Application of Fractional Differential Calculation in Pretreatment of Saline Soil Hyperspectral Reflectance Data

Pretreatment of spectrum data is a necessary and effective method for improving the accuracy of hyperspectral model building. Traditional differential calculation pretreatment only considers the integer-order differential, such as the 1st order and 2nd order, and overlooks important spectrum information located at fractional order. Since fractional differential (FD) has rarely been applied to spectrum field measurement, there are few reports on the spectrum features of saline soils under different degrees of human interference. We used FD to analyze field spectrum data of saline soil collected from Xinjiang’s Fukang City. Order interval of 0.2 was adopted to divide 0–2 orders into 11-order differentials. MATLAB programming was used to convert the raw spectral reflectance and its four common types of mathematics and to conduct FD calculation. Spectrum data for area A (no human interference area) and area B (human interference area) was separately processed. From the statistical analysis of soil salinization characteristics, the salinization degree and type for area B were more diverse and complicated than area A. MATLAB simulation results showed that FD calculation could depict the minute differences between different FD order spectra under different human interference areas. The overall differential value trend appeared to move towards 0 reflectance value. After differential processing, the trend of bands that passed the 0.05 significance test of correlation coefficient ( ) showed an increase first then decrease later. The maximum absolute value for five spectrum transformations all appeared in the fractional order. FD calculation could significantly increase the correlation between spectral reflectance and soil salt content both for full-band and single-band spectra. Results of this study can serve as a reference for the application of FD in field hyperspectral monitoring of soil salinization for different human interference areas.

[1]  Syed Muslim Shah,et al.  Fractional-order adaptive signal processing strategies for active noise control systems , 2016 .

[2]  Jianli Ding,et al.  Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS–NIR) spectroscopy, Ebinur Lake Wetland, Northwest China , 2018, PeerJ.

[3]  J. Baerdemaeker,et al.  Vis/NIR spectroscopic measurement of selected soil fertility parameters of Cuban agricultural Cambisols , 2014 .

[4]  Meichen Feng,et al.  Hyperspectral estimation of soil organic matter based on different spectral preprocessing techniques , 2017 .

[5]  Paul Woafo,et al.  Dynamics and synchronization analysis of coupled fractional-order nonlinear electromechanical systems , 2012 .

[6]  Wenzhong Shi,et al.  A New Methodology for Spectral-Spatial Classification of Hyperspectral Images , 2016, J. Sensors.

[7]  Samir Bouaziz,et al.  Modeling of soil salinity within a semi-arid region using spectral analysis , 2015, Arabian Journal of Geosciences.

[8]  Dong Zhang,et al.  Influence of Fractional Differential on Correlation Coefficient between EC1:5 and Reflectance Spectra of Saline Soil , 2017 .

[9]  P. Prakash,et al.  Exact solutions and maximal dimension of invariant subspaces of time fractional coupled nonlinear partial differential equations , 2017, Commun. Nonlinear Sci. Numer. Simul..

[10]  Kaiyi Zheng,et al.  Pretreating near infrared spectra with fractional order Savitzky–Golay differentiation (FOSGD) , 2015 .

[11]  Nisar Hussain,et al.  Characterizing soil salinity in irrigated agriculture using a remote sensing approach , 2013 .

[12]  Robert R. McDougal,et al.  Soil disturbance as a driver of increased stream salinity in a semiarid watershed undergoing energy development , 2015 .

[13]  D. Gui,et al.  Studies on the reflectance spectral features of saline soil along the middle reaches of Tarim River: a case study in Xinjiang Autonomous Region, China , 2013, Environmental Earth Sciences.

[14]  J. Schmitt.  Fractional Derivative Analysis of Diffuse Reflectance Spectra , 1998 .

[15]  Suhas P. Wani,et al.  Dependency measures for assessing the covariation of spectrally active and inactive soil properties in diffuse reflectance spectroscopy , 2014 .

[16]  Guofeng Wu,et al.  Comparison of multivariate methods for estimating soil total nitrogen with visible/near-infrared spectroscopy , 2012, Plant and Soil.

[17]  Ahmed S. Elwakil,et al.  Measurement of Supercapacitor Fractional-Order Model Parameters From Voltage-Excited Step Response , 2013, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[18]  Mohamed Elhag,et al.  Evaluation of Different Soil Salinity Mapping Using Remote Sensing Techniques in Arid Ecosystems, Saudi Arabia , 2016, J. Sensors.

[19]  Behrooz Safarinejadian,et al.  Simultaneous state estimation and parameter identification in linear fractional order systems using coloured measurement noise , 2016, Int. J. Control.

[20]  Ping Li,et al.  Hyperspectral field estimation and remote-sensing inversion of salt content in coastal saline soils of the Yellow River Delta , 2016 .

[21]  丁建丽 Ding Jianli.  Detecting soil salinization in arid regions using spectral feature space derived from remote sensing data , 2014 .

[22]  Jianli Ding,et al.  Monitoring and evaluating spatial variability of soil salinity in dry and wet seasons in the Werigan–Kuqa Oasis, China, using remote sensing and electromagnetic induction instruments , 2014 .

[23]  P. Sirisomboon,et al.  Rapid evaluation of the salt content of canned sardines in brine using near-infrared diffuse reflectance spectroscopy , 2016 .

[24]  M. Salakhov,et al.  A simple method to extract spectral parameters using fractional derivative spectrometry. , 2004, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.