Rapid estimation of soil water content based on hyperspectral reflectance combined with continuous wavelet transform, feature extraction, and extreme learning machine.

Journal: PeerJ
Published Date:

Abstract

BACKGROUND: Soil water content is one of the critical indicators in agricultural systems. Visible/near-infrared hyperspectral remote sensing is an effective method for soil water estimation. However, noise removal from massive spectral datasets and effective feature extraction are challenges for achieving accurate soil water estimation using this technology.

Authors

  • Shaomin Chen
    Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, China.
  • Jiachen Gao
    Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, China.
  • Fangchuan Lou
    Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, China.
  • Yunfei Tuo
    Ecology and Environment Department, Southwest Forestry University, Kunming, China.
  • Shuai Tan
    Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, China.
  • Yuyang Shan
    State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi'an University of Technology, Xi'an, China.
  • Lihua Luo
    Reproductive and Genetic Hospital, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
  • Zhilin Xu
    Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, China.
  • Zhengfu Zhang
    Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, China.
  • Xiangyu Huang
    Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, China.