Improved Na+ estimation from hyperspectral data of saline vegetation by machine learning-Reference-Cited by-同舟云学术

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Improved Na+ estimation from hyperspectral data of saline vegetation by machine learning

Published:2022-05 Issue: Volume:196 Page:106862
ISSN:0168-1699
Container-title:Computers and Electronics in Agriculture
language:en
Short-container-title:Computers and Electronics in Agriculture

Author:

Chen Daosheng,Zhang Fei,Tan Mou Leong,Chan Ngai Weng,Shi Jingchao,Liu Changjiang,Wang Weiwei

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference91 articles.

1. Hyperspectral remote sensing for extraction of soil salinization in the northern region of Ningxia;Abd El-Hamid;Modeling Earth Systems and Environment,2020

2. “One belt, one road”: China's new global strategy;Aoyama;Journal of Contemporary East Asia Studies,2016

3. Na+ transport in plants;Apse;FEBS Lett.,2007

4. Bao, S., Qin, H., Lao, J., 1988, Soil Agrochemistry Analysis, China Agriculture Press.

5. Random forest in remote sensing: A review of applications and future directions;Belgiu;ISPRS J. Photogramm. Remote Sens.,2016

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