All-Sky Soil Moisture Estimation over Agriculture Areas from the Full Polarimetric SAR GF-3 Data
Round 1
Reviewer 1 Report (Previous Reviewer 1)
The authors have revised the manuscript according to the comments.
Author Response
Thank you very much for your comments! According to the opinions of other reviewers, we appropriately revised the manuscript.
Author Response File: 
Author Response.pdf
Reviewer 2 Report (Previous Reviewer 2)
I appreciate the author’s efforts in revising the manuscript based on the comments and suggestions. By going through the revised manuscript, it looks like there is a substantial revision, but it’s very difficult to follow those due to the poor description in the response note. Authors mostly stated that comments were addressed, but where (Line no or page no.) the modification was made in the revised manuscript is not mentioned. This creates difficulty in tracking the corrections/improvements.
Though the authors revised the manuscript, still the significant lacking such as
"Why did the authors perform the analysis only for 1day, even though the GF3 has an excellent temporal resolution and a good in-situ soil moisture network to provide continuous measurements? By the time authors do not include the analysis “for different GF3 images (maybe for different months/seasons/years) to have SM maps for different wetness conditions and the corresponding validation,” the usability/validity of the proposed approach is challenging/limited. Authors can collect a few images as they have collected for 7 August 2019, I feel that data might not be fully restricted for many years. If data is entirely restricted then what is the motivation of this study?
Based on the revised manuscript, I have the following queries/comments:
1. As per my previous “Comment1” it’s not clear how/where did the authors address my concern in the revised manuscript
The concern is “What does this research contribute to this area of study and what information from it will be relevant to international researchers outside of the specific location of North China Plain?- Is the other region will be having similar accuracy using the proposed approach, or it need to be investigated again for other regions?
2. “Comment 2- “Dataset”- The given details of the SM dataset is not properly linked and referred/cited.
The author mentioned “The in situ measurements data employed in the study from the China National Meteorological Science Data Center (https://data.cma.cn/) but the given website link does not show any details about soil moisture measurements. Its shows the details for meteorological variables.
Besides, the given citation [41] “Wu, Y.; Fan, B.; Wang, J. Design and implementation of Henan meteorological observation station network management system. SCIENCE & TECHNOLOGY INFORMATION 2019, 17(35), 20-26. DOI:10.16661/j.cnki.1672-3791.2019.35.02051” to support the in-situ measurement is not available online even though using the given DOI and title search.
I suggest providing the proper link and citation for the in-situ soil moisture measurements.
3. “Methodology” –“ After removed the backscattering effect of vegetation layer” – How it was performed, explain it. Also, change “removed” to “removing”
4. “Comment 6 – Validation”
[A] Regarding “Validation across space instead time-series.”
The validation across space is a good approach when less data coverage is available in time-space. However, this validation approach is still not well in practice and involves a few assumptions. These assumptions should be properly addressed whenever validation across the space is being performed, which is missing in the manuscript. Please read the following papers to support your validation approach and to understand the assumptions.
(i) “Soil Moisture Retrieval Using SMAP L-Band Radiometer and RISAT-1 C-Band SAR Data in the Paddy Dominated Tropical Region of India (2021). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 10644 – 10664. DOI: 10.1109/JSTARS.2021.3117273 – explore “page 10658” for validation across the space with limited data availability.
(ii) “Validation of SMAP Soil Moisture Products Using Ground-Based Observations for the Paddy Dominated Tropical Region of India (2019)," IEEE Transactions on Geoscience and Remote Sensing, 57(11), 8479-8491, doi: 10.1109/TGRS.2019.2921333.- explore for the assumptions
[B] In my previous comment, my concern was “How can we justify the accuracy/significance of the derived spatial pattern of soil moisture in reference to only one-day soil wetness and vegetation conditions? Since there is good temporal coverage of the SAR dataset (GF3 revisit period is 2-3 days) and good availability of in-situ soil moisture measurements, I suggest providing soil moisture retrievals maps for different wetness conditions and conducting a performance analysis of the retrievals using in-situ observations.”
However, the author’s response explains how the validation was performed. My major concern is “If the GF3 has a good temporal resolution and there is a good in-situ network of soil moisture to provide continuous measurements, then why do the authors not perform the same analysis (SM retrievals) for different GF3 images (maybe for different months/seasons/years) to have SM maps for different wetness conditions. This analysis will provide a better platform to avoid the shortcoming in the validation process, where authors can validate the proposed approach for different days with different soil wetness conditions. Explore the suggested reference i) “Soil Moisture Retrieval Using SMAP L-Band Radiometer and RISAT-1 C-Band SAR Data….” to perform the analysis.
5. Figure 2. The space distribution of SM. – Add the date of this soil moisture map same for other related figures of this manuscript.
Is SM shown in Fig.2 is from in-situ measurements. If yes, then mention the same in the caption to avoid confusion with Fig.4.
Author Response
I appreciate the author’s efforts in revising the manuscript based on the comments and suggestions. By going through the revised manuscript, it looks like there is a substantial revision, but it’s very difficult to follow those due to the poor description in the response note. Authors mostly stated that comments were addressed, but where (Line no or page no.) the modification was made in the revised manuscript is not mentioned. This creates difficulty in tracking the corrections/improvements.
Answer：The contents of the first and second modifications have been marked in the round2 of revised paper.
Though the authors revised the manuscript, still the significant lacking such as
"Why did the authors perform the analysis only for 1day, even though the GF3 has an excellent temporal resolution and a good in-situ soil moisture network to provide continuous measurements? By the time authors do not include the analysis “for different GF3 images (maybe for different months/seasons/years) to have SM maps for different wetness conditions and the corresponding validation,” the usability/validity of the proposed approach is challenging/limited. Authors can collect a few images as they have collected for 7 August 2019, I feel that data might not be fully restricted for many years. If data is entirely restricted then what is the motivation of this study?
Answer：At present, the website (https://www.cheosgrid.org.cn/) has suspended the service of providing GF3 images, so we cannot obtain other GF3 data in a short time. We modified the model appropriately, and used the Sentinel-1 SAR data in different periods to verify the portability of the proposed method for estimating soil moisture.
Based on the revised manuscript, I have the following queries/comments:
- As per my previous “Comment1” it’s not clear how/where did the authors address my concern in the revised manuscript
The concern is “What does this research contribute to this area of study and what information from it will be relevant to international researchers outside of the specific location of North China Plain?- Is the other region will be having similar accuracy using the proposed approach, or it need to be investigated again for other regions?
Answer：The traditional method of retrieving soil moisture by water cloud model (WCM) is to calculate the vegetation backscatter coefficient, then subtract the vegetation backscatter coefficient from the radar backscatter coefficient to calculate the soil backscatter coefficient, and finally estimate the soil moisture in the study area according to the linear/non-linear relationship between measured soil moisture data and soil backscatter coefficient.
However, the main factors for the detection of the SM by radar include soil dielectric constant, surface roughness parameters, vegetation cover, etc. In vegetation covered areas, the composition of radar signals is significantly complex, and it is difficult to estimate the SM. Radar parameters also affect the estimation accuracy of soil moisture. Therefore, the influence of vegetation and radar parameters on radar backscatter is considered in this paper by combining water cloud model and Chen model. The relevant content is shown in the last two paragraphs of the “Introduction”.(Page 2, Line 64-83)
A large number of studies have proved that the Water Cloud Model and the Chen model are not limited by regions and and can be applied in many regions. Firstly, the soil backscatter coefficient is obtained by water cloud model. Then, the soil backscatter coefficient is brought into Chen model to retrieve soil moisture. We believe that the soil moisture retrieval model mentioned in the paper can be applied in other areas. Finally, we modified the model appropriately and retrieved the soil moisture of different study areas in other periods by using Sentinel-1 data. The final estimated soil moisture still has similar accuracy.
(Page 8, Line 230)
- “Comment 2- “Dataset”- The given details of the SM dataset is not properly linked and referred/cited.
The author mentioned “The in situ measurements data employed in the study from the China National Meteorological Science Data Center (https://data.cma.cn/) but the given website link does not show any details about soil moisture measurements. Its shows the details for meteorological variables.
Besides, the given citation [41] “Wu, Y.; Fan, B.; Wang, J. Design and implementation of Henan meteorological observation station network management system. SCIENCE & TECHNOLOGY INFORMATION 2019, 17(35), 20-26. DOI:10.16661/j.cnki.1672-3791.2019.35.02051” to support the in-situ measurement is not available online even though using the given DOI and title search.
I suggest providing the proper link and citation for the in-situ soil moisture measurements.
Answer：The website only provides in situ soil moisture data. References [41-42] showed the details about soil moisture measurements. However, these two papers were downloaded from China CNKI (https://www.cnki.net/) for a fee, and their language is Chinese. There are no open access papers showing details about in situ soil moisture data. So we can’t provide other link and citation for the in-situ soil moisture measurements.
- “Methodology” –“ After removed the backscattering effect of vegetation layer” – How it was performed, explain it. Also, change “removed” to “removing”
Answer：The relevant content has been explained (Page 4, Line 133-134). The details are as follows：
“The RVI is obtained using GF-3 data and brought into WCM to estimate the vegetation backscattering coefficient (). The soil backscattering coefficient （） can be calculated by subtracting the from the . ”
- “Comment 6 – Validation”
[A] Regarding “Validation across space instead time-series.”
The validation across space is a good approach when less data coverage is available in time-space. However, this validation approach is still not well in practice and involves a few assumptions. These assumptions should be properly addressed whenever validation across the space is being performed, which is missing in the manuscript. Please read the following papers to support your validation approach and to understand the assumptions.
(i) “Soil Moisture Retrieval Using SMAP L-Band Radiometer and RISAT-1 C-Band SAR Data in the Paddy Dominated Tropical Region of India (2021). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 10644 – 10664. DOI: 10.1109/JSTARS.2021.3117273 – explore “page 10658” for validation across the space with limited data availability.
(ii) “Validation of SMAP Soil Moisture Products Using Ground-Based Observations for the Paddy Dominated Tropical Region of India (2019)," IEEE Transactions on Geoscience and Remote Sensing, 57(11), 8479-8491, doi: 10.1109/TGRS.2019.2921333.- explore for the assumptions
[B] In my previous comment, my concern was “How can we justify the accuracy/significance of the derived spatial pattern of soil moisture in reference to only one-day soil wetness and vegetation conditions? Since there is good temporal coverage of the SAR dataset (GF3 revisit period is 2-3 days) and good availability of in-situ soil moisture measurements, I suggest providing soil moisture retrievals maps for different wetness conditions and conducting a performance analysis of the retrievals using in-situ observations.”
However, the author’s response explains how the validation was performed. My major concern is “If the GF3 has a good temporal resolution and there is a good in-situ network of soil moisture to provide continuous measurements, then why do the authors not perform the same analysis (SM retrievals) for different GF3 images (maybe for different months/seasons/years) to have SM maps for different wetness conditions. This analysis will provide a better platform to avoid the shortcoming in the validation process, where authors can validate the proposed approach for different days with different soil wetness conditions. Explore the suggested reference i) “Soil Moisture Retrieval Using SMAP L-Band Radiometer and RISAT-1 C-Band SAR Data….” to perform the analysis.
Answer：At present, the website (https://www.cheosgrid.org.cn/) has suspended the service of providing GF3 images, so we cannot obtain other GF3 data in a short time. Unable not determine the date that the GF3 image can be downloaded again.
We modified the model appropriately, and used the Sentinel-1 SAR data in different periods to verify the portability of the proposed method for estimating soil moisture. Related content is added in Section “4.3 Extension of SM retrieval Model”. After verification, the estimated soil moisture still has good accuracy using Sentinel-1 data. (Page 8, Line 230;)
- Figure 2. The space distribution of SM. – Add the date of this soil moisture map same for other related figures of this manuscript.
Is SM shown in Fig.2 is from in-situ measurements. If yes, then mention the same in the caption to avoid confusion with Fig.4.
Answer：The title of Figure 2 has been modified as “Figure 2. The space distribution of SM from in-situ measurements data on 7 August 2019.” (Page 4, Line 128)
Thank you very much for your comments!
Author Response File: Author Response.pdf
Reviewer 3 Report (Previous Reviewer 3)
I feel there are several inconsistencies between the original submission and the revised version. While in the original submission, the authors used soil moisture for the analysis and results, here a term ‘relative soil moisture’ is being introduced while completely ignoring the ‘soil moisture’ values presented in the original submission. No explanation of this sort is provided, it is confusion to understand if the actual values measured in the experimental sites are actual soil moisture or relative soil moisture, in any case the authors need to explain how the relative soil moisture presented in revised version was converted to soil moisture in the original submission.
Moreover, the authors have failed to convince the new contribution of this manuscript, since several works has already been published on estimating soil moisture from vegetated areas using radar data and with much dense soil moisture observations.
Author Response
I feel there are several inconsistencies between the original submission and the revised version. While in the original submission, the authors used soil moisture for the analysis and results, here a term ‘relative soil moisture’ is being introduced while completely ignoring the ‘soil moisture’ values presented in the original submission. No explanation of this sort is provided, it is confusion to understand if the actual values measured in the experimental sites are actual soil moisture or relative soil moisture, in any case the authors need to explain how the relative soil moisture presented in revised version was converted to soil moisture in the original submission.
Answer: In the process of translation, the paper mistakenly wrote the “soil relative moisture” into “relative soil moisture”. The soil moisture data used in the paper is “soil relative moisture”. The paper has been modified.
The soil moisture observation station actually measured the soil relative moisture (not the soil volumetric moisture). In the original submission, we converted the soil relative moisture data to soil volumetric moisture. However, when revising the paper, we found that using the original measured soil moisture data can make the paper more rigorous. Therefore, in the modified paper, the soil relative moisture has replaced the soil volumetric moisture..In Section “2.3, In situ measurements”, the paper illustrates that the in situ measurements data are soil relative moisture (Page 3, Line 113-114). The details are as follows：
“The meteorological bureau uses the GStar-I (DZN2) automatic SM observation instrument to collect hourly soil relative moisture data [41].”
Moreover, the authors have failed to convince the new contribution of this manuscript, since several works has already been published on estimating soil moisture from vegetated areas using radar data and with much dense soil moisture observations.
Answer: The traditional method of retrieving soil moisture by water cloud model (WCM) is to calculate the vegetation backscatter coefficient, then subtract the vegetation backscatter coefficient from the radar backscatter coefficient to calculate the soil backscatter coefficient, and finally estimate the soil moisture in the study area according to the linear/non-linear relationship between measured soil moisture data and soil backscatter coefficient.
However, the main factors for the detection of the SM by radar include soil dielectric constant, surface roughness parameters, vegetation cover, etc. In vegetation covered areas, the composition of radar signals is significantly complex, and it is difficult to estimate the SM. Radar parameters also affect the estimation accuracy of soil moisture. Therefore, the influence of vegetation and radar parameters on radar backscatter is considered in this paper by combining water cloud model and Chen model. To estimate SM in agriculture areas using the proposed model, we only need to update the coefficients of these indices using the corresponding train sample data.
The relevant content is shown in the last two paragraphs of “Introduction”(Page 2, Line 64-83) and second paragraph of “Conclusion”(Page 10, Line 261-266)
Thank you very much for your comments!
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report (Previous Reviewer 2)
I appreciate the authors’ efforts in working on another dataset such as Sentinel-1 to show the capability of the proposed algorithm and presenting the validation results for 7 July and 12 August 2019 in Fig.7. However, the performance metric ubRMSE is found to be 0.103 and 0.116 for July 7 and July 12, respectively, which is a very high soil moisture retrieval error. The current SMAP products have ubRMSE <0.04 m3/m3 and SMAP-Sentinel provides high-resolution products with ub-RMSE of 0.06 m3/m3. In this context, what is the use of the soil moisture retrieval using the proposed methodology in this manuscript having ub-RMSE > 0.10? Here I am not aware about the unit of ubRMSE (fraction or percentage, the authors should mention the unit to avoid confusion: the same suggestion was given in my first review in other comments 11), but looks like it is in a fraction.
- Fig. 5 and 7: What is meant by “measured/estimated soil relative moisture” – All the text and figure captions mentioned as “measured/estimated soil moisture”. I suggest rechecking these terms and correct accordingly.
- Be consistent with the term either “SM estimate” or “SM retrieval” in the whole manuscript. In different sections of the manuscript, the authors used different terms.
- Fig. 5 and 7: I am wondering how it is possible to have soil moisture value (in-situ) of nearly 90%. In my understanding, even if the soil is fully saturated its soil moisture value does not go more than 60% (soil porosity of typical mineral soils). In the case of peat and clay, the porosity might be higher (>60%) but typical soil moisture does not go more than 75%. So please cross-check the values or provide a few statements to support the given SM range (if the soil is highly porous).
On the other hand, the authors simply ignored my comment (comment 4 [A] Validation across space instead of time-series) which is important to address because the authors have performed the validation across space instead of time-series without mentioning its assumptions. Usually, the “Validation across space” approach is not well in practice and involves a few assumptions. These assumptions should be properly addressed whenever validation across the space is being performed. I am suggesting a few statements to be added in the section “3.3. Validation” for better scientific understanding for the readers. Please adopt/modify the suggested statements and update the citation numbers as per the requirements.
“Since this study is based on sparse coverage of the SAR dataset, we adopted an approach of substituting soil moisture comparison across space instead of time-series over a location to validate the soil moisture retrievals on a daily basis. In this approach, an ergodic substitution of space for time is adopted to match the grids of SM retrievals and in-situ SM measurement for a particular day with the assumption that those locations are geophysically similar in characteristics (i.e., biases in the grids to be similar due to spatial autocorrelation) and mimic the time-series with different soil moisture states [44,45].”
[44] “Soil Moisture Retrieval Using SMAP L-Band Radiometer and RISAT-1 C-Band SAR Data in the Paddy Dominated Tropical Region of India (2021). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 10644 – 10664. DOI: 10.1109/JSTARS.2021.3117273
[45] “Validation of SMAP Soil Moisture Products Using Ground-Based Observations for the Paddy Dominated Tropical Region of India (2019)," IEEE Transactions on Geoscience and Remote Sensing, 57(11), 8479-8491, doi: 10.1109/TGRS.2019.2921333
Author Response
- I appreciate the authors’ efforts in working on another dataset such as Sentinel-1 to show the capability of the proposed algorithm and presenting the validation results for 7 July and 12 August 2019 in Fig.7. However, the performance metric ubRMSE is found to be 0.103 and 0.116 for July 7 and July 12, respectively, which is a very high soil moisture retrieval error. The current SMAP products have ubRMSE <0.04 m3/m3and SMAP-Sentinel provides high-resolution products with ub-RMSE of 0.06 m3/m3. In this context, what is the use of the soil moisture retrieval using the proposed methodology in this manuscript having ub-RMSE > 0.10? Here I am not aware about the unit of ubRMSE (fraction or percentage, the authors should mention the unit to avoid confusion: the same suggestion was given in my first review in other comments 11), but looks like it is in a fraction.
Answer:We find that buildings will greatly affect the radar backscattering coefficient, and thus delete the radar image pixel data which containing buildings. Although the available data were reduced in the study, the accuracy of the estimated soil moisture was significantly improved. (Page 9, Line 248-251)
We referred to similar papers and ultimately retained only the correlation coefficients and the RMSE to evaluate the accuracy of estimated SM. (Page 5-6, Line 166-168)
- 5 and 7: What is meant by “measured/estimated soil relative moisture” – All the text and figure captions mentioned as “measured/estimated soil moisture”. I suggest rechecking these terms and correct accordingly.
Answer: The corresponding content has been modified.(Page 8, Line 222-237; Page 9, Line 251-255)
- Be consistent with the term either “SM estimate” or “SM retrieval” in the whole manuscript. In different sections of the manuscript, the authors used different terms.
Answer: All the “SM retrieval” was changed to “SM estimate” in the whole manuscript.
- 5 and 7: I am wondering how it is possible to have soil moisture value (in-situ) of nearly 90%. In my understanding, even if the soil is fully saturated its soil moisture value does not go more than 60% (soil porosity of typical mineral soils). In the case of peat and clay, the porosity might be higher (>60%) but typical soil moisture does not go more than 75%. So please cross-check the values or provide a few statements to support the given SM range (if the soil is highly porous).
Answer: The soil moisture observation station measures the soil relative moisture data. The soil volumetric moisture is generally used in the paper. The equation relationship between soil relative moisture and soil volumetric moisture recorded by meteorological stations is as follows:
Soil volumetric moisture=Soil relative moisture×Field water capacity×100%
Generally, the field water capacity is between 20% to 24%. The field water capacity of different soil texture is different. After conversion, the soil moisture (soil volumetric moisture) value in the paper does not exceed 40%. In order to make the data in the paper more rigorous, we did not convert the soil relative moisture into the soil volumetric moisture.
- On the other hand, the authors simply ignored my comment (comment 4 [A] Validation across space instead of time-series) which is important to address because the authors have performed the validation across space instead of time-series without mentioning its assumptions. Usually, the “Validation across space” approach is not well in practice and involves a few assumptions. These assumptions should be properly addressed whenever validation across the space is being performed. I am suggesting a few statements to be added in the section “3.3. Validation” for better scientific understanding for the readers. Please adopt/modify the suggested statements and update the citation numbers as per the requirements.
“Since this study is based on sparse coverage of the SAR dataset, we adopted an approach of substituting soil moisture comparison across space instead of time-series over a location to validate the soil moisture retrievals on a daily basis. In this approach, an ergodic substitution of space for time is adopted to match the grids of SM retrievals and in-situ SM measurement for a particular day with the assumption that those locations are geophysically similar in characteristics (i.e., biases in the grids to be similar due to spatial autocorrelation) and mimic the time-series with different soil moisture states [44,45].”
[44] “Soil Moisture Retrieval Using SMAP L-Band Radiometer and RISAT-1 C-Band SAR Data in the Paddy Dominated Tropical Region of India (2021). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 10644 – 10664. DOI: 10.1109/JSTARS.2021.3117273
[45] “Validation of SMAP Soil Moisture Products Using Ground-Based Observations for the Paddy Dominated Tropical Region of India (2019)," IEEE Transactions on Geoscience and Remote Sensing, 57(11), 8479-8491, doi: 10.1109/TGRS.2019.2921333
Answer: The suggested statements were added in the “3.3. Validation”. And the citation numbers were update as per the requirements. (Page 6, Line 172-179)
Thank you very much for your comments!
Author Response File: 
Author Response.docx
Reviewer 3 Report (Previous Reviewer 3)
The authors has provided clarifications for the queries raised by me. The manuscript in the present form needs further revision with respect to English Language, i see discontinuities at few places, authors should go through each section carefully and make it more readable.
Author Response
The authors has provided clarifications for the queries raised by me. The manuscript in the present form needs further revision with respect to English Language, i see discontinuities at few places, authors should go through each section carefully and make it more readable.
Answer: We have asked a professional organization to revise the English language.
Thank you very much for your valuable comments.
Author Response File: Author Response.docx
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
Soil moisture retrieval from SAR data is an important issue. This study uses GF-3 SAR data to retrieval soil moisture. There are some comments to be addressed.
The introduction is lack of review of current studies about soil moisture retrieval from SAR data, and the key problems in the current studies.
Line 99, the field observation data are not clear, how many samples?
Figure 4, the number of validation samples is only eight, it is too small to achieve a stable soil moisture estimation model.
Reviewer 2 Report
The manuscript presents an analysis on the retrieval of soil moisture in all-sky vegetation areas using fully polarimetric synthetic aperture radar (SAR) GF-3 data. The author used the radar vegetation index (RVI) to overcome the limitation of optical data in the craterization of vegetation attributes under cloud cover conditions. The authors mentioned that soil moisture retrievals using the proposed approach in this study are not affected by cloud cover and have good accuracy.
I agree with the authors’ argument that vegetation characterization using optical remote sensing is always challenging in cloud cover conditions. Microwave remote sensing is one of the techniques which provide all-weather observations. In this direction, current satellite missions like SMAP and SMOS have great potential to provide soil moisture with an accuracy of +/- 4% to a global extent. Notably, the SMAP also retrieves the vegetation optical depth, VOD (i.e., vegetation attribute) using the Dual-Chanel algorithm to eliminate the dependency on optical remote sensing (i.e., NDVI observation) for estimating vegetation attribute information. Currently, the SMAP mission provides better soil moisture retrievals using VOD retrievals, especially in agricultural regions. Besides, SMAP-Sentinal products based on an active-passive algorithm also provide soil moisture retrievals globally at 1km and 3 km. Other than microwave satellite missions products, various research has been carried out in the past on soil moisture retrievals using Water Cloud Model (WCM) as used by the authors in this study. Past studies have proven well that the SAR dataset can be utilized for soil moisture retrievals effectively, and combining the same with WCM helps to achieve very high accuracy. Besides a lot of studies (e.g. Mondal et.al., 2020) shows the use of RVI in conjunction with the WCM model to achieve very high accuracy in surface soil moisture retrievals.
Therefore, I feel that this study is just a repetition of past work on using SAR data in the WCM model for soil moisture estimation and therefore fails to provide any novel/new scientific approach and significant findings with solid justifications/conclusions. It’s a rather straightforward study on “Estimation of the surface soil moisture using SAR data”, without any single scientific novelty. The authors used a well-known “WCM model” incorporating RVI as vegetation characterization to estimate the surface soil moisture, which has been widely investigated in the last two decades.
The manuscript fails to present a clear objective of the study in relation to past studies in a similar direction. Why this study is important? How will this research contribute to the scientific literature? In what way will this research be helpful in future studies? What is the spatial resolution of the soil moisture estimates? Other than the novel objectives, the major drawback of this research is its analysis/validation, which is limited only to one-day observations. In addition to the scientific fairness, the manuscript structure and language are poor and require much improvement before publishing. The authors use various abbreviations without explaining them at least once in the whole manuscript. The appropriate discussion and justification on analysis and results are missing. I feel the manuscript needs substantial revision before publishing in well-known journals like “Sustainability”.
There are several issues given below that need to be addressed by the authors:
Major Comments:
1. The introduction section needs subsequent revision along with a novel research statement focusing on an international context. How do the findings of this study inform or build upon the wide range of international research that has been carried out on “soil moisture estimation using SAR observations, especially over Water Cloud Model? What does this research contribute to this area of study and what information from it will be relevant to international researchers outside of the specific location of North China Plain? I recommend improving the Introduction section thoroughly.
Besides, the “Conclusions” is not well written. I feel the conclusion should be a take-home message for the readers and should be related to the work's problem statement. The conclusion section of this manuscript is mostly having the repeatability of results.
2. Section: “Dataset” is poorly described in terms of details, proper citation, and availability of the dataset. None of the data source details is available for in-situ as well as SAR.
3. A well-structured “Methodology” section is missing. The authors have provided the details only about “RVI” and “Water Cloud Model” but did not explain how the analysis has been carried out and how the different analyses have been performed. It creates difficulty in understanding the results.
4. Figures, especially spatial maps, are just presented without much interpretation. The results lack justifications and discussions.
5. The major objective of this manuscript is the use of microwave remote sensing derived RVI for better estimation of soil moisture as compared to the optical remote sensing-based vegetation characteristic. However, none of this manuscript's analyses shows how using RVI improves soil moisture estimation. I suggest providing a comparative analysis using microwave and optical remote sensing-based vegetation attributes.
6. The study is based on only a one-day analysis (7 Aug 2019) which I feel is a major drawback of this manuscript. How can we justify the accuracy/significance of the derived spatial pattern of soil moisture in reference to only one-day soil wetness and vegetation conditions? Since there is good temporal coverage of the SAR dataset (GF3 revisit period is 2-3 days) and good availability of in-situ soil moisture measurements, I suggest providing soil moisture retrievals maps for different wetness conditions and conducting a performance analysis of the retrievals using in-situ observations.
7. I am also in confusion - “How did the author perform the validation using only one-day soil moisture retrievals and corresponding in-situ soil moisture observations. How did the author create the time series based on 1-day information? If authors validated soil moisture in space in place of time-series then it should be clearly mentioned with proper assumptions and procedure.
8. Figure 5 caption is “The spatial distribution of the retrieval SM”. However, the figure shows the scatter plot. How scatter plot can show the spatial distribution?
Other Comments:
1. L49:50- “The surface backscattering coefficient obtained by SAR is directly related to the surface dielectric constant, …” – What are the sources of surface dielectric constant? Is it only soil surface or vegetation also? If both (surface and vegetation) are sources then this can effectively extract both SM and vegetation information.
2. GF-3- please explain the abbreviation “GF-3”. , GF3 has not been explained anywhere in the manuscript.
3. The author used both terminologies “Soil moisture (SM)” and “Soil water content (SWC)” in this manuscript. It makes confusion in reading especially in the case of abbreviations. I suggest using one of them.
4. Figure 1. The unit of the elevation is missing from the map.
5. L94-95 “ … data used in the study from the automated SM monitoring data provided by the local weather bureau”- the sentence is incomplete and confusing. Please rewrite.
6. L106: “Improve the rapid response…”- It’s not clear who improves the rapid response?
7. L109: Define “SLC”-The readers might not be aware of this term.
8. L110-111: “The GF-3 data was obtained on 7 August 2019”- I am wondering, did the authors use only a one-day dataset for this study?
9. L164: What is meant by SVM. It is hard to understand without its explanation at least once in the whole document.
10. L164: “…found that they have no evident consistency”-what does it mean? Please explain properaly.
11. L166: “Figure 2: The space distribution of SVM and RVI”- the caption of the figure is not informative. Besides what is meant by SVM? Provide the unit of both of the variables in their legends. Check how to present unit for non-dimensional variables.
I also suggest explaining Figure 2 to provide a better understanding of the spatial patterns of both of the variables. Also, try to compare both of the spatial plots for better interpretation.
12. L179: “…high and low values of backscattering coefficient…”- Provide the range of high and low backscatter for better interpretation.
13. L186: Figure 3. “The space distribution of σ0 and σ0soil - what is meant by σ0 and σ0soil ? Please explain these. I also suggest making the caption more informative. Legends and their titles are not clearly visible. Besides, I recommend providing a common legend for all the given maps rather than providing a different one for each sub-maps. This will be helpful to better understand the spatial pattern of backscatter under different polarizations and will lead to a solid interpretation.
14. Fig.4: Scatter plots: It’s not clear for what period is this comparison?
15. L208: SVM is estimated using HH/VV polarization…”- I feel this was well proven earlier that HH and VV has a direct relation with soil moisture, so what is the new finding here?
16. L219: “RVI …brought into the WCM …”- The major objective of this manuscript was on RVI but none of the analyses of this manuscript shows how RVI improves the soil moisture estimation.
17. None of the details is provided on how the ubRMSE, bias, and RMSE are calculated?
18. The Y axis of Fig.4 and 5 is wrongly written “Estimation soil moisture…”; it should be “Estimated soil moisture…”.
Reference:
Mandal et al., 2020. Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data, Remote Sensing of Environment 247, 111954.
Reviewer 3 Report
Luo et al., presented a work on the retrieval of soil moisture over vegetated areas using SAR data and radar vegetation Index. The use of RVI for soil moisture estimation is as such not quite new and a lot of research has already been put into this. While there is no issue with the methodology adopted in the study, the validation of the retrieval model developed is not sufficient. I feel the field observation dataset is too small, though the author mentions that about 20 SM observations are available in the study site, I could only see about eight observation points has been used for validating the retrieval model. The author mentions that about 75% of the data are used to fit the model and the 25% remaining data is used to validate the model, however the lines 195-197 contradicts this statement. Figure 4 and Figure 5 show the same plot with different figure titles.
Additional information has to be provided about the SM observations; its variability among the said 20 observations etc. The soil and crop conditions etc. Few information about the study area including areal extent etc. should be provided
English Language needs to be improved at many places.
