Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method

By Liang Liang, Zhihao Qin, Shuhe Zhao, Liping Di, Chao Zhang, Meixia Deng, Hui Lin, Lianpeng Zhang, Lijuan Wang, Zhixiao Liu. 2016

120 citations recorded by the source corpus.

Subfields: Ecology, Global and Planetary Change, Plant Science

View Paper: https://www.semanticscholar.org/paper/12bcdc1749029a213d512dc1a17ac50a0bb3ad49

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

1. Gustau Camps-Valls
2. Z. Malenovsky
3. José F. Moreno
4. Jochem Verrelst
5. J. Gastellu‐Etchegorry
6. Luís Pádua
7. Peter R. J. North
8. Raul Morais

In Global and Planetary Change

1. Z. Malenovsky
2. Gustau Camps-Valls
3. J. Gastellu‐Etchegorry
4. José F. Moreno
5. Peter R. J. North
6. Jochem Verrelst
7. C. van der Tol

In Plant Science

1. Jochem Verrelst
2. José F. Moreno
3. Gustau Camps-Valls
4. J. Gastellu‐Etchegorry
5. Z. Malenovsky

Citations in the corpus

• Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry (2017, background): “[115], PRI and, once again, TCARI/OSVAI, are the most suitable indices for leaf chlorophyll content estimation.”
• Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods (2018, methodology): “To overcome this problem, RF is often used in combination with sensitive bands or simple transformations in the form of VIs that are known to be sensitive to the targeted vegetation property (Adam et al. 2014; Han et al. 2016; Liang et al. 2016).”