Predicting C3 and C4 grass nutrient variability using in situ canopy reflectance and partial least squares regression-Reference-Cited by-同舟云学术

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Predicting C3 and C4 grass nutrient variability using in situ canopy reflectance and partial least squares regression

Published:2015-03-19 Issue:6 Volume:36 Page:1743-1761
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Adjorlolo Clement¹²,Mutanga Onisimo²,Cho Moses Azong²³

Affiliation:

1. South African National Space Agency (SANSA), SANSA Earth Observation, Pretoria 0001, South Africa

2. School of Agriculture, Earth & Environmental Sciences, University of KwaZulu-Natal (UKZN), Pietermaritzburg 3209, South Africa

3. Natural Resources and the Environment, Council for Scientific and Industrial Research (CSIR), Pretoria 0001, South Africa

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2015.1024893

Reference39 articles.

1. Challenges and opportunities in the use of remote sensing for C3and C4grass species discrimination and mapping

2. Spectral resampling based on user-defined inter-band correlation filter: C3 and C4 grass species classification

3. C3 grasses have higher nutritional quality than C4 grasses under ambient and elevated atmospheric CO2

4. A classification-based assessment of the optimal spectral and spatial resolutions for Great Lakes coastal wetland imagery

5. An extension of the Jeffreys-Matusita distance to multiclass cases for feature selection

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