Jiangsu Provincial Key Laboratory of Resources and Environment Information Engineering

Tongshan, China

Jiangsu Provincial Key Laboratory of Resources and Environment Information Engineering

Tongshan, China

Time filter

Source Type

Lin L.,China University of Mining and Technology | Lin L.,Key Laboratory of Land Environment and Disaster Monitoring | Lin L.,Jiangsu Provincial Key Laboratory of Resources and Environment Information Engineering | Wang Y.,China University of Mining and Technology | And 6 more authors.
Environmental Monitoring and Assessment | Year: 2016

Hyperspectral estimation of soil organic matter (SOM) in coal mining regions is an important tool for enhancing fertilization in soil restoration programs. The correlation—partial least squares regression (PLSR) method effectively solves the information loss problem of correlation—multiple linear stepwise regression, but results of the correlation analysis must be optimized to improve precision. This study considers the relationship between spectral reflectance and SOM based on spectral reflectance curves of soil samples collected from coal mining regions. Based on the major absorption troughs in the 400–1006 nm spectral range, PLSR analysis was performed using 289 independent bands of the second derivative (SDR) with three levels and measured SOM values. A wavelet-correlation-PLSR (W-C-PLSR) model was then constructed. By amplifying useful information that was previously obscured by noise, the W-C-PLSR model was optimal for estimating SOM content, with smaller prediction errors in both calibration (R2 = 0.970, root mean square error (RMSEC) = 3.10, and mean relative error (MREC) = 8.75) and validation (RMSEV = 5.85 and MREV = 14.32) analyses, as compared with other models. Results indicate that W-C-PLSR has great potential to estimate SOM in coal mining regions. © 2016, Springer International Publishing Switzerland.


Lin L.,China University of Mining and Technology | Lin L.,Key Laboratory of Land Environment and Disaster Monitoring | Lin L.,Jiangsu Provincial Key Laboratory of Resources and Environment Information Engineering | Wang Y.,China University of Mining and Technology | And 4 more authors.
Environmental monitoring and assessment | Year: 2016

Hyperspectral estimation of soil organic matter (SOM) in coal mining regions is an important tool for enhancing fertilization in soil restoration programs. The correlation--partial least squares regression (PLSR) method effectively solves the information loss problem of correlation--multiple linear stepwise regression, but results of the correlation analysis must be optimized to improve precision. This study considers the relationship between spectral reflectance and SOM based on spectral reflectance curves of soil samples collected from coal mining regions. Based on the major absorption troughs in the 400-1006 nm spectral range, PLSR analysis was performed using 289 independent bands of the second derivative (SDR) with three levels and measured SOM values. A wavelet-correlation-PLSR (W-C-PLSR) model was then constructed. By amplifying useful information that was previously obscured by noise, the W-C-PLSR model was optimal for estimating SOM content, with smaller prediction errors in both calibration (R(2) = 0.970, root mean square error (RMSEC) = 3.10, and mean relative error (MREC) = 8.75) and validation (RMSEV = 5.85 and MREV = 14.32) analyses, as compared with other models. Results indicate that W-C-PLSR has great potential to estimate SOM in coal mining regions.


Lin L.-X.,China University of Mining and Technology | Lin L.-X.,Mapping and Geo Information NASG Key Laboratory of Land Environment and Disaster Monitoring | Lin L.-X.,Jiangsu Provincial Key Laboratory of Resources and Environment Information Engineering | Wang Y.-J.,China University of Mining and Technology | And 5 more authors.
Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis | Year: 2014

Soil available nitrogen content is an important index reflecting soil fertility. It provides dynamic information for land reclamation and ecological restoration if soil available nitrogen can be monitored and evaluated using hyperspectral technology. Facing the study blank of soil available nitrogen in National Mine Park and the deficiency of poor computational efficiency of partial least squares regression (PLSR) method, the present paper presents the relationship between soil spectrum and soil available nitrogen based on spectrum curves (ranging from 350 to 2 500 nm) of 30 salinized chestnut soil samples, which were collected from southern mountain coal waste scenic spot, located in Jinhuagong mine in Datong city, Shanxi Province, China (one part of Jinhuagong national mine park). Soil reflection spectrum was mathematically manipulated into first derivative and inverse-log spectral curves, then a corresponding estimation model was built and examined by PLSR and Enter-partial least squares regression (Enter-PLSR) based on characteristic absorption. The result indicated that Enter-PLSR corresponding estimation model greatly increased the computation efficiency by reducing the number of independent variables to 12 from 122 in case of a close accuracy of PLS corresponding estimation model. By using hyperspectral technology and Enter-PLSR method, the study blank of soil available nitrogen in National Mine Park was filled. At the same time, the computation efficiency problem of PLSR was resolved.

Loading Jiangsu Provincial Key Laboratory of Resources and Environment Information Engineering collaborators
Loading Jiangsu Provincial Key Laboratory of Resources and Environment Information Engineering collaborators