Quantitative Estimating Salt Content of Saline Soil Using Laboratory Hyperspectral Data Treated by Fractional Derivative

Dong Zhang,Jianli Ding,Jingzhe Wang,Fei Zhang,Ilyas Nurmemet,Tashpolat Tiyip,Ardak Kelimu

doi:10.1155/2016/1081674

Abstract

Most present researches on estimation of soil salinity by hyperspectral data have focused on the spectral reflectance or their integer derivatives but ignored the fractional derivative information of hyperspectral data. Motivated by this situation, the selected study area is the Ebinur Lake basin located in the southwest border in the Xinjiang Uygur Autonomous Region, China, with severe salinization. The field work was conducted from 15 to 25 October, 2014, and a total of 180 soil samples were collected from 45 sampling sites; after measuring the soil salt content and spectral reflectance in the laboratory, the range from 0 to 2 was divided into 11 orders (interval 0.2) and then the hyperspectral data were treated by 4 kinds of mathematical transformations and 11 orders of fractional derivatives. Combined with the soil salt content, partial least square regression method was applied for model calibrations and predictions and some indexes were used to evaluate the performance of models. The results showed that the retrieval model built up by 250 bands based on 1.2-order derivative of 1/lg⁡R had excellent capacity of estimating soil salt content in the study area (RMSEC=14.685 g/kg, RMSEP=14.713 g/kg, R2C=0.782, R2P=0.768, and RPD = 2.080). This study provides an application reference for quantitative estimations of other land surface parameters and some other applications on hyperspectral technology.

Highlights

Soil salinization is one of the most common but serious environmental problems worldwide and is considered as one of the main paths to land desertification [1]
There were 30 models having acceptable results with ratio of performance to deviation (RPD) > 1.4, and among these 30 models there was only one best model which was built up by 250 bands based on 1.2-order derivative of 1/lg R with 4 principal components, RPD = 2.080 (>2.0), lowest RMSEC (14.685 g/kg) and RMSEP (14.713 g/kg), highest R2C (0.782), and R2P (0.768)
As is known to all, the first and second derivatives correspondingly mean the slope and curvature of spectral curves, and, the physical meaning of fractional derivative in spectroscopy has not been clarified yet. It suggests that the order between 0 and 2 of fractional derivative could be described as the sensitivity to the slope and curvature of spectral curves; when the order increases from 0 to 1, the derivative value becomes more sensitive to the slope and less sensitive to reflectance, and while the order increases from 1 to 2, the derivative value turns out more sensitive to the curvature and less sensitive to the slope [22]

Summary

Introduction

Soil salinization is one of the most common but serious environmental problems worldwide and is considered as one of the main paths to land desertification [1]. Approximately 20% of irrigated lands are confronted with a severe threat of salinization and this figure will increase with great population pressure [4]. Faced with such large amounts of salt-affected land, timely detection and assessment of soil salinization become extremely necessary and urgent for sustainable development [5]. Because of low-cost, rapid data acquisition, and large area coverage [7], remote sensing (especially hyperspectral remote sensing) shows as a promising tool to substitute or complement traditional methods and provides an overview of salinization on different spatial scales, and hyperspectral techniques have been successfully used for quantitative analysis of some indexes of the soil salinization [8,9,10,11]

Methods

Findings

Discussion

Conclusion