So far, we found the above points. We will continue to look for inconsistencies, or kindly please the reviewer point them out.

 

Author Response File: Author Response.pdf

Reviewer 2 Report (Previous Reviewer 3)

Line 276, The fomat of the variables in the formulas must be same as in the following introduction text. For example X would be italic. 

Line 274 and 275, what is the difference between quation (1) and quation (2)?

Figure7 (c), (e), 1:1 line need to be given in this figure.

 

Author Response

Response to Reviewer 2 Comments

 

Comments and Suggestions for Authors

Line 276, The fomat of the variables in the formulas must be same as in the following introduction text. For example X would be italic. 

Response: Thank you for your valuable suggestions. Thank you very much for spending your precious time in reviewing our manuscript.

 [On page7, line 288-293] We revised the fomat of the variables both in the formulas and in the following introduction text. Variables (X) are italic.

Line 274 and 275, what is the difference between equation (1) and equation (2)?

Response: Thank you for your valuable suggestions. Thank you very much for spending your precious time in reviewing our manuscript. [On page7, line 288-295]

                                       (1)

                                     (2)                            

In the equation, A, B represents different indexes. X represents the combined indexes.

The numerators in equation (1) and equation (2) are the same, and the denominators are different. The denominator of equation (1) is the sum between indexes A and indexes B, which is the sum of the two indexes. Equation (1) is similar to the normalization approach. The denominator of equation (2) is a single index.

Special experiments are needed to give explanations about the meaning and advantages of the combined indexes. We could explain the possible covariance between the two indexes through remote sensing techniques. The estimated yield potential of combined indexes is stronger than the individual index. For example, the importance of NMDI_NDNI is ranked 5^th ((Figure 4 (b) and (c)), after combining the NMDI with NDNI using equation (1). NMDI_NDNI has a stronger ability to estimate yield than NDNI, and NMDI_NDNI may enhance the sensitivity nitrogen content.

Figure7 (c), (e), 1:1 line need to be given in this figure.

Response: Thank you for your valuable suggestions. Figure 7 is complex. We divide Figure 8 into three figures, which are Figure 7, Figure 8 and Figure 9.

[On Page 12, line 470] The study found that with the number of indexes increased to 20 (the top 20 indexes of importance), the estimated yield accuracy R² reached 0.78 (Figure 7(b)). Figure 7(b) is the scatter plot made using the top 20 indexes in importance ranking for estimating maize yield.

[On Page 10, line 388-389] In section 3.1.3, it is concluded that NMDI₁ (NMDI of vegetative growth period) ranks first in importance Figure 4(c). [On Page 13, line 488]  Figure 8(a) is the scatter plot made using the index ranked first in importance (NMDI₁) for estimating maize yield.

Figure 8(b) is the scatter plot made using the top four indexes in importance ranking (NMDI₁, Evapotranspiration₂, NMDI₂, GPP₁) for estimating maize yield.

We give 1:1 line for Figure 7(b), Figure 8(a) and Figure 8(b).

 

Thank you for your insightful comments and suggestions that have enhanced our manuscript, and even helped us in future research.

 

Author Response File: Author Response.pdf

Reviewer 3 Report (Previous Reviewer 4)

I was reviewer two in the previous submission of this manuscript. It has been improved greatly, but there are still several issues to be fixed, specially concerning English quality. Further details can be found in the next.

 

ABSTRACT

 

- L15 and 25. English.

 

- L25. You talk in here about biophysical variables, what do you mean by those?. Before you were talking about VIs and climate variables. If you do not specify what do you mean by this term, then it is all confusing and looks like you do not actually understand that VIs are biphysical variables. As far as I can see, then the only difference w.r.t. some literature papers is the use of temporal information, is it right?.

 

- While the abstract has improved w.r.t. to the previous submission, the English is still to be fixed. 

 

INTRODUCTION

 

- L47. Unreadable. English problems.

 

- L143-145. Just some lines before you talk in a generic way about biophysical variables, but in here you specify three methods. Please make sure you connect the terms you are using. Otherwise they seem to be different things.

 

- Introduction has been improved but English problems still remain around the whole text. Please revise.

 

MATERIAL AND METHODS

 

- L150. What do you mean?. It is not readable.

 

- L155. What is "t" in here?

 

- Figure 1. As already said before, this figure is way too complex, please split in four different figures. This will also help for readability. Not so sure that you actually require Figure 1 (a).

 

- Table 1 is presenting general spectral/radiometric indices. All the document, so far, you have talked only about VIs. Why to talk now about others?. This is out of context.

 

- L209. Now you talk again only about VIs. Do you see how do you change from one topic to another?

 

- Description of Figure 2 should be the proposed methodology and not about evaluation. Or it can be "proposed methodology for evaluating..."

 

- 2.3.1 and 2.3.2 are not described as part of the method presented in figure 2. Please keep coherence of information.

 

- Figure 3 is showing several spectral/radiometric indices and not only VIs. Once again, you are changing from one topic to another.

 

- English is generally better in this section, but it still requires corrections.

 

RESULTS

 

- L335. NDMI thought about water content and not just vegtation, isn't it?

 

- Figure 7. Please separate as much as possible. It is not good to have such heavy figures.

 

- 4.5. You had talked about MODIS only in materials description and not further or clear connection was made, of course indices are extracted from such sort of data, but it does not remain clear. You need to add some sort of table with bands and connections to extracted indices.

 

CONCLUSIONS

 

- Now conclusions are supported by the work presented. You still need to improve English.

 

REFERENECES

 

- Please pay attention to using more recent publication, barely 13 out of 78 references correspond to the 2021 and 2022, with rather less in the last year. 

 

Author Response

Response to Reviewer 3 Comments

 

Comments and Suggestions for Authors

I was reviewer two in the previous submission of this manuscript. It has been improved greatly, but there are still several issues to be fixed, specially concerning English quality. Further details can be found in the next.

Response: Thank you very much for the positive comments. Thank you very much for spending your precious time in reviewing our manuscript again. We carefully revised the manuscript. Below is an overview of the major revisions.

(1) We revised Figure 2, Figure 3, Figure 4, Figure 7, Figure 8, and Figure 9. This Figure 1 is way too complex. We split them into three figures. Considering the number of figures in the manuscript, we put Figure 1 (a) and (b) in the Appendix A.  [On page 4, line 176] Figure 1 Map overview of the study area: (a) distribution of maize in 2018 and agricultural areas; (b) distribution of maize yield per unit area in 2018.We divide Figure 8 into three figures, which are Figure 7, Figure 8 and Figure 9. And modified the text description corresponding to the revised figure.

(2) We give the connection and difference between biophysical variables and vegetation indices. We did not reject the vegetation index, we corrected the expression. At present, commonly applied VIs is being widely used in yield estimation. The main influencing factors of yield are climate, soil, genotype, and management. Clearly, commonly applied VIs is insufficient for yield estimation. In this study, the indexes closely related to yield, such as moisture content, pigment content, biomass, nitrogen content, and climate factors, were calculated. These indexes could also either directly or indirectly reflect the climate, soil, genotype, and management factors.

(3) We revised the English spelling, terminology, tense, grammar, sentence structure, and the back-and-forth logic between sentences in the manuscript. And the research ideas and logical structure of the paper were sorted out.

(4) After we revised the content of the entire manuscript according to the review comments, we invited two editors to revise the English. The English review was conducted using a two-stage process in which two editors reviewed the file. Both editors are native English speakers.

 

ABSTRACT

- L15 and 25. English.

Response: Thank you for your valuable suggestions.

[On line 15] We revised “Vegetation Indexes (VIs) are mainly used in crop yield estimation studies.” to “Commonly applied Vegetation Indexes (VIs) are mainly used in crop yield estimation, as they can reflect the greenness of vegetation.”

[On line 25] We revised “We propose a novel yield evaluation method that integrate biophysical variables and temporal aggregation. By using Google Earth Engine (GEE), the spectral indexes are calculated, and the maize yield in China at county level from 2015 to 2019 is estimated by using random forest.”

The sentence should be expressed as follows:

[On page 1, line 22-25] “Thus, this study proposes a novel yield evaluation method for integrating multiple spectral indexes and temporal aggregation data. In particular, spectral indexes were calculated by integrating publicly available data (remote sensing images and climate data) from the Google Earth Engine platform, and county-level maize yields in China from 2015 to 2019 were estimated using a random forest model.”

- L25. You talk in here about biophysical variables, what do you mean by those?. Before you were talking about VIs and climate variables. If you do not specify what do you mean by this term, then it is all confusing and looks like you do not actually understand that VIs are biophysical variables. As far as I can see, then the only difference w.r.t. some literature papers is the use of temporal information, is it right?.

Response: Thank you for your valuable and specific suggestions.

[On page 2, line 67-70] “Biophysical variables include leaf area index (LAI), vegetation cover, fraction of absorbed photo synthetically active radiation (fAPAR), pigment content(chlorophyll, carotenoid, anthocyanin), nitrogen content, canopy water content (CWC) and biomass [1-4]. ”

[On page 6 line 221-231] The commonly applied VIs are widely used to monitor crop cover, growth status and yield. However, the commonly applied VIs mainly reflects the greenness of vegetation, but it is difficult to fully capture the environmental pressure of crop growth and development. The factors influencing yield include climate, soil, genetics and management. Indexes reflecting biophysical variables (water content, pigments, nutrient elements and biomass) could be monitored using remote sensing technique. And these indexes could reflect to climate, soil, genetics and management. Therefore, we could monitor indirectly climate, soil, genetics and management by monitoring biophysical variables.

In addition, indexes such as those reflecting water content and pigmentation are VIs. Most VIs are belong to biophysical yields. We used not only the commonly applied VIs, but also some indexes reflecting water content, pigment content, nutrient elements and biomass, which could reflect the influencing factors of yield from the side.

With regard to time information. In this study, we evaluated biophysical variables and climatic factors, and selected the most sensitive indicators for yield. Through the entire growing period, two growing periods (nutritional and reproductive), and 8-day time-series data, we evaluate the effect of temporal aggregation on yield.

- While the abstract has improved w.r.t. to the previous submission, the English is still to be fixed. 

Response: Thank you for your valuable suggestions. We carefully checked and revised the English expressions, grammar, etc. in the abstract. In addition, we improved the entire abstract. And rewrote some sentences, For example, [on line 14-19] “Commonly applied Vegetation Indexes (VIs) are mainly used in crop yield estimation, as they can reflect the greenness of vegetation. However, the environmental pressures of crop growth and development are difficult to monitor and evaluate. Indexes for water content, pigment content, nutrient elements, and biomass have been developed to indirectly explain the influencing factors of yield, with extant studies mainly assessing VIs, climate, and water content factors”

INTRODUCTION

- L47. Unreadable. English problems.

Response: Thank you for your valuable suggestions.

-47 “Therefore, timely and accurate obtain the information on the spatial and temporal distribution of maize yield, which is important to ensure national food security and rational agricultural structure.”

The modified expression is as follows: [On page 1-2, line 46-48] “Obtaining information on the spatial and temporal distribution of maize yield in a timely and accurate manner is essential in ensuring national food security and rationalizing the country’s agricultural structure.”

- L143-145. Just some lines before you talk in a generic way about biophysical variables, but in here you specify three methods. Please make sure you connect the terms you are using. Otherwise they seem to be different things.

Response: Thank you for your valuable suggestions. We calculated spectral indexes, which could reflect biophysical variables of maize. By assessing the sensitivity of spectral indexes to yield, we selected the most yield-sensitive indexes, and some combined indexes are designed using these indices. In addition, climatic data were used. Thus, we use three types of data, including spectral indexes, climate factors, and combined indexes. Biophysical variables showed great potential for yield estimation.

[On line page3, line 148-152] “The main objectives of this research were to assess the sensitivity of biophysical variables (i.e., pigment content, nitrogen content, and canopy water content), assess the effect of temporal aggregation data on yield estimation, and explore how to realize national maize yield estimation by integrating feature knowledge related to multiple spectral indexes and temporal aggregation data. ”

- Introduction has been improved but English problems still remain around the whole text. Please revise.

Response: Thank you for your valuable suggestions. We carefully checked and revised the English， including the presentation, grammar, and spelling of the introduction.

MATERIAL AND METHODS

- L150. What do you mean?. It is not readable.

Response: Thank you for your valuable suggestions. [On page 4, line 155-159] “The global total maize yield data were obtained from the database of theFood and Agriculture Organization of the United Nations (FAOSTAT) (https://www.fao.org/faostat/zh/#data/QCL), from which the proportions of total maize yields of different countries were calculated. According to data, maize yield in China accounts for more than one-fourth (28%) of the global maize yield (Figure 1 of Appendix A).”

- L155. What is "t" in here?

Response: Thank you for your valuable suggestions. [On page 4, line 160] Here, “t” refers to the unit of weight. We revised "t" to "ton".

- Figure 1. As already said before, this figure is way too complex, please split in four different figures. This will also help for readability. Not so sure that you actually require Figure 1 (a).

Response: Thank you for your valuable suggestions. This Figure 1 is way too complex. We split them into thre figures. Considering the number of figures in the manuscript, we put Figure 1 (a) and (b) in the Appendix A.  [On page 4, line 176] Figure 1 Map overview of the study area: (a) distribution of maize in 2018 and agricultural areas; (b) distribution of maize yield per unit area in 2018.

- Table 1 is presenting general spectral/radiometric indices. All the document, so far, you have talked only about VIs. Why to talk now about others?. This is out of context.

Response: Thank you for your insightful suggestions. We didn't consider it carefully enough.

The commonly used indexes for yield estimation are mainly NDVI and LAI. In the introduction, the indexes reflecting chlorophyll and nitrogen content are gradually being used in current yield estimation studies, however, they are used less frequently. Some researchers assessed the potential of these indexes for estimating yield.

[On page 3, line 101-108] “Pigment, nutrient element, and biomass indexes are rarely used in crop yield estimation [5, 6] despite their close relation to yield estimation. The N partitioning index at anthesis, which can describe the proportion of aboveground N of crop composition at flowering, is an example of a good yield index [7]. Another important indicator is the plant nitrogen spectral index, which is closely related to crop nitrogen uptake [8]. Incidentally, only a few studies have systematically evaluated the sensitivity of spectral indexes and climate factors [9, 10].”

Commonly applied VIs is not sufficient to estimate yield. Factors influencing yield include climate, soil, genetics, and management. In this study, we calculated indexes from other studies that have potential for yield estimation, as well as indexes that better reflect crop growth and yield. We systematically assesse the sensitivity of indexes reflecting water content, pigmentation, nitrogen content, etc. to yield. In this study, we will explore whether yield estimation accuracy could be further improved by integrating biophysical variables (water content, pigmentation, nitrogen content, etc.).

[On page 4, line 187-194] “VIs (i.e., NDVI) are often used to estimate the yield, but this approach is insufficient. The influencing factors of yield include climate, soil, genotype, and management. In this study, the indexes for the aforementioned factors were calculated using MODIS data, which were obtained from the Google Earth Engine (GEE) platform. The spectral index used in the study is presented in Table 1, while its detailed description is given in Appendix A. The relationships of the indexes and the influencing factors from the perspective of research methodology are presented in Section 2.3 and Figure 2.”

Previously, we mainly described the VIs, and rarely talked about other indexes, we add some content in the introduction and data presentation, thus to make the logic more reasonable.

- L209. Now you talk again only about VIs. Do you see how do you change from one topic to another?

Response: Thank you for your valuable suggestions. We deleted this sentence＂At present, vegetation indexes (VIs) is the most widely used in yield estimation research, but there are some limitations: VIs mainly reflect the green degree of vegetation, but cannot fully reflect the pressure of environment on crop development. ”

[On line 206] The appropriate expression should be: “At present, the commonly applied VIs is being widely used in yield estimation. The main influencing factors of yield are climate, soil, genotype, and management. Clearly, commonly applied VIs is insufficient for yield estimation. In this study, the indexes closely related to yield, such as moisture content, pigment content, biomass, nitrogen content, and climate factors, were calculated. These indexes could also either directly or indirectly reflect the climate, soil, genotype, and management factors.”

- Description of Figure 2 should be the proposed methodology and not about evaluation. Or it can be "proposed methodology for evaluating..."

Response: Thank you for your valuable suggestions. [On page 6, line 242] The description of Figure 2 are revised, "Proposed methodology of parameter (index) development for yield estimation"

- 2.3.1 and 2.3.2 are not described as part of the method presented in figure 2. Please keep coherence of information.

Response: Thank you for your valuable suggestions. We carefully revised figure 2. We have added the content corresponding to the method of Section 2.3.1 and 2.3.2. “2.3.1 Phenological Period Calculation” and “2.3.2 RF and Importance Assessment”. 2.3.1 and 2.3.2 are described as part of the method presented in figure 2.

- Figure 3 is showing several spectral/radiometric indices and not only VIs. Once again, you are changing from one topic to another.

Response: Thank you for your valuable suggestions.

Researchers use VIs to monitor the growth status and yield of vegetation. Factors influencing yield include climate, soil, genetics and management. The indexes calculated by remote sensing technology could reflect these factors from the side. These indexes can appear in the form of maximum value, minimum value, mean, and standard deviation. We systematically evaluated the indexes reflecting climate, soil, genetics and management. The most sensitive indexes for yield were selected. And then we combined the indexes. Further assess which combined indexes is most sensitive to yield.

[On page 7 line 284-289] “We designed the combined indexes is aimed at: first, the original indexes could reflect some information. The combined indexes may reflect the information from different indexes, thus avoiding the use of more complex indexes and eliminating data redundancy. Second, compared to single index, the combined indexes may enhance the sensitivity to yield.” For example, by combined indexes reflecting both moisture content and nitrogen content, and even more sensitivity to yield than single index.”

- English is generally better in this section, but it still requires corrections.

Response: Thank you for your recognition and suggestions. For this part of the English problem, we carefully rechecked and corrected the deficiencies and errors.

RESULTS

- L335. NMDI thought about water content and not just vegetation, isn't it?

Response: Thank you for your valuable suggestions. You review our manuscripts very carefully.

NMDI is extremely sensitive to soil and vegetation water content. For bare land or weak vegetation regions, NMDI is a function of soil water content, and the increase of land surface moisture was related to the decrease of NMDI. For densely vegetation regions, NMDI represent the index for estimating vegetation water content, NMDI increased almost linearly with leaf water content. For regions with medium vegetation coverage, NMDI is still sensitive to surface moisture and leaf water content, which decreases with the decrease of land surface moisture and increased with the increase leaf water content [11].

In the early stages of maize growth, when the stems and leaves are small and do not completely cover the bare soil, NMDI mainly reflects the soil water content. As maize grows, NMDI gradually reflects leaf water content along with soil water content. When maize stems and leaves were growing luxuriantly, NMDI reflects vegetation water content, NMDI increases almost linearly with leaf water content.

In Discussion 4.1 [On page 15, line 553-557] “Evaluations of the indexes for the whole-growth period (Figure 4 (b)) indicate that NMDI is the most important index for yield estimation, further suggesting precise data from using this index in maize mapping [12]. Previous studies have shown that water-related indexes are sensitive to maize yield estimation [13], which may be explained by maize being a crop with a high water demand [14].”

- Figure 7. Please separate as much as possible. It is not good to have such heavy figures.

Response: Thank you for your valuable suggestions. Your suggestion is a great help to our manuscript. We changed previous complex style of figures. We divided Figure 7 into three figures. They are Figure 7, Figure 8 and Figure 9. And we lightened the colors in the figure and brightened the fill color a bit. The labeled provinces and prefecture-level cities have many names and are rather crowded, so we removed the labeled names of several provinces (Hebei, Chongqing, Guizhou, Shanghai, Taiwan and Hubei).

- 4.5. You had talked about MODIS only in materials description and not further or clear connection was made, of course indices are extracted from such sort of data, but it does not remain clear. You need to add some sort of table with bands and connections to extracted indices.

Response: Thank you for your valuable suggestions.

[On line 185-190] “The spectral indexes were extracted from MOD09A1, and the climate data were extracted from MYD11A2, EAR5, and TerraClimate. VIs (i.e., NDVI) are often used to estimate the yield, but this approach is insufficient. The influencing factors of yield include climate, soil, genotype, and management. In this study, the indexes for the aforementioned factors were calculated using MODIS data, which were obtained from the Google Earth Engine (GEE) platform.”

The MODIS data contain 36 bands. We have calculated these spectral indices using bands 1-7 with spatial resolution of 500 m. It should be noted that a few indices are calculated for bands close to the band of MODIS, since there are no exact correspondence bands.

CONCLUSIONS

- Now conclusions are supported by the work presented. You still need to improve English.

Response: Thank you for your valuable suggestions and recognition. We improved our English expressions and revised some sentences. Your comments and suggestions have helped us a lot, and helped further improve the quality of our manuscripts.

REFERENECES

- Please pay attention to using more recent publicat on, barely 13 out of 78 references correspond to the 2021 and 2022, with rather less in the last year. 

Response: Thank you for your valuable suggestions. In the introduction and section 4.5 Uncertainties and Future Work. We have reduced several previous references and added references for 2021 and 2022. In addition, we replaced several previous references with references from 2021 and 2022.

In the introduction, we have added references on yield estimation using time series data. In addition, references on yield estimation using indexes reflecting nitrogen content and chlorophyll has been added, but there is a few this type of references. Therefore, this study further extended the application of these indicators in yield estimation by integrating water content, pigment content, nitrogen content and biomass, as well as temporal aggregation, and achieved relatively considerable results.

[On page 3, line 102-106] “The N partitioning index at anthesis, which can describe the proportion of aboveground N of crop composition at flowering, and is closely related to crop nitrogen uptake，they are used in a few yield estimation studies[7, 15]. Cotton yield prediction using chlorophyll content[16].”

In section 4.5 Uncertainties and Future Work. We have added references on assessing the optimal period for estimating births, and on early estimation of births.

[On page 17, line 657-660] “In the future, we plan to subdivide the growth period as follows: turning-green period, jointing period, heading period, and maturity period. In this manner, the optimal phenological phase of maize yield prediction could be obtained[17], and early crop yield estimation can be realized[18].”

 

Thank you for your insightful comments and suggestions that have enhanced our manuscript, and even helped us in future research.

 

References

1. Clevers J: Beyond NDVI: extraction of biophysical variables from remote sensing imagery. In Land use and land cover mapping in Europe. Springer; 2014: 363-381
2. Upreti D, Huang W, Kong W, Pascucci S, Pignatti S, Zhou X, Ye H, Casa R: A comparison of hybrid machine learning algorithms for the retrieval of wheat biophysical variables from sentinel-2. Remote Sensing 2019, 11:481.
3. De Grave C, Verrelst J, Morcillo-Pallarés P, Pipia L, Rivera-Caicedo JP, Amin E, Belda S, Moreno J: Quantifying vegetation biophysical variables from the Sentinel-3/FLEX tandem mission: Evaluation of the synergy of OLCI and FLORIS data sources. Remote Sensing of Environment 2020, 251:112101.
4. Thenkabail PS, Mariotto I, Gumma MK, Middleton EM, Landis DR, Huemmrich KF: Selection of hyperspectral narrowbands (HNBs) and composition of hyperspectral twoband vegetation indices (HVIs) for biophysical characterization and discrimination of crop types using field reflectance and Hyperion/EO-1 data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2013, 6:427-439.
5. Tian H, Wang P, Tansey K, Zhang J, Zhang S, Li H: An LSTM neural network for improving wheat yield estimates by integrating remote sensing data and meteorological data in the Guanzhong Plain, PR China. Agricultural and Forest Meteorology 2021, 310:108629.
6. Vergara-Díaz O, Zaman-Allah MA, Masuka B, Hornero A, Zarco-Tejada P, Prasanna BM, Cairns JE, Araus JL: A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization. Frontiers in Plant Science 2016, 7.
7. Gaju O, Allard V, Martre P, Le Gouis J, Moreau D, Bogard M, Hubbart S, Foulkes MJ: Nitrogen partitioning and remobilization in relation to leaf senescence, grain yield and grain nitrogen concentration in wheat cultivars. Field Crop Res 2014, 155:213-223.
8. Blackmer TM, Schepers JS: Use of a Chlorophyll Meter to Monitor Nitrogen Status and Schedule Fertigation for Corn. J Prod Agric 1995, 8:56-60.
9. Ma Y, Zhang Z, Kang Y, Özdoğan M: Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach. Remote Sensing of Environment 2021, 259:112408.
10. Li Z, Ding L, Xu D: Exploring the potential role of environmental and multi-source satellite data in crop yield prediction across Northeast China. Science of The Total Environment 2022, 815:152880.
11. Wang L, Qu JJ: NMDI: A normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing. Geophysical Research Letters 2007, 34:117-131.
12. Qiu B, Huang Y, Chen C, Tang Z, Zou F: Mapping spatiotemporal dynamics of maize in China from 2005 to 2017 through designing leaf moisture based indicator from Normalized Multi-band Drought Index. Comput Electron Agr 2018, 153:82-93.
13. Zhang L, Zhang Z, Luo Y, Cao J, Tao F: Combining Optical, Fluorescence, Thermal Satellite, and Environmental Data to Predict County-Level Maize Yield in China Using Machine Learning Approaches. Remote Sensing 2020, 12:21.
14. Shuangjie J, Hongwei L, JIANG Y, Guoqiang Z, Hezhou W, Shenjiao Y, Qinghua Y, Jiameng G, Ruixin S: Effects of drought on photosynthesis and ear development characteristics of maize. Acta Ecologica Sinica 2020, 40:854-863.
15. Chen B, Lu X, Yu S, Gu S, Huang G, Guo X, Zhao C: The Application of Machine Learning Models Based on Leaf Spectral Reflectance for Estimating the Nitrogen Nutrient Index in Maize. Agriculture 2022, 12:1839.
16. P S, K. R L, S P, R K, G K, N. S S: Cotton yield prediction using drone derived LAI and chlorophyll content. Journal of Agrometeorology 2022, 24:348-352.
17. Yang B, Zhu W, Rezaei EE, Li J, Sun Z, Zhang J: The Optimal Phenological Phase of Maize for Yield Prediction with High-Frequency UAV Remote Sensing. Remote Sensing 2022, 14:1559.
18. Cheng M, Penuelas J, McCabe MF, Atzberger C, Jiao X, Wu W, Jin X: Combining multi-indicators with machine-learning algorithms for maize yield early prediction at the county-level in China. Agricultural and Forest Meteorology 2022, 323:109057.

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report (Previous Reviewer 2)

I appreciate efforts made by the authors to revise the manuscript. However, I still suggest authors to re-review the paper to avoid inconsistencies and minor error. For instance, authors need to be careful in the use of terms such as yield vs production, maize vs corn etc. The very first sentence of the paper under the abstract "Maize yield in China accounts more than one-fourth of the global maize yield...", if I am not wrong authors want to imply 'production' not a yield. In the successive sentence authors say '... corn yield...', which reflects inconsistencies in use of the language.

Happy new year 2023.

Best wishes.

Author Response

Response to Reviewer 1 Comments

Comments and Suggestions for Authors

I appreciate efforts made by the authors to revise the manuscript. However, I still suggest authors to re-review the paper to avoid inconsistencies and minor error. For instance, authors need to be careful in the use of terms such as yield vs production, maize vs corn etc. The very first sentence of the paper under the abstract "Maize yield in China accounts more than one-fourth of the global maize yield...", if I am not wrong authors want to imply 'production' not a yield. In the successive sentence authors say '... corn yield...', which reflects inconsistencies in use of the language.

Response: We are very grateful to the reviewers for their time and effort in reviewing our manuscript very carefully and giving us many insightful comments and suggestions. We carefully revised the entire manuscript for each suggestion. The reviewers' comments and suggestions helped our manuscript a lot.

[On page 1 line 14 ] We will re-review the paper to avoid inconsistencies and minor error. We will be careful in the use of terms such as yield vs production, maize vs corn etc. The very first sentence of the paper under the abstract "Maize yield in China accounts more than one-fourth of the global maize yield...", we want to imply ' yield'.

[On page 1 line 14-16 ] “Accurate and timely estimation of corn maize yield is of great significance to crop management and food security.” We have changed “corn yield” to “maize yield”.

We read the entire manuscript carefully, and found the following inconsistencies and minor error:

1. [On page 3 line 108-109] We have changed “Cotton yield prediction using chlorophyll content” to “Indexes reflecting chlorophyll content was used in cotton yield prediction”
2. [On page 3 line 115,121 ] We have changed “stage” to “period”, in order to ensure consistency of terminology.

 

3. Full terms with acronyms/abbreviations need not be set to the uppercase.

[On page 3 line 128-130] We have changed "the International Maize and Wheat Improvement Center (CIMMYT)" to "the International Maize and Wheat Improvement Center (CIMMYT)"

[On page 10 line 359 ]We have changed “Chlorophyll vegetation Index (CVI)” to “chlorophyll vegetation index (CVI)”

 

4. [On page 7 line 244-245] For Figure 2, The initial letter of the word is changed to capitalized form. In addition, we have changed " Temporal Aggregation assess" to "Temporal Aggregation Assess"; Besides, we have changed "Maize yield estimation" to " Maize Yield Estimation "

 

5. [On page 9, line 327-330] In this study, we calculated multiple indexes for three periods(whole-growth period, the two growth periods (vegetative and reproductive), and the eight-day time series). We want to imply 'phenological period' not 'phenological period index'.

“ Here, the phenological period index, which is the index most sensitive to yield estimation, was used to estimate maize yield.” This sentence should be expressed as follows:

“Here, the phenological period, which is the growth period most sensitive to yield estimation, was used to estimate maize yield.”

 

6. “the change… combined indexes was explored.” Here, The indexes we use are not just combined indexes, spectral indexes and climate factors are also included.

 [On page 10 387-391] We obtained the following conclusions: “The coefficient of determination (R²) values were 0.58 for the whole-growth period, 0.7175 for the vegetative and reproductive growth periods, and 0.6901 for the eight-day time series. A comparison of the results indicates that the indexes for the vegetative and re-productive growth periods have great potential for yield estimation.”

 

7. [On page 13 line 471-474] This sentence should be expressed as “the change rule of overestimation/underestimation (i.e., from dispersion to aggregation) with an increasing number of combined indexes multiple spectral indexes of the vegetative and reproductive growth periods was explored.”

 

8. [On page 9 line 342] We found a minor error. R² cannot be expressed as " accuracy", which is generally expressed as a percentage. Here，R² is the coefficient of determination. We checked the entire manuscript, and have changed "accuracy" to "the coefficient of determination" or deleted "accuracy" in places where R² was written. In addition, we have supplemented the RMSE in the corresponding position.

We use "accuracy" when expressing R² and RMSE, to indicate the reliability of maize yield estimation results. and "R²" when expressing only the value of R².

For instance, [on page 13 line 481-482] “The accuracy R² was 0.2837, and the overestimation/ underestimation values were relatively dispersed”.

The revised expression is “The coefficient of determination R² was 0.2837,RMSE = 2583.5kg/ha, and the overestimation/underestimation values were relatively dispersed”

9. On page 18 line 691-692] “The research results are expected to enhance the feature knowledge and provide references for index assessments for large-scale crop yield estimation research.” This sentence should be expressed as:“The research results are expected to provide the feature knowledge and references for index assessments for large-scale crop yield estimation research.”
10. [On page 18 line 694-697] Acknowledgements: We added the National Key Research and Development Program of China (No. 2022YFD2001101, 2021YFB3901303), and deleted “the Ministry of Natural Resources of China (KY-010000-04-2000-002) and the Fujian provincial department of ecology and environment (2022R023).”
11. We have removed some of the references that are less relevant to the manuscript, as well as several references that already have that type of reference. Including the following references:

Gao B: NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment 1996, 58(3):257-266.

Leroux L, Falconnier GN, Diouf AA, Ndao B, Gbodjo JE, Tall L, Balde AA, Clermont-Dauphin C, Bégué A, Affholder F et al: Using remote sensing to assess the effect of trees on millet yield in complex parklands of Central Senegal. Agricultural Systems 2020, 184:102918.

Kaiyu G, Zhan L, Nagraj RL, Feng G, Donghui X, The HN, Zhenzhong Z: Mapping Paddy Rice Area and Yields Over Thai Binh Province in Viet Nam From MODIS, Landsat, and ALOS-2/PALSAR-2. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2018, 11(7):1-15.

Johnson DM: An assessment of pre- and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sensing of Environment 2014, 141:116-128.

Schwalbert RA, Amado T, Corassa G, Pott LP, Prasad PVV, Ciampitti IA: Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil. Agricultural and Forest Meteorology 2020, 284:107886.

Liu J, Liu M, Tian H, Zhuang D, Zhang Z, Zhang W, Tang X, Deng X: Spatial and temporal patterns of China's cropland during 1990–2000: An analysis based on Landsat TM data. Remote Sensing of Environment 2005, 98(4):442-456.

Yordanov I, Velikova V, Tsonev T: Plant Responses to Drought, Acclimation, and Stress Tolerance. Photosynthetica 2000, 38(2):171-186.

Yoo SD, Greer DH, Laing WA, McManus MT: Changes in photosynthetic efficiency and carotenoid composition in leaves of white clover at different developmental stages. Plant Physiology and Biochemistry 2003, 41(10):887-893.

Thenkabail PS, Mariotto I, Gumma MK, Middleton EM, Landis DR, Huemmrich KF: Selection of hyperspectral narrowbands (HNBs) and composition of hyperspectral twoband vegetation indices (HVIs) for biophysical characterization and discrimination of crop types using field reflectance and Hyperion/EO-1 data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2013, 6(2):427-439.

 

In addition, we revised some minor errors, but they are not listed here. We are very grateful to the reviewers for offering insightful comments which significantly improve the manuscript.

 

Happy new year 2023.

 

Best wishes.

 

Author Response File: Author Response.pdf

Reviewer 3 Report (Previous Reviewer 4)

Authors have improved manuscripts quality as suggested by reviewers. There are some minor issues with English and some Chinese letters/names that reimaned across the manuscript. Other than this, the manuscript is now ready for publication.

Author Response

Response to Reviewer 3 Comments

 

Comments and Suggestions for Authors

Authors have improved manuscripts quality as suggested by reviewers. There are some minor issues with English and some Chinese letters/names that reimaned across the manuscript. Other than this, the manuscript is now ready for publication.

Response: We are very grateful to the reviewers for their time and effort in reviewing our manuscript very carefully and giving us many insightful comments and suggestions. We carefully revised the entire manuscript for each suggestion. The reviewers' comments and suggestions helped our manuscript a lot.

We read the entire manuscript carefully and found the following minor issues with English and some Chinese letters/names.

 

1. [On page 1 line 4] We have wrote two authors’ names, they are Li LIN and Aizhen XU, who contributed some data to the manuscript. [On page 1 line 11 ] and have supplemented their units. “⁴ Fujian Jingwei Digital Technology corporation, Fuzhou 350001, Fujian, China”
2. [On page1-2 line 48-50] “Obtaining information on the spatial and temporal distribution of maize yield in a timely and accurate manner is essential in ensuring national food security and rationalizing the country’s agricultural structure [3]. “We deleted the “country’s”.
3. [On page 2 line 53] We have changed “Yield = f (Climate) + (Soil) + (Genotype) + (Management).” to“Yield = f (climate) + (soil) + (genotype) + (management).”
4. [On page 2 line 98] We have changed “multisatellite data” to “multiple satellite data”.
5. [On page 3 line 116-118] “For example, GP3 (from silking to dough, i.e., from nutritional development to reproductive development) is a critical period for maize, as it is most sensitive to environmental changes.” Here, the GP3 is the abbreviation in the citation, we have changed “GP3” to “the period”.
6. We checked and and confirmed some Chinese letters/names in our manuscript. For instance, [On page 4 line 181] We have changed “Huanghuai sea region” to “Huang-Huai-Hai region”.                      [On page 12 line 429-431] “Note: (d) to (f) show the time series curves of the high-yield fields in Kangning County, Liaoning City, and Liaoning Province (yield = 8808 kg/ha) and the low-yield fields in Pianguan County, Shanxi City, and Shanxi Province (yield = 3723.3708 kg/ha)”

12. [On page 9 line 319] Table 3 Brief descriptions of spectral indexes, climate factors, and combined indexes, we deleted the “NMDI_NMDI₂”, , NMDI_NMDI₂ is not meaningful and we excluded such indexes.
13. [On page 11 line 392-393] For the Figure 4, We have changed “T_night2” to “LST_night2”. “LST_night2” represents the value of Night land surface temperature for the vegetative growth period.
14. [On page 12 line 425-426] For the Figure5, We have changed “High yield field” to “High-yield field”, and have changed “Low yield field” to “Low-yield field”.
15. [On page 13 line 448-449] For the Figure 6, We have changed “T_{early and late}” to “T_{1 and 2}”. We have changed “T_8-day” to “T_{eight day}”. And 1 and 2 in “NMDI_{1 and 2}”, “T_{1 and 2}” represent the values of both the vegetative and reproductive growth periods.
16. We added "note" in front of the sentence with the additional description below the figure name. [On page 14 line 479] The modified expression is as follows:“Note: NMDI₁ represents the value of the vegetative growth period. NMDI₂ represents the value of the reproductive growth period. Other items are similar.”       [On page 15 line 502] The revisedied expression is as follows:“Note: NMDI₁ represents the value of the vegetative growth period, NMDI₂ represents the value of the reproductive growth period, and the others are similar.”
17. [On page 16 line 567] We have changed “NRI1510” to “NRI₁₅₁₀”. This index is an index constructed by the wave 1510, “1510” and should be written in the form of a small marker.
18. We added this sentence to the Acknowledgements section to express our appreciation.  [On page 19 line 698-700] “We are very grateful to the Editorial Board and anonymous reviewers for offering insightful suggestions and detailed comments which significantly improve the manuscript.”

In addition, we revised some minor errors, but they are not listed here. In addition, we revised some minor errors, but they are not listed here. We are very grateful to the reviewers for offering insightful comments which significantly improve the manuscript.

 

Happy new year 2023.

Best wishes.

 

 

 

Author Response File: Author Response.pdf

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 and Editor.

After reading the paper, I have the following comments:

11. In the abstract, include the objectives of the study, as it appears on page 3, column 114-119 but in a summarized way.

 

P2. Page 3, line 102-103: “Second, the combined application of multi-source data could improve crop yield prediction [27] . The fusion of multi-source data a…”.

 You should include other references and mention multiple sources that could improve crop yield prediction. It would be merger and/or combination or just merger. Explain and include in this section.

 

33. Page 3, line 110-112: “In this study, the whole growth period was divided into early growth and late 110 growth periods, and more than one hundred yield-influencing factors such as water con- 111 tent, pigment content, nutrient elements, biomass, and climatic factors were systematically evaluated.”

What was the criterion for dividing the growth period of the crop in two? Explain it. You don't need more than 100 factors to calculate crop yield. Remember that:

Yield = f (climate) + (soil) + (Genotype) + (management).

Other subvariables can be derived from these variables. When many variables are used, they generate a lot of confusion and must be grouped by principal component analysis to find out which variables have more influence on the dependent variable (crop yield). Improve the wording of this section and include the yield equation generated by CIMMyT with at least two references.

 

44. Page 3 line 114 you say: “The aim was to investigate…”

Should say: The objective of this work was…

55. Page 3, lines 115-119. The objectives should not be presented as questions, but should be statements made, written in a row and without numbering. Eliminate numbering and improve wording.

 

66. Page 3, line 120, Section 2. Materials.

 This section 2. Must be entitled "Materials and Methods" and eliminate the title of section 3: Methods. From section 2, renumber subtitles progressively.

 

77. Page 4, line 37-38: Figure 1 contains a lot of information that confuses the reader; you should improve and simplify it. Page 6, line 190-191: do the same with figure 2. Page 7, lines 117-118 the title is repeated as Figure 1, it should be Figure 3. Give progressive numbering to both figures and tables.

 

88. Page 7, lines 232-242, section 3.3. maize yield estimation

 The individual spectral indexes and combined with information of some climatic elements (Table 3), are not enough to estimate the crop yield with an acceptable level of precision. Soil variables (organic matter content, pH, texture, porosity, cation exchange capacity, etc.) and management (topological arrangement, plant density, fertilization, planting periods, etc.) are required, which are not included in this research work.

9. Page 10, lines 348-349: Figure 5 contains a lot of information (it's actually 7 figures in one). You should simplify it and leave only the one that is most related to the text of the corresponding section. It is recommended to do the same with Figures 7 and 8.

 

110. Page 16, lines 561-576:

The conclusions must be clear, and without repetition of texts mentioned in previous paragraphs. It is recommended to delete the following paragraph: “In this study, we proposed a maize yield estimation method incorporating feature knowledge of biophysical variables and temporal aggregation in China at county level. The maize yield in China from 2015 to 2019 was evaluated.”

The accuracy of the yield estimate (R2 = 0.78) is low, and when incorporating water content, pigment content, nitrogen content and climatic factors, the accuracy of the estimate remains the same (R2 = 0.7832). Explain why it did not improve significantly. Also, explain why other additional statistical criteria were not used to assess the precision of the results (confusion matrix, kappa analysis, etc).

Comments for author File: Comments.pdf

Reviewer 2 Report

Thank you so much for the opportunity to review the paper which aims to find the indices and factors sensitive for yield evaluation (estimation), effect of temporal aggregation on the yield, and way to achieve national level maize yield estimation by fusing feature knowledge of biophysical variables and temporal aggregation.

The paper indeed carries a novel idea of estimating maize yield using 53 different indicators, however the way (methods) to do is largely missing. It is not clear, what was the methods used to estimate the sensitivity, not clear what the temporal aggregation means though the paper claims this research consider the early and late growth period instead of the whole growth period, and also the way to achieve national level yield estimation does not came out clearly. Conclusion could be drawn based on the three main research objectives and their possible implications should be discussed. Moreover, the paper is very crude requiring a thorough polishing of writing including English editing.

Comments for author File: Comments.pdf

Reviewer 3 Report

This manuscript explores a topic of estimating maize yield. The authors systematically evaluate the sensitivity of spectral indices, composite indices and climatic factors on yield, as well as the effect of temporal aggregation on maize yield estimation, and propose an evaluation method that combines knowledge of biophysical variables and temporal aggregation characteristics, providing knowledge of characteristics and a basis for indicator selection for crop yield estimation studies. The manuscript is well organized and the following comments are recommended for consideration.

1.      I think in the introduction the authors need to briefly introduce some background knowledge on corn yield estimation.

2.      Line 49. The first mention of LAI in the manuscript is not written in its full name. However, the full name and abbreviation are written again in line 51. There are similar problems in several other places in the manuscript.

3.      Line 115, the serial numbers are misused.

4.      Line 137, Figure 1 (c), the phenological periods of maize can't be normal distribution.

5.      Line 154, how to solve the inconsistent problems of the  spatial and temporal scales of the different data.

6.      Line 399, Line 466, the maize yields are mainly from 200 to 600 kg per mu. They are obvious undervaluation.

7.      Some diagrams in the article are typographically crowded, with inconsistent font sizes and unclear character occlusion displays.

8.      The manuscript is very rich in content and workload, and the writing is coherent and clear, so it may be accepted after making minor changes.

Reviewer 4 Report

This manuscript presents a study of different vegetation indices at single and time series level and their impact/influence for estimating maize yield in China. The manuscript is not well-organized, it is not clear what the novelty is, authors do not seem to understand the real meaning of vegetation indices with respect to biophysical meanings. English is really poor and makes it almost impossible to follow. Sentences are way too long, making the reading much more complext. Further comments can be found in the next.

 

ABSTRACT

 

- The abstract cannot be written in past tense. It sounds like Earth and human kind no longer exist. You are describing something that you want to do.

 

- L18, it is not readable. Not possible to understand what do you mean.

 

- L19-L23. This sentence is way too long.

 

- There is no context on what is the relevance of this work, and what has been done so far in literature, what is the general existing problem?. You need to re-write the abstract.

 

- L27. This is not readable, English is really bad. The whole abstract so far does not make any sense. There are lots of repetitions, no clear idea on the goal and what has been done.

 

- You passed the entire time talking about findings, but no clear context or explanation on what you mean to do is stated. English is poor and no organization can be seen so far. How does your abstract connect to the title of your manuscript?.

 

INTRODUCTION

 

- Please make use of the proper template and citation format for MDPI.

 

- Once again, everything is written in past tense. You should not do so in here, the only place where you can use this tense is on the results and conclusions. Please be careful with English. It has to be carefully revised.

 

- L50, were used by you?. 

 

- L68. Typo.

 

- L99. What?. the method?.

 

- The entire introduction must be re-written. English must be revised. There is no clarity of context, state of the art, problem/gaps. There is not even clarity about what exactly are authors proposing.

 

MATERIAL

 

- L122-124. This does not belong in here. This is more related to the introduction.

 

- Figure 1. Please improve: start by separating all the maps in different figures. Write down a proper figure description in each case. There is way too much information and it is not possible to follow what do you intend in here. Are you working at entire Country level?

 

- Table 1, 2 (and I presume all of your figures and tables). Please improve Table description. You cannot simply add two words and that's it.

 

- Table 2. Why to put K for some and Kelvin for others?. Please homogenize.

 

- L155-163. This belongs rather to the methodology or to the experimental setup.

 

- Must be improved. English problems overall and organization/logical flow missing.

 

METHOD

 

- L187. Missing period.

 

- Figure 2. Where is the general description of your method?. It should appear at first. You are saying in the previous paragraph that vegetation indices do not work and those are the standard in literature, but here you want to use something else. But I see you are still using vegetation indices. What is the sense of your work then?. You need to be careful with your statements and keep a clear idea of your work, as well as an explanation.

 

- General: The methodology used is not clear. How does it contribute to imprpove what already present in literature?. The fact that you have not performed a proper state of the art analysis on your introduction does not mean that there are not plenty of works out there working on the same direction and exploiting the same sort of method/input information. English problem still remain.

 

RESULTS

 

- A short paragraph connecting the method with data is missing. This should be also followed by a proper design of experiments and a description on how results are evaluated or what are they compared with to prove reliability.

 

- Figure 5. How is a possible reader suppose to understand the relationship that you claim to show in here if no single plot is shown that has both the indices and the yield production together?. Once again, please avoid making this sort of complex figures without a clear description of what is being presented and with way too much information that a possiblel reader cannot connect without your interpretation.

 

- Figure 7 is the same than figure 5 and all the figures on your manuscript..

 

- The information reported on the results (text) is way too much, but no clear analysis/understanding of what is being presented can be easily reached by a possible reader. English does not help on this as well. You need to re-write everything by reducing the current lenght and focusing on the main findings, according with your missing goals for this work. There is no single validation/comparison of your results with other methodologies. How can one see if your proposed methodology is working and working any different from what already existing in literature?.

 

DISCUSSION

 

- First paragraph. There are 5 lines and a single sentence. You keep writing down this long sentences that are hard to impossible to follow.

 

- This section needs to be reduced in accordance with similar suggestions than results.

 

CONCLUSIONS

 

- The conclusions are well-written, but it is rather difficult to understand how do you support the different estatements. Please improve the entire manuscript.