Estimation of Low Organic Matter Content in Desert Soil of Arid Area Based on Fractional Order Sprott Chaotic Circuit and Gray Theory

• Main Authors: Anhong Tian, Chengbiao Fu, Xiaoyi Su, Her-Terng Yau, Heigang Xiong
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Organic matter of desert soil; fractional order Sprott chaotic system; gray prediction model; hyperspectral reflectance; different human interference regions
• Online Access: https://ieeexplore.ieee.org/document/8978818/

Weak spectral response and low model accuracy problems occurred in the process of quantitative inversion of low organic matter content of desert soil in arid areas. This study collects soil samples and field spectral data from different human interference regions in Fukang City, Xinjiang, to search the soil hyperspectral response law that based on the fractional Sprott chaotic system, and combined with the gray system theory to estimate organic matter content rapidly and accurately. Simulation shows that for those sampling soil with lower organic matter content, the range of X and Y components of the dynamic error motion curve distribution of the Sprott chaotic system is larger, and the motion curve of the non-integer order 1.9-order dynamic error is the most obvious. Since the chaotic attractors will appear as a linear trend according to different contents of the organic matter, so this thesis establishes a gray prediction model based on the 1.9 fractional order chaotic attractors. The R², RPD, and RMSE of the low organic matter content in the region without human interruption are 0.995, 14.86, and 0.17, respectively. The R², RPD, and RMSE of low organic matter content in the region with human interruption are 0.992, 11.95, and 0.11, respectively. This study demonstrates that it is feasible to estimate the low organic matter content of desert soils in arid regions via the gray prediction model that based on fractional chaotic attractors. This study provides a novel method for soil spectra signal analysis and estimation of the organic matter content.