Use of portable X-ray fluorescence spectrometry for classifying soils from different land use land cover systems in India

被引：48

作者：

Chakraborty, Somsubhra ^{[1
]}

Li, Bin ^{[2
]}

Weindorf, David C. ^{[3
]}

Deb, Shovik ^{[4
]}

Acree, Autumn ^{[3
]}

De, Parijat ^{[4
]}

Panda, Parimal ^{[4
]}

机构：

[1] Indian Inst Technol, Kharagpur, W Bengal, India

[2] Louisiana State Univ, Dept Expt Stat, Baton Rouge, LA 70803 USA

[3] Texas Tech Univ, Dept Plant & Soil Sci, Box 42122, Lubbock, TX 79409 USA

[4] Uttar Banga Krishi Viswavidyalaya, Cooch Behar, India

来源：

GEODERMA | 2019年 / 338卷

关键词：

Land use/land classification; Proximal sensors; India; Machine learning; Portable X-ray fluorescence; ORGANIC-CARBON; CONTAMINATION; ECOSYSTEM; CAPACITY; STORAGE; FOREST; PXRF;

D O I：

10.1016/j.geoderma.2018.11.043

中图分类号：

S15 [土壤学];

学科分类号：

0903 ; 090301 ;

摘要：

In this study, elemental data from portable X-ray fluorescence (PXRF) spectrometry was used to test the efficiency of four machine learning techniques (random forest; linear and nonlinear support vector machine; classification and regression tree) for distinguishing three land use types in India based upon scans of mineral surface (0-20 cm) soil. Results showed similar performance among the four tested algorithms, with classification accuracy of a randomly selected validation set ranging from 83% to 91%. The classification and regression tree was favored based upon simple "IF AND THEN" rules which make classification of the data simple. In sum, PXRF data was shown highly effective at differentiating land use types in India. Future work should focus on a larger number of land use classification types and possible combination of PXRF data with complimentary proximal sensing datasets (e.g., visible near infrared spectroscopy).

引用

页码：5 / 13

页数：9

共 51 条

[1] Rice to shrimp: Land use land cover changes and soil degradation in Southwestern Bangladesh [J].