Extraction and Analysis of Soil Salinization Information in an Alar Reclamation Area Based on Spectral Index Modeling
, Xingpeng Wang 2, Mingzhe Li 1, Chengcheng Liu 1, Lianjie Cong 1, Xinyi Qu 3 and Xu Li 1,*Round 1
Reviewer 1 Report
The reseach adds to our knowledge on soil salinization detection and modeling with interesting results.
Author Response
Dear reviewer, thank you for your positive comments
Reviewer 2 Report
Title “Extraction and analysis of soil salinization information in Alar reclamation area based on spectral index modelling”
The authors perform a very interesting study on soil salinity indices in the Alar Reclamation Area, that is located in the arid area of southern Xinjiang. Salt causes variations in the surface roughness, which induces variation in the soil spectral reflectance (Goldshleger et al., 2013).
They used Remote Sensing based soil salinization analysis model based on NDVI-SI feature space, the salinity index 1 (salinity index 1, SI1) and four types of vegetation indices that sensitive to crop growth monitoring were selected, including the Normalized difference vegetation index in green red band (GRNDVI), Normalized vegetation index of greenness (GNDVI), Normalized difference vegetation index (Normalized difference vegetation index, NDVI), difference environmental vegetation index (Difference environmental vegetation index, DVI).Than they combine Sentinel 2 remote sensing image with these indices to build 4 types of remote sensing soil salinities indices, that are S1DI1 (SI1-GRNDVI), S1DI2 (SI1-GNDVI), S1DI3 (SI1-NDVI), SDI4 (SI1-DVI).
According to Elnaggar and Noller (2010) that salt affected soils with salt encrustation at the surface are, generally, smoother than non-saline surfaces and cause high reflectance in the visible and near-infrared bands. Normally, unhealthy vegetation has a lower
photosynthetic activity, causing increased visible reflectance and the reduced near-infrared reflectance (NIR) from the vegetation (E. Weiss, et al., 2001). Furthermore, based on the high correlations between the Normalized Difference Vegetation Index (NDVI) values of cotton, sugarcane crops and the EC, Wiegand et al.1996 successfully assessed the severity and extent of soil salinity in terms of the economic impact on crop production and also distinguished saline soils from non-affected soils. Furthermore, spectral vegetation Indices and salinity Indices change with various natural conditions, soil types , vegetation cover and density.
This study found that the green band, red band, and short-infrared waves have better responses to soil salinization information.
In the table 1 you have to specify the index that you called SI12, for the readers remain unclear how you obtain this index.
After the meaning of SI12 will be clarified, it can be said that the analyses show a large correlation with the four vegetation indexes.
Being SI12 a key component in the realization of the 4 models, it is not possible to publish the manuscript until the authors have clearly explained how they arrived at the realization of this indicator.
Author Response
Dear reviewers:
We would like to thank the reviewers for their comments, which help us to revise and improve the paper and are an important guide for our research. We have carefully studied the comments and made changes, and we hope they will be approved by the reviewers. The revised parts are marked in red in the paper, and we sincerely hope you are satisfied with our responses and changes.
Reviewer 2
Comment : The authors perform a very interesting study on soil salinity indices in the Alar Reclamation Area, that is located in the arid area of southern Xinjiang. Salt causes variations in the surface roughness, which induces variation in the soil spectral reflectance (Goldshleger et al., 2013).
Author's Response: The reviewer's comments are greatly appreciated.
Comment : They used Remote Sensing based soil salinization analysis model based on NDVI-SI feature space, the salinity index 1 (salinity index 1, SI1) and four types of vegetation indices that sensitive to crop growth monitoring were selected, including the Normalized difference vegetation index in green red band (GRNDVI), Normalized vegetation index of greenness (GNDVI), Normalized difference vegetation index (Normalized difference vegetation index, NDVI), difference environmental vegetation index (Difference environmental vegetation index, DVI).Than they combine Sentinel 2 remote sensing image with these indices to build 4 types of remote sensing soil salinities indices, that are S1DI1 (SI1-GRNDVI), S1DI2 (SI1-GNDVI), S1DI3 (SI1-NDVI), SDI4 (SI1-DVI).
Author's Response: The reviewer's comments are greatly appreciated.
Comment :According to Elnaggar and Noller (2010) that salt affected soils with salt encrustation at the surface are, generally, smoother than non-saline surfaces and cause high reflectance in the visible and near-infrared bands. Normally, unhealthy vegetation has a lower
Author's Response: The reviewer's comments are greatly appreciated.
Comment : photosynthetic activity, causing increased visible reflectance and the reduced near-infrared reflectance (NIR) from the vegetation (E. Weiss, et al., 2001). Furthermore, based on the high correlations between the Normalized Difference Vegetation Index (NDVI) values of cotton, sugarcane crops and the EC, Wiegand et al.1996 successfully assessed the severity and extent of soil salinity in terms of the economic impact on crop production and also distinguished saline soils from non-affected soils. Furthermore, spectral vegetation Indices and salinity Indices change with various natural conditions, soil types , vegetation cover and density.
This study found that the green band, red band, and short-infrared waves have better responses to soil salinization information.
Author's Response: The reviewer's comments are greatly appreciated.
Comment : In the table 1 you have to specify the index that you called SI12, for the readers remain unclear how you obtain this index.
Author's Response: The reviewer's comments are greatly appreciated. We have modified the content in the article.
SI1 is the salinity index 1 , not SI12, should be SI12,
|
Spectral index |
Expression |
|
SI1 |
SI1=√(G*R) |
|
GRNDVI |
GRNDVI=(N-R-G)/(N+R+G) |
|
GNDVI |
GNDVI=(N-G)/(N+G) |
|
NDVI |
NDVI=(N-R)/(N+R) |
|
DVI |
DVI=N-R |
|
S1DI1 Model (SI1-GRNDVI) |
S1DI1=√(GRNDVI−1)2+SI12 |
|
S1DI2 Model (SI1-GNDVI) |
S1DI2=√(GNDVI−1)2+SI12 |
|
S1DI3 Model (SI1-NDVI) |
S1DI3=√(NDVI−1)2+SI12 |
|
S1DI4 Model (SI1-DVI) |
S1DI4=√(DVI−1)2+SI12 |
Comment : After the meaning of SI12 will be clarified, it can be said that the analyses show a large correlation with the four vegetation indexes.
Author's Response: The reviewer's comments are greatly appreciated. Yes, the analyses correlation with the four vegetation indexes.
Comment : Being SI12 a key component in the realization of the 4 models, it is not possible to publish the manuscript until the authors have clearly explained how they arrived at the realization of this indicator.
Author's Response: The reviewer's comments are greatly appreciated. We have modified the SI12, is SI12
Reviewer 3 Report
The figure must be in line with the paragraph
Doi needs to be added to the references section
Changes and additions are shown on the pdf
Comments for author File: 
Comments.pdf
Author Response
Dear reviewers:
We would like to thank the reviewers for their comments, which help us to revise and improve the paper and are an important guide for our research. We have carefully studied the comments and made changes, and we hope they will be approved by the reviewers. The revised parts are marked in red in the paper, and we sincerely hope you are satisfied with our responses and changes.
Reviewer 3
Comment : The figure must be in line with the paragraph
Author's Response: The reviewer's comments are greatly appreciated. We have modified it.
Comment : Doi needs to be added to the references section
Author's Response: The reviewer's comments are greatly appreciated. We have added the DOI in reference.
Comment : Changes and additions are shown on the pdf
Author's Response: The reviewer's comments are greatly appreciated.
