سابِق|تال

Hyperspectral reflectance characteristics paramter extraction for soil classification model

  • role： First author第一作者
  • Affiliation：

    College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China

  • Email：huanjunliu@yeah.net
  • Introduction：刘焕军(1981— )，男，副教授，研究方向为农业遥感。Email：huanjunliu@yeah.net
LIU Huanjun1,  
  • Affiliation：

    College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China

ZHANG Xiaokang1,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China

  • Email：zhangxinle@gmail.com
  • Introduction：张新乐(1980— )，女，副教授，研究方向为生态遥感。Email：zhangxinle@gmail.com
ZHANG Xinle1*,  
  • Affiliation：

    Institute of Science and Technology Information, Heilongjiang Academy of Land Reclamation Sciences, Harbin 150038, China

WU Hongfeng2,  
  • Affiliation：

    College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China

JIN Huining1,  
  • Affiliation：

    College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China

YU Shengnan1,  
  • Affiliation：

    College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China

QIU Zhengchao1

ملخص

Soil taxonomy plays a significant role in soil remote sensing. Soil spectral reflectance is the comprehensive representation of soil’s physical and chemical parameters. The study of soil spectral reflectance features is the physical basis for soil remote sensing, and it provides new ideas and methods for soil classification.To quickly classify soil based on topsoil reflectance spectral characteristics and provide an effective method, the room spectral reflectance in the visible and near-infrared region (400—2500 nm) of 148 soil samples, including black, chernozem, blown, and meadow soils,were collected from Songnen plain, which is located in Heilongjiang province. Given that the high-frequency noise of reflectance spectrum is relatively strong in the range of 400 nm to 430 nm and 2450 nm to 2500 nm, we chose the visible and near-infrared region of 430 nm to 2450 nm.The spectral reflectance of soil samples were measured using ASDFieldSpec 3 in the laboratory. Resampling and continuum removal techniques were used to process spectral data and extract the spectral characteristic parameters (i.e., the absorption positions of the spectral curve, the vale’s area, the slope of the spectral curve, the distance between adjacent absorption positions, the depth of the vale, and the width of the vale), respectively. When the K-means clustering results based on spectral reflectance were compared with the K-means clustering results based on the spectral characteristic parameters, the spectral characteristic parameters were found to be more suitable for soil classification. Finally, the spectral characteristic parameters were used to constructsoil classification model that is based on the decision tree. The classification accuracy of black, chernozem, blown, and meadow soilsare 97.22%, 94.2%, 85.29% and 55.56%, respectively.These results were obtained by using the decision tree model. The most effective spectral characteristic parameters include the second absorption positions of the spectral curve, the first vale’s area, the first two vales’ area, and the slope of the spectral curve at 500 nm to 600 nm and 1340 nm to 1360 nm. Meadow soilis often distributed in the lower area, and the spectral curve of the topsoil of meadow soil is similar to its adjacent soil, which is regarded asa trend toward its adjacent soil. The spectral characteristic parameters that were extracted could be used to study the soil classification, and the decision tree model that is based on the spectral characteristic parameters of topsoil reflectance has achieved excellent results. This paper provides a convenient, rapid, and nondestructive approach for soil classification, which helps in soil mapping.

مفهوم

soil taxonomy;spectral characteristic parameters;K-means clustering;decision tree;Songnen plain

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