Formation and Evolution of Soil Salinization in Shouguang City Based on PMS and OLI/TM Sensors
Round 1
Reviewer 1 Report
This is a very interesting article about the prediction and monitoring of surface soil salinity from remote sensing images. The methods are innovative and conclusions very relevant, but the methods are not well described and should be improved. Some comments and suggestions are:
210, Table 1 and Table 4 – It is confusing that is meant by “PMS” and “OLI” images, because Table 1 describes them as imagery from satellite GF-1, but later in the text something else is understood (e.g. 269-270).
213 – check the consistence of the variable names. E.g SI is the “salt index” and “salinity index” in line 199.
219-223 – There is probably an error in the manuscript as these lines are repeated. Probably is missing a paragraph referring to (6).
198-238 - In the equations the same symbols are used with different meanings. For instance, M0 and M1 are used with completely different meanings in equations (1) and (7) , and (S) is the soil salt content (g/kg) in (1), while it refers to a non-dimensional sequence (!) in (6). I advise to correct these so that equations present unequivocal symbols!
242 - the “average predicted accuracy” is the root mean square error? Please provide the formula how this is calculated, including the degrees of freedom used. This could substitute eq (7) which refers to partial calculation. Do you really need to present a value with 9 decimals?
Section 3.2 - the text here presents regression results. I suggest that it is placed in the “results” section. Also the text presented under 3.1 is belongs to “material and methods”.
274-276 – are you referring to the regression models presented in Figure 2 and figure 3? It would be helpful to be clearer here and refer exactly what is being used.
303 –correct the title of the section.
319 – there is some confusion in the description of figure 6. Does the second image refers to 2007 or to 2017?
Tables 7 and 8, it does not make sense to present the total area for each category for each groundwater depth. It would be easier to interpret if there was a percentage of the total area for each salinization category instead.
440 – it is desirable to present here quantitative results of the prediction of soil salinization for both cases!
Author Response
Point 1:210, Table 1 and Table 4 – It is confusing that is meant by “PMS” and “OLI” images, because Table 1 describes them as imagery from satellite GF-1, but later in the text something else is understood (e.g. 269-270).
Response 1: We have already modified the Table 1 from Line 154.
Table 1. Remote sensing data source
satellite type | sensor | date acquired | spatial resolution/m |
GF-1 | PMS | 2017.5.28 | 8(XS)/2(P) |
LANDSAT | OLI | 2017.5.25 | 30 |
TM | 2007.5.14 | 30 | |
TM | 1998.4.3 | 30 |
Point 2:213 – check the consistence of the variable names. E.g SI is the “salt index” and “salinity index” in line 199.
Response 2:SI is the “salinity index” according to the Reference [22].
Point 3:219-223 – There is probably an error in the manuscript as these lines are repeated. Probably is missing a paragraph referring to (6).
Response 3:219-223 lines have been deleted. We have already added some sentences："Where NDVImin is the minimum value of NDVI, NDVImax is the maximum value of NDVI, N is the normalized value of NDVI, SImin is the minimum value of SI, SImax is the maximum value of SI, S is the normalized value of SI.
Calculating SDI value of soil salinization monitoring index using remote sensing."
Point 4:198-238 - In the equations the same symbols are used with different meanings. For instance, M0 and M1 are used with completely different meanings in equations (1) and (7) , and (S) is the soil salt content (g/kg) in (1), while it refers to a non-dimensional sequence (!) in (6). I advise to correct these so that equations present unequivocal symbols!
Response 4: The equations (7) has been modified in the paper.
Point 5:242 - the “average predicted accuracy” is the root mean square error? Please provide the formula how this is calculated, including the degrees of freedom used. This could substitute eq (7) which refers to partial calculation. Do you really need to present a value with 9 decimals?
Response 5:We used the equations (7) to calculated the predicted accuracy, because the amount of sample is not large enough to use the root mean square error. The value has been modified with 2 decimals.
Point 6:Section 3.2 - the text here presents regression results. I suggest that it is placed in the “results” section. Also the text presented under 3.1 is belongs to “material and methods”.
Response 6:The article structure has been modified in the paper.
Point 7:274-276 – are you referring to the regression models presented in Figure 2 and figure 3? It would be helpful to be clearer here and refer exactly what is being used.
Response 7: We are referring to the regression model presented in Figure 2. We have already added some sentences：" (see the the regression model in Figure 2)".
Point 8:303 –correct the title of the section.
Response 8:The title of the section has been modified in the paper.
Point 9:319 – there is some confusion in the description of figure 6. Does the second image refers to 2007 or to 2017?
Response 9:We analyzed the area change from 1998 to 2017 in accordance with figure 5 and figure 6. The article has been modified in the paper.
Point 10:Tables 7 and 8, it does not make sense to present the total area for each category for each groundwater depth. It would be easier to interpret if there was a percentage of the total area for each salinization category instead.
Response 10:We have already modified the Table 7 and 8.The figures have been modified in the paper.
We present a value with 6 decimals because percentage values are too small
Table 7. Statistical Table of Groundwater Level and Salinized Soil in 2017
groundwater level（m） | Non- salinization（%） | Mild salinization（%） | Moderate salinization（%） | Severe salinization（%） | Saline soil（%） |
-35～-25 | 0.170769 | 0.130500 | 0.033391 | 0.000005 | 0.000001 |
-25～-20 | 1.538693 | 1.075309 | 0.244264 | 0.000018 | 0.000003 |
-20～-15 | 4.591855 | 4.536966 | 1.008100 | 0.000131 | 0.000007 |
-15～-10 | 9.920527 | 12.583344 | 6.783124 | 0.000128 | 0.000025 |
-10～-3.5 | 0.356635 | 0.602779 | 0.379338 | 0.000432 | 0.000124 |
-3.5～0 | 6.067667 | 18.934436 | 29.185629 | 1.641038 | 0.075199 |
0～5 | 0.134818 | 0.004716 | 0.000021 | 0.000005 | 0.000003 |
%: a percentage of the total area for each salinization category
Table 8. Statistical Table of Groundwater Level and Salinized Soil in 1998
groundwater level（m） | Non- salinization（%） | Mild salinization（%） | Moderate salinization（%） | Severe salinization（%） |
-15~-10 | 0.000132 | 3.527277 | 8.839041 | 0.001848 |
-10~-3.5 | 0.073651 | 19.059474 | 35.132371 | 0.151249 |
-3.5~0 | 0.004092 | 5.022902 | 24.463030 | 0.570153 |
0~5 | 0 | 1.339585 | 0.955767 | 0 |
5~10 | 0.001056 | 0.569287 | 0.214041 | 0 |
10~15 | 0.000792 | 0.025870 | 0.048382 | 0 |
%: a percentage of the total area for each salinization category
Point 11:440 – it is desirable to present here quantitative results of the prediction of soil salinization for both cases!
Response 11:Thank you very much for your suggestions. We will pay more attention to such these issues in the future research.
Author Response File: 
Author Response.doc
Reviewer 2 Report
Abstract gives a idea of the manuscript content and the extension is adequate, but not presents any data. Most Keywords are adequate. The references are not numerous although they are current. Theme is interesting and there are relatively few studies related to the topic. Manuscript contains valuable data that deserve to be published. Nevertheless, the paper can be improved. The paper can be published with minor corrections.
Study area is large enough so that there are sensitive climatic differences, but the authors only provide average precipitation data and average ET for the whole area. Are not meteorological data available?.
Discussion is poorly expressed, is something confusing and insufficient. E.g. authors mention "saline soils" as a different class of "mild salinization, moderate salinization and severe salinization". What do the authors understand by "saline soils"?. It is limited to exposing the results without entering the bottom of the matter.
Figures must be improved. Often the different tones of the legend are not distinguished. They should use tones or colors with greater difference.
Frequently, it uses percentages that do not appear in any table. All the values of the percentages must appear in the table. Otherwise it is very difficult to understand what the authors say.
Superposition of the salinity class map and depth map of the groundwater level at two different times (years 1998 and 2017), is difficult to see only with tables 7 and 8.
Authors mention the effect of leaching, but they do not demonstrate it. On the other hand, has this effect not happened in the rest of the study area?. Why do you only mention in southern part of the study area?.
I have included comments in a attached pdf file that the authors should see and attend to the comments that are mentioned.
Comments for author File: 
Comments.pdf
Author Response
Point 1:Study area is large enough so that there are sensitive climatic differences, but the authors only provide average precipitation data and average ET for the whole area. Are not meteorological data available?.
Response 1:We have added the sentences “The annual average temperature is 12.9℃, the monthly average temperature is the highest in July (26.6℃), the lowest in January (-2.8℃), the extreme maximum temperature is 42.5℃, the extreme minimum temperature is -22.3℃, the annual average sunshine duration is 2 444.4 h, the annual average evaporation is 1 834.0 mm, and the frost-free period is 202 days.”
Point 2:Discussion is poorly expressed, is something confusing and insufficient. E.g. authors mention "saline soils" as a different class of "mild salinization, moderate salinization and severe salinization". What do the authors understand by "saline soils"?. It is limited to exposing the results without entering the bottom of the matter.
Response 2:Saline soil refers to solonchak and alkali soil .According to the different degree of salinization, the grade of saline soil is classified, such as mild salinization, moderate salinization and severe salinization.
Point 3:Figures must be improved. Often the different tones of the legend are not distinguished. They should use tones or colors with greater difference.
Response 3:We used gradient color to represent the continuous change of the same phenomenon, so we use the same tone with different saturation.
Point 4:Frequently, it uses percentages that do not appear in any table. All the values of the percentages must appear in the table. Otherwise it is very difficult to understand what the authors say.Superposition of the salinity class map and depth map of the groundwater level at two different times (years 1998 and 2017), is difficult to see only with tables 7 and 8.
Response 4:We have already modified the Table 7 and 8.The figures have been modified in the paper. We have used a percentage of the total area for each salinization category instead. We present a value with 6 decimals because percentage values are too small.
Point 5:other hand, has this effect not happened in the rest of the study area?. Why do you only mention in southern part of the study area?.
Response 5:The leaching in this region helped to reduce soil salinity. For the future, quantitative study of leaching is one of our research directions. Thank you very much for your suggestions. We will pay more attention to such these issues in the future research.
Author Response File: Author Response.pdf
Reviewer 3 Report
The present paper describes the distribution analysis of saline soil by using satellite image data. Distribution of saline soil is well mapped by using PMS data. Transition of soil salinity was shown by using TM data. The present investigation presents important results; however, authors should make revision because of some uncertainties in the text.
1. Lines 135-136: Soil types should follow USDA soil taxonomy or FAO UNESCO soil map.
2. Lines 267-270: Although the correspondence between PMS and OLI is clear, correspondence between OLI and TM is not shown. Although these are sensors on LANDSAT, description on the correspondence between OLI and TM should be included.
3. Lines 279-281: Authors should show the reason of categorization of soil salinity. The standard shown in Table 4 is based on SDI but it seems to be arbitrary scale. Authors should describe the reason why different standards are used between PMS and OLI. Classification of soil salinity based on soil science or relating to crop productivity is recommended.
4. Figures 4 & 5: The correspondence between the two figures is not clear. If authors use the exact category of soil salinity, only Figure 5 will be enough. Authors should describe the difference between these figures more exactly.
5. Figure 6: The soil salinization distribution maps shown in the figure is based on OLI image, however data in 1998 and 2007 are based on TM image.
6. Figure 7: Data source of groundwater level should be clarified.
Author Response
Point 1: Lines 135-136: Soil types should follow USDA soil taxonomy or FAO UNESCO soil map.
Response 1: We used the Chinese soil taxonomy (GB-17296-2009). This is conducive to the effective use of pre-data.
Point 2:Lines 267-270: Although the correspondence between PMS and OLI is clear, correspondence between OLI and TM is not shown. Although these are sensors on LANDSAT, description on the correspondence between OLI and TM should be included.
Response 2:We have already added some sentences："OLI and TM are sensors on LANDSAT. The reflectivity of typical objects is roughly the same in the visible and near-infrared range. So we can also use TM images to extract soil salinity." from line 311.
Point 3:Lines 279-281: Authors should show the reason of categorization of soil salinity. The standard shown in Table 4 is based on SDI but it seems to be arbitrary scale. Authors should describe the reason why different standards are used between PMS and OLI. Classification of soil salinity based on soil science or relating to crop productivity is recommended.
Response 3: Based on the grading standard in Reference 4, we adjusted the numerical scales according to the measured data and remote sensing images of the study area. Surface soil salinity varies from region to region, and its SDI values in remote sensing images are also different. Therefore, it is impossible to establish a unified SDI grading standard at present. Nevertheless, it is necessary to establish models based on measured data and remote sensing image data to determine the best classification criteria of the study area in the future. Thank you very much for your suggestions! We will pay more attention to such these issues in the future research.
[4]Wang Fei;Ding Jianli;Wu Manchun. Remote sensing monitoring models of soil salinization based on NDVI-SI feature space.Transactions of the CSAE, 2010.26(8):168-173.
Point 4: Figures 4 & 5: The correspondence between the two figures is not clear. If authors use the exact category of soil salinity, only Figure 5 will be enough. Authors should describe the difference between these figures more exactly.
Response 4:Fig. 4 is the intermediate result of remote sensing inversion of soil salinity. Figure 5 is the result of reclassification of .
Point 5:Figure 6: The soil salinization distribution maps shown in the figure is based on OLI image, however data in 1998 and 2007 are based on TM image.
Response 5: We analyzed the spatial and temporal patterns of soil salinization in Shouguang in 1998 ,2007 and 2017. PMS image can offer more accurate soil salinization information, so we use the PMS image in 2017. TM images are available images in 1998 and 2007. Because both GF-1 remote sensing satellite(PMS) and Landsat-8(OLI) were successfully launched in 2013. OLI and TM are sensors on LANDSAT. The reflectivity of typical objects is roughly the same in the visible and near-infrared range. So we used TM images to extract soil salinity in 1998 and 2007.
Point 6: Figure 7: Data source of groundwater level should be clarified.
Response 6: Groundwater data are obtained from measured data of wells. We have 14 observation points in the study area.
Author Response File: Author Response.doc
Round 2
Reviewer 1 Report
Dear authors,
most of the previous comments were not adressed or discussed. I send again the list of those comments and strongly encourage you to use them to improve you manuscript:
213 – check the consistence of the variable names. E.g SI is the “salt index” and “salinity index” in line 199.
This was not corrected! Please correct the name of the variable in line 253.
198-238 - In the equations the same symbols are used with different meanings. For instance, M0 and M1 are used with completely different meanings in equations (1) and (7) , and (S) is the soil salt content (g/kg) in (1), while it refers to a non-dimensional sequence (!) in (6). I advise to correct these so that equations present unequivocal symbols!
This was not completely corrected! S appears in equations (1), (5) and (6), is it always the same variable you are referring to?
242 - the “average predicted accuracy” is the root mean square error? Please provide the formula how this is calculated, including the degrees of freedom used. This could substitute eq (7) which refers to partial calculation. Do you really need to present a value with 9 decimals?
(7) is not a correct for calculating the prediction accuracy of a set of observations! Please review the statistics about prediction indicators and present the prediction accuracy accordingly.
303 –correct the title of the section.
This title is still not understandable in English!
319 – there is some confusion in the description of figure 6. Does the second image refers to 2007 or to 2017?
This was not corrected. Figure 6 plots the dates 1998 and 2007, but the text describes changes between 1998 and 2017!
Tables 7 and 8, it does not make sense to present the total area for each category for each groundwater depth. It would be easier to interpret if there was a percentage of the total area for each salinization category instead.
You have to be scientific choosing the methods and presenting the results. If the percentage is not proper for presenting the results, choose another indicator as the permille.
440 – it is desirable to present here quantitative results of the prediction of soil salinization for both cases!
The authors have chosen to ignore this comment. The comment refers directly to what is promised in the Introduction, that quantitative soil salinity will be provided in this study with the used methods. Although these results are obtained, nothing is referred in the conclusions.
Author Response
Point 1:213 – check the consistence of the variable names. E.g SI is the “salt index” and “salinity index” in line 199.This was not corrected! Please correct the name of the variable in line 253.
Response 1: The title of the section has been revised to “ 2.4 SDI” and SI is all defined as the “salinity index”.We have already added some sentences："SDI is a remote sensing synthesis index model for monitoring of soil salinization based on NDVI-SI.” in line 200.
Point 2:198-238 - In the equations the same symbols are used with different meanings. For instance, M0 and M1 are used with completely different meanings in equations (1) and (7) , and (S) is the soil salt content (g/kg) in (1), while it refers to a non-dimensional sequence (!) in (6). I advise to correct these so that equations present unequivocal symbols!
This was not completely corrected! S appears in equations (1), (5) and (6), is it always the same variable you are referring to?
Response 2: The equations (1) has been modified in the paper.
Point 3:242 - the “average predicted accuracy” is the root mean square error? Please provide the formula how this is calculated, including the degrees of freedom used. This could substitute eq (7) which refers to partial calculation. Do you really need to present a value with 9 decimals?
(7) is not a correct for calculating the prediction accuracy of a set of observations! Please review the statistics about prediction indicators and present the prediction accuracy accordingly.
Response 3:The equations (7) and Table 3 have been modified in the paper.
We have already added some sentences：“Where R is the root mean square error, Xobs,i is the measured surface soil salinity, Xmodel,i is the predicted surface soil salinity, and n is 20.”
“The RMSE value is 2.373. In T-test, a = 0.02, T (19) = 2.539, because 2.373 < 2.539, the accuracy is within acceptable range.”
Point 4:303 –correct the title of the section.This title is still not understandable in English!
Response 4:The title of the section has been modified “Dynamic Monitoring of Soil Salinization ”.
Point 5:319 – there is some confusion in the description of figure 6. Does the second image refers to 2007 or to 2017?
This was not corrected. Figure 6 plots the dates 1998 and 2007, but the text describes changes between 1998 and 2017!
Response 5: We extracted the information of soil salinization from 1998 to 2007 in Shouguang from the Landsat image (Figure 6) and extracted the information of soil salinization in 2017 from the PMS image (Figure 5). From there, we analyzed the spatial and temporal patterns of soil salinization in Shouguang from 1998 to 2017.
The article has been modified in the paper.
Point 6:Tables 7 and 8, it does not make sense to present the total area for each category for each groundwater depth. It would be easier to interpret if there was a percentage of the total area for each salinization category instead.
You have to be scientific choosing the methods and presenting the results. If the percentage is not proper for presenting the results, choose another indicator as the permille.
Response 6: This is modified Figure 7 with the per mille.
groundwater level（m） | Non- salinization（‰） | Mild salinization（‰） | Moderate salinization（‰） | Severe salinization（‰） | Solonchak（‰） |
-35～-25 | 1.70769 | 1.305 | 0.33391 | 0.00005 | 0.00001 |
-25～-20 | 15.38693 | 10.75309 | 2.44264 | 0.00018 | 0.00003 |
-20～-15 | 45.91855 | 45.36966 | 10.081 | 0.00131 | 0.00007 |
-15～-10 | 99.20527 | 125.83344 | 67.83124 | 0.00128 | 0.00025 |
-10～-3.5 | 3.56635 | 6.02779 | 3.79338 | 0.00432 | 0.00124 |
-3.5～0 | 60.67667 | 189.34436 | 291.85629 | 16.41038 | 0.75199 |
0～5 | 1.34818 | 0.04716 | 0.00021 | 0.00005 | 0.00003 |
We also modified the article: “The areas with very low ground water level (-35m~-15m) are dominated by non-saline soil and mild saline soil, which account for more than 120.44‰ of the total area. The proportion of moderately salinized soil in areas with low ground water level (-15m~0m) gradually increased in this order: 71.62462‰ (-15m ~ -3.5m) and 291.85629‰ (-3.5m ~0m). In this region, the proportion of mildly salinized soil remained high, while non-salinized soil showed a decreasing trend, from about 100‰ to 60‰. ”
But we do not think the permille is proper for presenting the results. These very small figures are not the main aspect of our study. We expressed these small values as 0 and illustrated them below the Figure 7.The article has been modified in the paper. We have already modified the Table 7 and 8.
Table 7. Statistical Table of Groundwater Level and Salinized Soil in 2017
groundwater level（m） | Non- salinization（%） | Mild salinization（%） | Moderate salinization（%） | Severe salinization（%） | Solonchak（%） |
-35～-25 | 0.171 | 0.130 | 0.033 | 0.000* | 0.000* |
-25～-20 | 1.539 | 1.075 | 0.244 | 0.000* | 0.000* |
-20～-15 | 4.592 | 4.537 | 1.008 | 0.000* | 0.000* |
-15～-10 | 9.920 | 12.583 | 6.783 | 0.000* | 0.000* |
-10～-3.5 | 0.357 | 0.603 | 0.379 | 0.000* | 0.000* |
-3.5～0 | 6.068 | 18.934 | 29.186 | 1.641 | 0.075 |
0～5 | 0.135 | 0.005 | 0.000* | 0.000* | 0.000* |
%: a percentage of the total area for each salinization category
*: value less than 0.001%
Table 8. Statistical Table of Groundwater Level and Salinized Soil in 1998
groundwater level（m） | Non- salinization（%） | Mild salinization（%） | Moderate salinization（%） | Severe salinization（%） |
-15~-10 | 0.000* | 3.527 | 8.839 | 0.002 |
-10~-3.5 | 0.074 | 19.060 | 35.132 | 0.151 |
-3.5~0 | 0.004 | 5.023 | 24.463 | 0.570 |
0~5 | 0 | 1.340 | 0.956 | 0 |
5~10 | 0.001 | 0.569 | 0.214 | 0 |
10~15 | 0.001 | 0.026 | 0.048 | 0 |
%: a percentage of the total area for each salinization category
*: value less than 0.001%
Point 7:440 – it is desirable to present here quantitative results of the prediction of soil salinization for both cases!The authors have chosen to ignore this comment. The comment refers directly to what is promised in the Introduction, that quantitative soil salinity will be provided in this study with the used methods. Although these results are obtained, nothing is referred in the conclusions.
Response 7 : We have modified Figure 4, and added some sentences：
"According to equations (6), the SDI values of all pixels in the PMS image are calculated(Figure 4-SDI), and the maximum SDI in the study area is 1.291 and the minimum is 0.754. Based on the established remote sensing monitoring model of soil salinization from PMS images (see the the regression model in Figure 2), we performed the inversion of soil salinity using the PMS images of Shouguang in 2017(Figure 4-SALT). It showed that the maximum and minimum values of surface soil salinity in the study area are 158.77g/kg and 35.359g/kg respectively.”
SDI SALT
Figure 4. SDI and Retrieval of Soil Salt Content from PMS Images in Northern Shouguang in 2017
“According to the grading standard of saline soil, the soils in Shouguang were be categorized into five grades (i.e., non-salinization, mild salinization, moderate salinization, severe salinization and solonchak).Because of the strong correlation between SDI and the measured soil salinity, SDI can be used to classify the soil salinity, thus the distribution area of different grades of soil salinity in the study area were obtained. Based on the Salinization classification standard of PMS (Table 4.), we classified the SDI image(Figure 4-SDI) into 5 grades(Figure 5). Table 5 demonstrates the quantitative features of surface soils of Shouguang which were categorized using the grading standard of saline soil.”
we also have modified the conclusions: “（2）The total area of saline soil decreased from 1998 (681.323km2) to 2017 (526.532km2) with a reduction rate of 22.719%. There was a significant drop in the moderately salinized soil area from 474.938km2 to 256.613km2 in the past 20 years (a reduction rate of 45.969%), whereas the area of mildly salinized soil increased slightly to 258.210km2. The area of severely salinized soil and solonchak was smaller in the study area, it gradually increased .
（3）The distribution of soil salinization in Shouguang presented obvious characteristics of strip distribution. The change of soil salinization from the south to the north followed the order of: non-salinization, mild salinization, moderate salinization, severe salinization, salinized soil. In particular, the most serious areas of salinization were mostly distributed around the coastline. From 1998 to 2017, the coastal area of severe soil salinization has increased, while the inland area of mild soil salinization has also expanded.”
Author Response File: Author Response.doc
Reviewer 2 Report
Authors have responded satisfactorily to almost all the observations made to them. I have only observed the following few minor corrections that should be made:
1) It seems to me correct to use the term Solonchak of the WRB classification, but it should do the same with the soils of the Chinese classification. Chinese taxonomy is unknown to most readers. Use WRB or Soil Taxonomy.
2) Line 521: “Changes Characteristics of …” -- > change by “Characteristic Changes of…”
3) Line 632: 2 decimals only in the mentioned percentages
Author Response
Point 1: It seems to me correct to use the term Solonchak of the WRB classification, but it should do the same with the soils of the Chinese classification. Chinese taxonomy is unknown to most readers. Use WRB or Soil Taxonomy.
Response 1: We used the USDA soil taxonomy. The article has been modified in the paper: “The soils in Shouguang are characterized by Eutrochrepts, Hapludalf, Aquents , Calciaquert and Haplaguept (according to USDA soil taxonomy).”
Point 2: Line 521: “Changes Characteristics of …” -- > change by “Characteristic Changes of…”
Response 2:The title of the section has been modified “Dynamic Monitoring of Soil Salinization ”.
Point 3: Line 632: 2 decimals only in the mentioned percentages
Response 3:We have already modified the Table 7 and 8. We present a value with 3 decimals because percentage values are too small. These very small figures are not the main aspect of our study. We expressed these small values as 0 and illustrated them below the Figure 7.The article has been modified in the paper.
Table 7. Statistical Table of Groundwater Level and Salinized Soil in 2017
groundwater level（m） | Non- salinization（%） | Mild salinization（%） | Moderate salinization（%） | Severe salinization（%） | Solonchak（%） |
-35～-25 | 0.171 | 0.130 | 0.033 | 0.000* | 0.000* |
-25～-20 | 1.539 | 1.075 | 0.244 | 0.000* | 0.000* |
-20～-15 | 4.592 | 4.537 | 1.008 | 0.000* | 0.000* |
-15～-10 | 9.920 | 12.583 | 6.783 | 0.000* | 0.000* |
-10～-3.5 | 0.357 | 0.603 | 0.379 | 0.000* | 0.000* |
-3.5～0 | 6.068 | 18.934 | 29.186 | 1.641 | 0.075 |
0～5 | 0.135 | 0.005 | 0.000* | 0.000* | 0.000* |
%: a percentage of the total area for each salinization category
*: value less than 0.001%
Table 8. Statistical Table of Groundwater Level and Salinized Soil in 1998
groundwater level（m） | Non- salinization（%） | Mild salinization（%） | Moderate salinization（%） | Severe salinization（%） |
-15~-10 | 0.000* | 3.527 | 8.839 | 0.002 |
-10~-3.5 | 0.074 | 19.060 | 35.132 | 0.151 |
-3.5~0 | 0.004 | 5.023 | 24.463 | 0.570 |
0~5 | 0 | 1.340 | 0.956 | 0 |
5~10 | 0.001 | 0.569 | 0.214 | 0 |
10~15 | 0.001 | 0.026 | 0.048 | 0 |
%: a percentage of the total area for each salinization category
*: value less than 0.001%
Author Response File: Author Response.doc
Reviewer 3 Report
Reply comments by authors of water-425737 are almost appropriate and the manuscript has been properly revised. However, authors are requested to make further revision concerning the following points.
1. I understand the difference between Figure 4 and Figure 5. However, these two figures are based on the same data from PMS Images. If authors present both of the figures, authors should clearly describe what information can be provided from these respective figures; i.e. the difference of information those readers of the manuscript can get. I think that the difference between these figures is the classification of each pixel.
2. As for the data of groundwater level appearing in Figure 7, authors answer that authors measured groundwater level at 14 observation sites. However, description on the method never appears in the manuscript. Authors should describe the method of groundwater measurements. I think that the resolution of the distribution map of groundwater (Figure 7) seems to be quite high compared as the 14 data sampling points. Authors are requested to describe the data analysis method to draw these distribution maps based on the data from 14 sampling points.
Author Response
Point 1: I understand the difference between Figure 4 and Figure 5. However, these two figures are based on the same data from PMS Images. If authors present both of the figures, authors should clearly describe what information can be provided from these respective figures; i.e. the difference of information those readers of the manuscript can get. I think that the difference between these figures is the classification of each pixel.
Response 1: We have modified Figure 4, and added some sentences：
"According to equations (6), the SDI values of all pixels in the PMS image are calculated(Figure 4-SDI), and the maximum SDI in the study area is 1.291 and the minimum is 0.754. Based on the established remote sensing monitoring model of soil salinization from PMS images (see the the regression model in Figure 2), we performed the inversion of soil salinity using the PMS images of Shouguang in 2017(Figure 4-SALT). It showed that the maximum and minimum values of surface soil salinity in the study area are 158.77g/kg and 35.359g/kg respectively.”
SDI SALT
Figure 4. SDI and Retrieval of Soil Salt Content from PMS Images in Northern Shouguang in 2017
“According to the grading standard of saline soil, the soils in Shouguang were be categorized into five grades (i.e., non-salinization, mild salinization, moderate salinization, severe salinization and solonchak).Because of the strong correlation between SDI and the measured soil salinity, SDI can be used to classify the soil salinity, thus the distribution area of different grades of soil salinity in the study area were obtained. Based on the Salinization classification standard of PMS (Table 4.), we classified the SDI image(Figure 4-SDI) into 5 grades(Figure 5). Table 5 demonstrates the quantitative features of surface soils of Shouguang which were categorized using the grading standard of saline soil.”
Point 2: As for the data of groundwater level appearing in Figure 7, authors answer that authors measured groundwater level at 14 observation sites. However, description on the method never appears in the manuscript. Authors should describe the method of groundwater measurements. I think that the resolution of the distribution map of groundwater (Figure 7) seems to be quite high compared as the 14 data sampling points. Authors are requested to describe the data analysis method to draw these distribution maps based on the data from 14 sampling points.
Response 2: We have modified some sentences：
“Groundwater data are obtained from measured data of 14 observation points in the study area by wells (available in 1998, 2007 and April 2017). To evaluate the evolution mechanism of surface soil salinization in the area from 1998 to 2017, the distribution map of groundwater level (figure 7) was got by the Natural Neighbor of spatial interpolation analysis based on the data of sampling points.”
This is the measured data of 14 observation points.
WellName | 1998/4 | 2007/4 | 2017/4 |
6 | -12.02 | -11.9 | -12.52 |
48 | 26.11 | -8.69 | |
88 | 3.46 | 3.05 | 1.43 |
155 | 4.48 | -1.27 | |
174 | -1.95 | -12.67 | |
177 | 14.48 | 17.69 | |
179 | 6.41 | 9.72 | |
182 | 0.11 | -2.23 | |
185 | -3.21 | -8.83 | -8.14 |
188 | 12.71 | 2.06 | |
110A | 37.7 | 46.1 | |
138A | -10.34 | -4.1 | |
151B | 25.32 | 27.3 | 9.68 |
156A | -9.85 | -12.29 | -13.99 |
Author Response File: Author Response.doc
