Estimation of bioenergy crop yield and N status by hyperspectral canopy reflectance and partial least square regression-Reference-Cited by-同舟云学术

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Estimation of bioenergy crop yield and N status by hyperspectral canopy reflectance and partial least square regression

Published:2016-05-27 Issue:2 Volume:18 Page:192-209
ISSN:1385-2256
Container-title:Precision Agriculture
language:en
Short-container-title:Precision Agric

Author:

Foster A. J.,Kakani V. G.,Mosali J.

Funder

USDA-NIFA, US DOE Biomass Research and Development Initiative

Publisher

Springer Science and Business Media LLC

Subject

General Agricultural and Biological Sciences

Link

http://link.springer.com/content/pdf/10.1007/s11119-016-9455-8.pdf

Reference33 articles.

1. An, N., Price, K. P., & Blair, J. M. (2013). Estimating above-ground net primary productivity of the tallgrass prairie ecosystem of the Central Great Plains using AVHRR NDVI. International Journal of Remote Sensing, 34(11), 3717–3735. doi: 10.1080/01431161.2012.757376 .

2. Blackburn, G. A. (1998). Quantifying chlorophylls and carotenoids at leaf and canopy scales: An evaluation of some hyperspectral approaches. Remote Sensing of Environment, 66(3), 273–285.

3. Bouman, B. (1995). Crop modelling and remote sensing for yield prediction. NJAS wageningen Journal of life sciences, 43(2), 143–161.

4. Cho, M. A., Skidmore, A., Corsi, F., Van Wieren, S. E., & Sobhan, I. (2007). Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression. International Journal of Applied Earth Observation and Geoinformation, 9(4), 414–424.

5. Danson, F., & Plummer, S. (1995). Red-edge response to forest leaf area index. Remote Sensing, 16(1), 183–188.

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