Experimental Study on Soil Electrical Conductivity Inversion by Ground Spectral Measurement and SAR Data

Yin Chengshen; Liu Quanming; Wang Chunjuan; Wang Fuqiang

doi:doi:10.11648/j.sd.20210904.22

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Experimental Study on Soil Electrical Conductivity Inversion by Ground Spectral Measurement and SAR Data

Yin Chengshen, Liu Quanming, Wang Chunjuan, Wang Fuqiang

Published in Science Discovery (Volume 9, Issue 4)

Received: 16 July 2021 Accepted: 13 August 2021 Published: 23 August 2021

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Abstract

In this paper, the ground-measured spectral reflectance was combined with C-band microwave radar quadrupolarized backscattering data, and the characteristic bands were selected using partial least squares and correlation coefficient methods, and a model was developed to evaluate the degree of soil salinization. Using the spectral reflectance and its logarithmic, first-order and second-order derivatives of the four spectral data, correlation analysis was performed and found that the first and second order derivatives of the spectra were better correlated compared to the first two. The correlations of soil EC values in the four bands of 1584-1588 nm, 1802-1806 nm, 2201-2205 nm, and 2344-2348 nm transformed by second-order derivatives were 0.27, 0.34, 0.33, and 0.35, respectively, and there existed two bands of 1802-1806 nm and 2344-2348 nm that both had better soil EC correlation. The bands selected by the partial least squares method are more backward than those selected by the correlation coefficient method, and there are extremely sensitive bands, and the fit of the second-order derivative transformation model is better compared with that of the correlation coefficient method. By combining the second-order derivatives of reflectance, surface roughness and radar backscatter coefficients, the neural network model with the second-order derivatives of reflectance and radar backscatter characteristics was the best prediction model, and its R² for soil EC was 0.8666.

Published in	Science Discovery (Volume 9, Issue 4)
DOI	10.11648/j.sd.20210904.22
Page(s)	200-205
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Soil Conductivity, Multi-source Remote Sensing, Collaborative Inversion, Partial Least Squares Method, The Neural Network

References

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APA Style

Yin Chengshen, Liu Quanming, Wang Chunjuan, Wang Fuqiang. (2021). Experimental Study on Soil Electrical Conductivity Inversion by Ground Spectral Measurement and SAR Data. Science Discovery, 9(4), 200-205. https://doi.org/10.11648/j.sd.20210904.22

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ACS Style

Yin Chengshen; Liu Quanming; Wang Chunjuan; Wang Fuqiang. Experimental Study on Soil Electrical Conductivity Inversion by Ground Spectral Measurement and SAR Data. Sci. Discov. 2021, 9(4), 200-205. doi: 10.11648/j.sd.20210904.22

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AMA Style

Yin Chengshen, Liu Quanming, Wang Chunjuan, Wang Fuqiang. Experimental Study on Soil Electrical Conductivity Inversion by Ground Spectral Measurement and SAR Data. Sci Discov. 2021;9(4):200-205. doi: 10.11648/j.sd.20210904.22

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@article{10.11648/j.sd.20210904.22,
  author = {Yin Chengshen and Liu Quanming and Wang Chunjuan and Wang Fuqiang},
  title = {Experimental Study on Soil Electrical Conductivity Inversion by Ground Spectral Measurement and SAR Data},
  journal = {Science Discovery},
  volume = {9},
  number = {4},
  pages = {200-205},
  doi = {10.11648/j.sd.20210904.22},
  url = {https://doi.org/10.11648/j.sd.20210904.22},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20210904.22},
  abstract = {In this paper, the ground-measured spectral reflectance was combined with C-band microwave radar quadrupolarized backscattering data, and the characteristic bands were selected using partial least squares and correlation coefficient methods, and a model was developed to evaluate the degree of soil salinization. Using the spectral reflectance and its logarithmic, first-order and second-order derivatives of the four spectral data, correlation analysis was performed and found that the first and second order derivatives of the spectra were better correlated compared to the first two. The correlations of soil EC values in the four bands of 1584-1588 nm, 1802-1806 nm, 2201-2205 nm, and 2344-2348 nm transformed by second-order derivatives were 0.27, 0.34, 0.33, and 0.35, respectively, and there existed two bands of 1802-1806 nm and 2344-2348 nm that both had better soil EC correlation. The bands selected by the partial least squares method are more backward than those selected by the correlation coefficient method, and there are extremely sensitive bands, and the fit of the second-order derivative transformation model is better compared with that of the correlation coefficient method. By combining the second-order derivatives of reflectance, surface roughness and radar backscatter coefficients, the neural network model with the second-order derivatives of reflectance and radar backscatter characteristics was the best prediction model, and its R2 for soil EC was 0.8666.},
 year = {2021}
}

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TY  - JOUR
T1  - Experimental Study on Soil Electrical Conductivity Inversion by Ground Spectral Measurement and SAR Data
AU  - Yin Chengshen
AU  - Liu Quanming
AU  - Wang Chunjuan
AU  - Wang Fuqiang
Y1  - 2021/08/23
PY  - 2021
N1  - https://doi.org/10.11648/j.sd.20210904.22
DO  - 10.11648/j.sd.20210904.22
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 200
EP  - 205
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20210904.22
AB  - In this paper, the ground-measured spectral reflectance was combined with C-band microwave radar quadrupolarized backscattering data, and the characteristic bands were selected using partial least squares and correlation coefficient methods, and a model was developed to evaluate the degree of soil salinization. Using the spectral reflectance and its logarithmic, first-order and second-order derivatives of the four spectral data, correlation analysis was performed and found that the first and second order derivatives of the spectra were better correlated compared to the first two. The correlations of soil EC values in the four bands of 1584-1588 nm, 1802-1806 nm, 2201-2205 nm, and 2344-2348 nm transformed by second-order derivatives were 0.27, 0.34, 0.33, and 0.35, respectively, and there existed two bands of 1802-1806 nm and 2344-2348 nm that both had better soil EC correlation. The bands selected by the partial least squares method are more backward than those selected by the correlation coefficient method, and there are extremely sensitive bands, and the fit of the second-order derivative transformation model is better compared with that of the correlation coefficient method. By combining the second-order derivatives of reflectance, surface roughness and radar backscatter coefficients, the neural network model with the second-order derivatives of reflectance and radar backscatter characteristics was the best prediction model, and its R2 for soil EC was 0.8666.
VL  - 9
IS  - 4
ER  -

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Author Information

Yin Chengshen

College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot, China
Liu Quanming

College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot, China
Wang Chunjuan

College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot, China
Wang Fuqiang

College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot, China

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Plain Text BibTeX RIS

APA Style

Yin Chengshen, Liu Quanming, Wang Chunjuan, Wang Fuqiang. (2021). Experimental Study on Soil Electrical Conductivity Inversion by Ground Spectral Measurement and SAR Data. Science Discovery, 9(4), 200-205. https://doi.org/10.11648/j.sd.20210904.22

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ACS Style

Yin Chengshen; Liu Quanming; Wang Chunjuan; Wang Fuqiang. Experimental Study on Soil Electrical Conductivity Inversion by Ground Spectral Measurement and SAR Data. Sci. Discov. 2021, 9(4), 200-205. doi: 10.11648/j.sd.20210904.22

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AMA Style

Yin Chengshen, Liu Quanming, Wang Chunjuan, Wang Fuqiang. Experimental Study on Soil Electrical Conductivity Inversion by Ground Spectral Measurement and SAR Data. Sci Discov. 2021;9(4):200-205. doi: 10.11648/j.sd.20210904.22

Copy | Download

@article{10.11648/j.sd.20210904.22,
  author = {Yin Chengshen and Liu Quanming and Wang Chunjuan and Wang Fuqiang},
  title = {Experimental Study on Soil Electrical Conductivity Inversion by Ground Spectral Measurement and SAR Data},
  journal = {Science Discovery},
  volume = {9},
  number = {4},
  pages = {200-205},
  doi = {10.11648/j.sd.20210904.22},
  url = {https://doi.org/10.11648/j.sd.20210904.22},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20210904.22},
  abstract = {In this paper, the ground-measured spectral reflectance was combined with C-band microwave radar quadrupolarized backscattering data, and the characteristic bands were selected using partial least squares and correlation coefficient methods, and a model was developed to evaluate the degree of soil salinization. Using the spectral reflectance and its logarithmic, first-order and second-order derivatives of the four spectral data, correlation analysis was performed and found that the first and second order derivatives of the spectra were better correlated compared to the first two. The correlations of soil EC values in the four bands of 1584-1588 nm, 1802-1806 nm, 2201-2205 nm, and 2344-2348 nm transformed by second-order derivatives were 0.27, 0.34, 0.33, and 0.35, respectively, and there existed two bands of 1802-1806 nm and 2344-2348 nm that both had better soil EC correlation. The bands selected by the partial least squares method are more backward than those selected by the correlation coefficient method, and there are extremely sensitive bands, and the fit of the second-order derivative transformation model is better compared with that of the correlation coefficient method. By combining the second-order derivatives of reflectance, surface roughness and radar backscatter coefficients, the neural network model with the second-order derivatives of reflectance and radar backscatter characteristics was the best prediction model, and its R2 for soil EC was 0.8666.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Experimental Study on Soil Electrical Conductivity Inversion by Ground Spectral Measurement and SAR Data
AU  - Yin Chengshen
AU  - Liu Quanming
AU  - Wang Chunjuan
AU  - Wang Fuqiang
Y1  - 2021/08/23
PY  - 2021
N1  - https://doi.org/10.11648/j.sd.20210904.22
DO  - 10.11648/j.sd.20210904.22
T2  - Science Discovery
JF  - Science Discovery
JO  - Science Discovery
SP  - 200
EP  - 205
PB  - Science Publishing Group
SN  - 2331-0650
UR  - https://doi.org/10.11648/j.sd.20210904.22
AB  - In this paper, the ground-measured spectral reflectance was combined with C-band microwave radar quadrupolarized backscattering data, and the characteristic bands were selected using partial least squares and correlation coefficient methods, and a model was developed to evaluate the degree of soil salinization. Using the spectral reflectance and its logarithmic, first-order and second-order derivatives of the four spectral data, correlation analysis was performed and found that the first and second order derivatives of the spectra were better correlated compared to the first two. The correlations of soil EC values in the four bands of 1584-1588 nm, 1802-1806 nm, 2201-2205 nm, and 2344-2348 nm transformed by second-order derivatives were 0.27, 0.34, 0.33, and 0.35, respectively, and there existed two bands of 1802-1806 nm and 2344-2348 nm that both had better soil EC correlation. The bands selected by the partial least squares method are more backward than those selected by the correlation coefficient method, and there are extremely sensitive bands, and the fit of the second-order derivative transformation model is better compared with that of the correlation coefficient method. By combining the second-order derivatives of reflectance, surface roughness and radar backscatter coefficients, the neural network model with the second-order derivatives of reflectance and radar backscatter characteristics was the best prediction model, and its R2 for soil EC was 0.8666.
VL  - 9
IS  - 4
ER  -

Copy | Download