Influence of Fractional Differential on Correlation Coefficient between EC1:5 and Reflectance Spectra of Saline Soil

Nan Xia,Dong Zhang,Tashpolat Tiyip,Ilyas Nurmemet,Ardak Kelimu,Jianli Ding,Fei Zhang

doi:10.1155/2017/1236329

Abstract

Soil salinization is one of the most serious environmental issues in arid and semiarid area with severe social, economic, and ecological problems. At present, most inversion models are based on raw reflectance spectra or integer differential transform. In this study, we measured the hyperspectral reflectance and EC1:5 of soil samples collected form Ebinur Lake to analyze the influence of fractional differential on correlation coefficient between EC1:5 and reflectance spectra. The results showed that the fractional differential increased sensibly the accuracy for the analysis of the reflectance spectra. The study might provide a new insight for monitoring soil salinity using hyperspectral data, and further researches should be concentrated on physical meaning of fractional differential in hyperspectral data to provide theoretical basis to building, describing, and spreading inversion models.

Highlights

Using the hyperspectral data combined with the EC1:5 of saline soil samples, we aimed to discuss the influence of fractional differential on correlation coefficient between EC1:5 and reflectance spectra of saline soil and to provide a basis reference for building inversion model of soil salinity by using hyperspectral data
According to the results found in this study, spectral derivative algorithm could increase the number of bands to pass the significant test (p < 0 01) with increase of the order; the numbers followed nearly increasing-decreasing trend, and all reached the maximum at fractional order
We took the Ebinur Lake in the northwest border of Xinjiang, China, as the study area, used the hyperspectral reflectance data and EC1:5 values of 43 saline soil samples that were collected around the Ebinur Lake, and studied the influence of fractional differential on correlation coefficient between EC1:5 and reflectance spectra of saline soil

Summary

Introduction

Hyperspectral Data for Estimation of Soil Salinity. Identifying, monitoring, and mapping soil salinity are badly needed for sustainable agricultural management in the areas which are facing the salinization problem [2]. Hyperspectral remote sensing and spectroradiometer, a wide-spectral range from 350 nm to 2500 nm with a spectral resolution of 1 nm~10 nm per spectral band, have been shown as good alternatives to traditional field work and become a promising tool for estimating soil salinity [5,6,7]. The target in hyperspectral data could be identified by spectral features deposited in the spectral libraries when the data has a wide-spectral range and a high-spectral resolution [8]

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Conclusion