Title
Mapping Soil Alkalinity and Salinity in Northern Songnen Plain, China with the HJ-1 Hyperspectral Imager Data and Partial Least Squares Regression.
Abstract
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 R-2 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.
Year
DOI
Venue
2018
10.3390/s18113855
SENSORS
Keywords
Field
DocType
soil,alkalinity and salinity,hyperspectral data,PLSR model
Soil science,Analytical chemistry,Partial least squares regression,Alkali soil,Hyperspectral imaging,Engineering,Salinity
Journal
Volume
Issue
ISSN
18
11.0
1424-8220
Citations 
PageRank 
References 
0
0.34
3
Authors
6
Name
Order
Citations
PageRank
Lin Bai100.34
Cuizhen Wang26512.07
Shuying Zang322.86
Changshan Wu4103.85
Jinming Luo500.34
Yuexiang Wu600.34