Investigation of optimal vegetation indices for retrieval of leaf chlorophyll and leaf area index using enhanced learning algorithms-Reference-Cited by-同舟云学术

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Investigation of optimal vegetation indices for retrieval of leaf chlorophyll and leaf area index using enhanced learning algorithms

Published:2022-01 Issue: Volume:192 Page:106581
ISSN:0168-1699
Container-title:Computers and Electronics in Agriculture
language:en
Short-container-title:Computers and Electronics in Agriculture

Author:

Verma Bhagyashree,Prasad Rajendra,Srivastava Prashant K.,Yadav Suraj A.,Singh Prachi,Singh R.K.

Funder

Indian Institute of Technology Delhi

Banaras Hindu University

Ministry of Education, India

Publisher

Elsevier BV

Subject

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

Reference50 articles.

1. Comparative relationships between some red edge parameters and seasonal leaf chlorophyll concentrations;Belanger;Can. J. Remote Sens.,1995

2. Utilization of single-image normalized difference vegetation index (SI-NDVI) for early plant stress detection;Biesel;Appl. Plant Sci.,2018

3. Random Forests;Breiman;Mach. Learn.,2001

4. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density;Broge;Remote Sens. Environ.,2001

5. Forcing a wheat crop model with LAI data to access agronomic variables: Evaluation of the impact of model and LAI uncertainties and comparison with an empirical approach;Casa;Eur. J. Agron.,2012

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