Feasibility of estimating heavy metal concentrations in water column using hyperspectral data and partial least squares regression

2009 ◽  
Author(s):  
Yiyun Chen ◽  
Yaolin Liu ◽  
Dun Wang ◽  
XueSong Kong ◽  
Chen Zeng
Keyword(s):  
Heavy Metal ◽  
Least Squares ◽  
Water Column ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Hyperspectral Data ◽  
Least Squares Regression ◽  
Metal Concentrations ◽  
Heavy Metal Concentrations

scholarly journals Mapping Soil Alkalinity and Salinity in Northern Songnen Plain, China with the HJ-1 Hyperspectral Imager Data and Partial Least Squares Regression

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3855 ◽  
Author(s):  
Lin Bai ◽  
Cuizhen Wang ◽  
Shuying Zang ◽  
Changshan Wu ◽  
Jinming Luo ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Soil Ph ◽  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Validation Dataset ◽  
Least Squares Regression ◽  
Songnen Plain ◽  
Mean Square Errors

In arid and semi-arid regions, identifying and monitoring of soil alkalinity and salinity are in urgently need for preventing land degradation and maintaining ecological balances. In this study, physicochemical, statistical, and spectral analysis revealed that potential of hydrogen (pH) and electrical conductivity (EC) characterized the saline-alkali soils and were sensitive to the visible and near infrared (VIS-NIR) wavelengths. On the basis of soil pH, EC, and spectral data, the partial least squares regression (PLSR) models for estimating soil alkalinity and salinity were constructed. The R2 values for soil pH and EC models were 0.77 and 0.48, and the root mean square errors (RMSEs) were 0.95 and 17.92 dS/m, respectively. The ratios of performance to inter-quartile distance (RPIQ) for the soil pH and EC models were 3.84 and 0.14, respectively, indicating that the soil pH model performed well but the soil EC model was not considerably reliable. With the validation dataset, the RMSEs of the two models were 1.06 and 18.92 dS/m. With the PLSR models applied to hyperspectral data acquired from the hyperspectral imager (HSI) onboard the HJ-1A satellite (launched in 2008 by China), the soil alkalinity and salinity distributions were mapped in the study area, and were validated with RMSEs of 1.09 and 17.30 dS/m, respectively. These findings revealed that the hyperspectral images in the VIS-NIR wavelengths had the potential to map soil alkalinity and salinity in the Songnen Plain, China.

Brightness-normalized Partial Least Squares Regression for hyperspectral data

2010 ◽  
Vol 111 (12-13) ◽  
pp. 1947-1957 ◽  
Author(s):  
Hannes Feilhauer ◽  
Gregory P. Asner ◽  
Roberta E. Martin ◽  
Sebastian Schmidtlein
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Hyperspectral Data ◽  
Least Squares Regression
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