Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data-Reference-Cited by-同舟云学术

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Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data

Published:2019-02-04 Issue:1 Volume:15 Page:
ISSN:1746-4811
Container-title:Plant Methods
language:en
Short-container-title:Plant Methods

Author:

Han Liang,Yang Guijun,Dai Huayang,Xu Bo,Yang Hao,Feng Haikuan,Li Zhenhai,Yang Xiaodong

Funder

National Key Research and Development Program of China

Natural Science Foundation of China

Natural Science Foundation of Beijing Municipality

Beijing Academy of Agriculture and Forestry Sciences

Publisher

Springer Science and Business Media LLC

Subject

Plant Science,Genetics,Biotechnology

Link

http://link.springer.com/content/pdf/10.1186/s13007-019-0394-z.pdf

Reference73 articles.

1. Bendig J, Yu K, Aasen H, Bolten A, Bennertz S, Broscheit J, Gnyp ML, Bareth G. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int J Appl Earth Obs Geoinf. 2015;39:79–87.

2. Li W, Niu Z, Huang N, Wang C, Gao S, Wu CY. Airborne LiDAR technique for estimating biomass components of maize: a case study in Zhangye City, Northwest China. Ecol Indic. 2015;57:486–96.

3. Yang S, Feng Q, Liang T, Liu B, Zhang W, Xie H. Modeling grassland above-ground biomass based on artificial neural network and remote sensing in the Three-River Headwaters Region. Remote Sens Environ. 2018;204:448–55.

4. Yang X, Xu B, Yunxiang J, Jinya L, Zhu X. On grass yield remote sensing estimation models of China’s northern farming-pastoral ecotone. In: Lee G, editor. Advances in computational environment science. Berlin: Springer; 2012. p. 281–91.

5. Zheng G, Chen JM, Tian QJ, Ju WM, Xia XQ. Combining remote sensing imagery and forest age inventory for biomass mapping. J Environ Manag. 2007;85:616–23.

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