A comparison of machine learning algorithms for mapping soil iron parameters indicative of pedogenic processes by hyperspectral imaging of intact soil profiles-Reference-Cited by-同舟云学术

A comparison of machine learning algorithms for mapping soil iron parameters indicative of pedogenic processes by hyperspectral imaging of intact soil profiles

Published:2021-12-16 Issue:1 Volume:73 Page:
ISSN:1351-0754
Container-title:European Journal of Soil Science
language:en
Short-container-title:European J Soil Science

Author:

Xu Shengxiang¹²^ORCID,Zhao Yongcun¹²,Wang Meiyan¹²^ORCID,Shi Xuezheng¹²

Affiliation:

1. State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science Chinese Academy of Sciences Nanjing China

2. College of Advanced Agricultural Sciences University of Chinese Academy of Sciences Beijing China

Funder

National Natural Science Foundation of China

National Basic Research Program of China

Publisher

Wiley

Subject

Soil Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/ejss.13204

Reference62 articles.

1. Predictive ability of soil properties to spectral degradation from laboratory Vis-NIR spectroscopy data

2. Improving the prediction performance of a large tropical vis-NIR spectroscopic soil library from Brazil by clustering into smaller subsets or use of data mining calibration techniques

3. Estimating relative ages from iron-oxide/total-iron ratios of soils in the western Po Valley, Italy

4. A comparison of point and imaging visible-near infrared spectroscopy for determining soil organic carbon

5. Pedogenesis, permafrost, substrate and topography: Plot and landscape scale interrelations of weathering processes on the central-eastern Tibetan Plateau

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