Results 231 to 240 of about 4,811,303 (245)

Vegetation Indices Combining the Red and Red-Edge Spectral Information for Leaf Area Index Retrieval [PDF]

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018
Leaf area index (LAI) is a crucial biophysical variable for agroecosystems monitoring. Conventional vegetation indices (VIs) based on red and near infrared regions of the electromagnetic spectrum, such as the normalized difference vegetation index (NDVI),
Giovanni Laneve, Huichun Ye, Qiaoyun Xie
exaly   +3 more sources

Some of the next articles are maybe not open access.

Related searches:

Inversion of Wheat Leaf Area Index by Multivariate Red-Edge Spectral Vegetation Index

Sustainability, 2022
Xiaoxuan Wang
exaly  

Estimating Leaf Chlorophyll Content Using Red Edge Parameters

Pedosphere, 2010
Xia Yao, Yan Zhu, Cao Weixing
exaly  

A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method

Remote Sensing of Environment, 2006
Moses A. Cho, Andrew K. Skidmore
exaly  

Red edge shift and biochemical content in grass canopies

ISPRS Journal of Photogrammetry and Remote Sensing, 2007
Onisimo Mutanga, Andrew K. Skidmore
exaly  

SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity

International Journal of Applied Earth Observation and Geoinformation, 2016
Carmen Quintano Pastor, Alfonso Fernández-Manso, Oscar Fernández-Manso   +2 more
exaly  

Extraction of red edge optical parameters from Hyperion data for estimation of forest leaf area index

IEEE Transactions on Geoscience and Remote Sensing, 2003
Ruiliang Pu, Peng Gong
exaly  

WREP: A wavelet-based technique for extracting the red edge position from reflectance spectra for estimating leaf and canopy chlorophyll contents of cereal crops

ISPRS Journal of Photogrammetry and Remote Sensing, 2017
Xia Yao, Yan Zhu
exaly  

Evaluating chlorophyll density in winter oilseed rape ( L.) using canopy hyperspectral red-edge parameters

Computers and Electronics in Agriculture, 2016
Xiaokun Li, Zhifeng Lu, Rihuan Cong
exaly