Modeling Soil CO2 Efflux in a Subtropical Forest by Combining Fused Remote Sensing Images with Linear Mixed Effect Models
Round 1
Reviewer 1 Report
Thank you for the opportunity to review this Manuscript (Modeling soil CO2 efflux dynamics in subtropical forests combined with multi-source remote sensing fusion images and linear mixed effect models). The study has sufficient and compelling results and demonstrates that image fusion was feasible to improve the spatiotemporal resolutions of soil CO2 efflux dynamics, wherein a STI-FM model was employed to image fusion. The MS is well-written. A minor revision is needed and the points that should be considered by authors listed below.
Line 16: It is hard to recognize what the abbreviation of FRs represents for.
Line 87- and line 96: What does the abbreviation of GY represent for (Orchard or Guoyuan)?
Line 150-179: How did equation (1)-(6) derived? Please provide the related references (or other details)?
Line 150-179: How did equation (7) derived? Please provide the related references (or other details)?
Line 195: Please provide more details about image fusion, such as equations, derivative process and other materials in this part (2.3.3 STI-FM fusion model).
Line 211-213: Where did equation (8)-(10) be referenced?
Line 233: What is Marginal R2 (mR2) and conditional R2 (cR2)? Please provide the related references, equations or other materials.
Line 263-265: In Figure 4, what dose ‘Corr’ represent for? What is the implication of value A and B? Please provide more specific explanation. The same for Figure 8.
Author Response
Responses to the reviewer’ comments:
Reviewer #1:
Thank you very much for your comments about our manuscript (ID: remotesensing-2205298) entitled “Modeling soil CO2 efflux in a subtropical forest by combining remote sensing fusion data with linear mixed effect models” for publication in Remote Sensing. I am very grateful to your comments for the manuscript. According to your advice, we amended the relevant parts in manuscript. All of your questions are answered below.
- Line 16: It is hard to recognize what the abbreviation of FRs represents for.
Response: Based on the revised full name, we have decided to use "FSCO2" as the abbreviation for consistency throughout the paper.
- Line 87- and line 96: What does the abbreviation of GY represent for (Orchard or Guoyuan)?
Response: Thank you for your valuable suggestions for our work. GY represents for the Guoyuan. I sincerely apologize for our oversight. Thanks again.
- Line 150-179: How did equation (1)-(6) derived? Please provide the related references (or other details)?
Response: Thanks again. Related references have been added to the MS, and explained in detail in MS.
- Line 150-179: How did equation (7) derived? Please provide the related references (or other details)?
Response: Thanks for the referee’s kind advice. Related references have been added to the MS.
- Line 195: Please provide more details about image fusion, such as equations, derivative process and other materials in this part (2.3.3 STI-FM fusion model).
- Line 211-213: Where did equation (8)-(10) be referenced?
Response: Thank you for your constructive feedback, which has greatly helped to improve the quality of our paper. We have included detailed information about fusion-LST in the methods and results sections of the paper, with the relevant pages specified. Once again, we appreciate your input and support.
The modified revision can be found on lines 196-224, page 6:
2.3.3 STI-FM fusion model and validation
The STI-FM model is a fusion method proposed by Khaled hazaymeh in 201517. The aim of the model is to use two different sources of satellite remote sensing images to generate high-resolution fusion products with both high temporal and spatial resolution. The model is based on two assumptions. First, there is a linear relationship between two inconsistent MODIS LST images, and second, the LSTs obtained from Landsat-8 and MODIS images at a specific time (such as T1 or T2) are similar. The core idea of the model is to use the linear relationship between the two MODIS LST products to generate Landsat-8 LST prediction data of high time series. In other words, the model uses the linear relationship between the MODIS LST in T1 and T2 to generate a synthetic Landsat-8 LST image in T2 using the Landsat-8 LST image in T1.
(8)
(9)
where, and are two consecutive MODIS LST images; a and c are the slope and intercept between the and , respectively; is Landsat_8 images collected from same time with ; is the fusion images in T2 and same time and spatial resolution of .
hazaymeh applied the STI-FM model to the semi-arid area of the Middle East and Jordan. When applying to the subtropical monsoon climate region, it is necessary to verify the applicability of the model. Previously, scholars applied the model to the Great Bay area of Guangdong, Hong Kong, and Macao and verified its feasibility16. The model was performed in the Google Earth Engine (GEE) platform, using R Language and ArcGIS 10.8 software. The accuracy of model is evaluated by assessing their adjusted R2 and root mean square error (RMSE) using R Language.
(10)
(11)
where, is the actual Landsat 8 LST value; is the Landsat 8 LST value synthesized by the model; is the average value of the actual Landsat 8 LST; is the average value of Landsat 8 LST synthesized by the model; N = 10000.
And on lines 275-293, page 9:
3.2.1. Fusion LST datasets and accuracy assessment
Figure 5A shows a qualitative comparison of Landsat-8 LST and Fusion-LST images on 28 September 2019, highlighting their similarity in features. The study also investigated three different elevations during the study period and found that the Fusion-LST image accurately predicted LST in various topographic conditions. Histograms were generated for actual Landsat 8 LST and Fusion-LST images for the entire study area and revealing their similarities (Fig. 5B). For quantitative evaluation, Fusion-LST and MYD LST of the entire study area were plotted on 28 September 2019, and further Fusion-LST and topsoil temperature in sampling chamber points were also plotted during the study period (Fig. 5C&D). Strong relationships were found between the variables of interest, with R2 values of 0.77 and 0.60 and RMSE values of 0.55 and 0.24, respectively. Additionally, the close relationship between the regression line of MYD-LST and Fusion-LST and the 1:1 line indicates a strong correlation between the two.
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Figure 5 Comparative example between the Fusion-LST and Actual Landsat8-LST in different altitude (DEM= 498 m, 262 m, 1040 m) of study area in Sep 28, 2019 (A). The histogram plot of Fusion-LST and Landsat8-LST (B). Relationship between the MYD-LST and Fusion-LST (C) and relationship between the Field based top soil temperature and Fusion-LST (D).
- Line 233: What is Marginal R2 (mR2) and conditional R2 (cR2)? Please provide the related references, equations or other materials.
Response: Thank you for your feedback. The references have been added.
- Line 263-265: In Figure 4, what dose ‘Corr’ represent for? What is the implication of value A and B? Please provide more specific explanation. The same for Figure 8.
Response: Thank you for you kind advice. ‘Corr’ represent for the correlation coefficient between the FSCO2 and environmental variables. A and B represents the two sampling sites : 1. Guoyuan (GY), 2. Chenhe Dong (CHD).
We appreciate your feedback and input, and we hope this change improves the clarity and readability of the paper. Please let us know if there is anything else we can do to improve the paper further.
Reviewer 2 Report
Dear Authors:
In the article entitled Modeling soil CO2 efflux dynamics in subtropical forests com-2 bined with multi-source remote sensing fusion images and lin-3 ear mixed effect models” . The authors used time series Landsat-8 LST and MODIS LST fusion images, and the 20 Linear Mixed Effect Model to estimate the forest soil CO2 emission efflux (FRs) at the basin scale.
Although the results reported in the present paper sound useful, but the paper needs some minor modifications to be suitable for publication. I will list here some general and significant comments, which need the authors to response with them. Where there are other comments in the attached PDF version of the submitted paper.
1- Abstract, seems to be good
2- Introduction is presented good and enough
3- Results and discussion: are discussed enough and supported appropriate figures and table.
4- The conclusion is informative and concise
5- Some comments are put for the references. Please check the PDF version
6- some information need to be completed:
- There are some abbreviations need to be added through the text
- There are some references in the text but missed in the reference list
- Please replace the word “et al.” in 9 references by the full names of the other authors, in addition to the first author.
- The Supplementary Figure and Two tables did not inserted in the text body, please refer to the them in the text
Finally, the paper may be publishable in remote sensing but after minor revisions. The authors are encouraging to response with the above comments/others in the attached PDF version.
Thank you for your patience
Comments for author File: 
Comments.pdf
Author Response
Reviewer #2:
Thank you very much for your comments about our manuscript (ID: remotesensing-2205298) entitled “Modeling soil CO2 efflux in a subtropical forest by combining remote sensing image fusion data with linear mixed effect models” for publication in Remote Sensing. We are very grateful for your comments on the manuscript. According to your advice, we amended the relevant part in the manuscript. Special thanks to you for your good comments. All of your questions are answered below.
- Abstract, seems to be good
Response: Thank you for your kind words of encouragement and support. We appreciate your feedback and are glad that our work has been helpful.
- Introduction is presented good and enough
Response: Thank you for your affirmation of our work.
- Results and discussion: are discussed enough and supported appropriate figures and table.
Response: Once again, we appreciate your input and support.
- The conclusion is informative and concise
Response: Thank you.
- Some comments are put for the references. Please check the PDF version:
5.1 Figure 2 The flowchart of the research
5.2 ArcMap _what is the version?
5.3 NDVI/ GEE _write the complete names fro the first time
5.4 Hazaymeh and Hassan 2015 NOT et al.
5.5 delete, the family name enough
5.6 Landsat-8
5.7 please add this reference to the ref. list
5.8 these fig. and table did not inserted in the Text
5.9 where is table S2
5.10 Reference [2-4],[19-21],[23-25],[33] please write the complete authors names
Response: Thank you for your time and efforts into our work.
5.1 “Figure 2 The flowchart of the research” is the title of figure 2, modified below the Figure 2.
5.2 revised to “ArcMap 10.8 ”.
5.3 Completed name added in MS.
5.4 Completed name added in MS.
5.5 Deleted “et al”.
5.6 Added
5.7 Thank you for your feedback. We apologize for the oversight and have updated our paper to include all the relevant references.
5.8 Inserted in the text. Thanks again.
5.9 Table S2 can be find on line 330-331
5.10 Corrected.
Thanks again.
- There are some abbreviations need to be added through the text
Response: Thank you for letting us know. We appreciate your efforts in reviewing and checking the paper, and we are pleased to hear that all the required sections have been added. We are committed to delivering a high-quality and comprehensive paper, and we appreciate your input in helping us achieve this goal.
- There are some references in the text but missed in the reference list
- Please replace the word “et al.” in 9 references by the full names of the other
authors, in addition to the first author.
Response: Thank you for your feedback and for going through the references. We appreciate your attention to detail and your help in ensuring that all the references are accurate and up-to-date. We are also grateful for your efforts in organizing the references according to the requirements of the journal.
- The Supplementary Figure and Two tables did not inserted in the text body, please refer to the them in the text.
Response: Thank you for your feedback. We appreciate your efforts in reviewing the paper and are glad to hear that all the necessary changes have been made and the references have been added to the appropriate sections. We hope that these updates improve the clarity and accuracy of the paper.
We appreciate your feedback and input, and we hope this change improves the clarity and readability of the paper. Please let us know if there is anything else we can do to improve the paper further.
Author Response File: Author Response.pdf
Reviewer 3 Report
The article "Modeling soil CO2 efflux dynamics in subtropical forests combined with multi-source remote sensing fusion images and linear mixed effect models " sent for review to Remote Sensing journal, used time series Landsat-8 LST, MODIS LST fusion images, and the Linear Mixed Effect Model to estimate the soil CO2 emission efflux at basin scale. The authors found a strong positive correlation between Fusion-LST and the abiotic factors represented by the air and soil temperature. From the three FRs models built, the monthly random effect model performed better with and without the random factors. The topic is relevant in the field and the article is within the Remote Sensing scope providing new and useful information for the journal readers. I recommend the publication after minor revisions regarding the abstract and the study area.
1. Abstract section is not summarizing well the key points of the research work. The findings/importance of this research should be presented in one sentence at the end of this abstract section.
2. The data collection, the use of multi-source remote sensing fusion images with both high spatio-temporal resolutions is adequate to the purpose of the article. However, the study area definition is imprecise generating confusion. What exactly is the LXH basin in Guangzhou province? Is it the Liuxihe River basin? From total basin area you are covering a surface of 456.7 square km only? You mention the selected Chenhedong (CHD) Nature Reserve and Orchard (GY) in line 86-87. However in Figure 1, you write The Guoyuan 96 (GY) and Chenhe Dong (CHD). Please clarify, explain and present more accurate your study area (maybe you should add another map). You country name should be written China not china (line 83).
3. The references cited are up to date but I suggest one more reference for the study area: https://doi.org/10.3390/rs13061168.
Author Response
Reviewer #3:
Thank you very much for your comments about our manuscript (ID: remotesensing-2205298) entitled “Modeling soil CO2 efflux in a subtropical forest by combining remote sensing image fusion data with linear mixed effect models” for publication in Remote Sensing. We greatly appreciate both your help and associated improvement to this paper. Special thanks to you for your good comments. All of your questions are answered below.
- Abstract section is not summarizing well the key points of the research work. The findings/importance of this research should be presented in one sentence at the end of this abstract section.
Response: Thanks for the Reviewer’s helpful advice. We modified the Abstract according to your comment.
The modified revision can be found on lines 19-31, page 1:
Abstract: Monitoring tropical and subtropical forest soil CO2 emission efflux (FSCO2) is crucial to understand the global carbon cycle and terrestrial ecosystem respiration. Previous studies estimated FSCO2 at regional scales based on various remote sensing images, however, there is still a need for more accurate calculations using multi-source remote sensing fusion images with high spatio-temporal resolutions. Here, we combine time series Landsat-8 LST and MODIS LST fusion images, with the Linear Mixed Effect Model to estimate the FSCO2 at watershed scale. Results show that the modeling without random factors, and the use of Fusion-LST as the fixed predictor resulted in 47 % (marginal R2 = 0.47) of FSCO2 variability in the Monthly Random Effect Model while it only accounted for 19 % of FSCO2 variability in the Daily Random Effect Model and 7 % in the Seasonally Random Effect Model. However, the inclusion of random effects in the model’s parameterization improved the performance of both models. The Monthly Random Effect Model that performed optimally had an explanation rate of 55.3 % (conditional R2 = 0.55 and t value > 1.9) for FSCO2 variability and yielded the smallest discrepancy from observed FSCO2.
- The data collection, the use of multi-source remote sensing fusion images with both high spatio-temporal resolutions is adequate to the purpose of the article. However, the study area definition is imprecise generating confusion. What exactly is the LXH basin in Guangzhou province? Is it the Liuxihe River basin? From total basin area you are covering a surface of 456.7 square km only? You mention the selected Chenhedong (CHD) Nature Reserve and Orchard (GY) in line 86-87. However in Figure 1, you write The Guoyuan 96 (GY) and Chenhe Dong (CHD). Please clarify, explain and present more accurate your study area (maybe you should add another map). You country name should be written China not china (line 83).
Response: Thank you for your kind advice. The Liuxi River basin is located in the north of Guangdong Province, which is divided into three small watersheds: upstream, downstream and midstream. The study takes the upstream of the Liuxi River basin as the study area, which is also the source area of the Liuxi River basin. There is a reservoir in the study area called Liuxi River Reservoir, so the study area is called Liuxi River Reservoir Basin.
We are very sorry about our carelessness. The field survey site that we selected are Chenhedong (CHD) Nature Reserve and GuoYuan (GY). We have revised your questions in the MS. Once again, we appreciate your input and support.
The modified revision can be found on lines 10-30, page 1:
The study area (23.67–23.96 ° N, 114.03–113.75 ° E) is a headwater catchment of Liuxihe (LXH) River Basin, named as the LXH Reservoir watershed and located in the northern part of Guangdong Province of China.
- The references cited are up to date but I suggest one more reference for the study area: https://doi.org/10.3390/rs13061168.
Response: We are very appreciated to this important comment from the reviewer. Added.
We appreciate your feedback and input, and we hope this change improves the clarity and readability of the paper. Please let us know if there is anything else we can do to improve the paper further.
Author Response File: Author Response.pdf
