Hyperspectral Estimation Models for the Saline Soil Salinity in the Yellow River Delta

Hong  Yan Chen; Geng Xing Zhao; Ya Qiu Liu; Jing Chun Chen; Hong Zhang

doi:10.4028/www.scientific.net/AMM.738-739.197

Paper Titles

Study on Manufacture Process, Micro-Hardness and Hall-Petch Relation of Pure Rutile Phase TiO₂ MAO Coating
p.175

Vibration Control by Using Piezostack Based Mount
p.180

Design and Implementation of LCD Based on FPGA
p.184

Applying Data Mining Techniques on Continuous Sensed Data for Daily Living Activity Recognition
p.191

Hyperspectral Estimation Models for the Saline Soil Salinity in the Yellow River Delta
p.197

GIS-Based Urban Land Use and Traffic Coordination Evaluation System
p.204

Inversion of Aerosol Optical Depth Based on MODIS Remote Sensor
p.209

Research and Implementation of Three-Dimensional Power Lines Selection System Based on Aerial Images
p.213

Study on Remote Sensing Images of De-Blurring Based on Dictionary Learning
p.217

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 738-739Hyperspectral Estimation Models for the Saline...

Hyperspectral Estimation Models for the Saline Soil Salinity in the Yellow River Delta

Abstract:

Quantitative identification of the saline soil salinity content is a necessary precondition for the reasonable improvement and utilization of saline land, the article aimed at comparing the different quantitative analysis methods and achieving fast estimation of the saline soil salt content in the Yellow River Delta based on the visible-near infrared spectroscopy. Kenli County in Shandong Province was selected as the experimental area, firstly, the representative soil samples were selected, hyperspectral reflectance of the soil samples were measured in situ and transformed to the first deviation. Secondly the correlate spectra, the characteristic spectra and indices were firstly filtered using correlation analysis. Finally, the estimation models of soil salinity content were built using the multiple linear regression (MLR), back propagation neural network (BPNN) and support vector machine (SVM) respectively. The results indicated that the characteristic wave bands of soil salinity were 684 nm and 2058 nm. On the condition of the same input variables, the prediction precision of the SVM models was the highest, followed by the BPNN, the MLR was the lowest. The SVM model based on the first deviation of the reflectance at 684 and 2058nm had the highest precision, with the calibration R² of 0.91 and RMSE as 0.11%, the validation R² of 0.93, RMSE as 0.26% and RPD as 2.61, which had very good prediction accuracy of soil salt content, and was very stable and reliable. Different input variables had a great impact on the model accuracy, among of the MLR models, only the precision of the model based on characteristic spectral indices was slightly higher and could be used to estimate salt content, among of the BPNN and SVM models, the precision of the models based on characteristic spectra and indices was more high and stable significantly than the models on the correlate spectra. Therefore, for the three modeling methods of multiple linear regression, back propagation neural network and support vector machine, building the estimation model of saline soil salinity content based on characteristic spectra indices was effective.