Comparison between vegetation indices for detecting spatial and temporal variabilities in soybean crop using canopy sensors-Reference-Cited by-同舟云学术

Comparison between vegetation indices for detecting spatial and temporal variabilities in soybean crop using canopy sensors

Published:2019-12-09 Issue:5 Volume:21 Page:979-1007
ISSN:1385-2256
Container-title:Precision Agriculture
language:en
Short-container-title:Precision Agric

Author:

Morlin Carneiro Franciele^ORCID,Angeli Furlani Carlos Eduardo,Zerbato Cristiano,Candida de Menezes Patricia,da Silva Gírio Lucas Augusto,Freire de Oliveira Mailson

Publisher

Springer Science and Business Media LLC

Subject

General Agricultural and Biological Sciences

Link

http://link.springer.com/content/pdf/10.1007/s11119-019-09704-3.pdf

Reference103 articles.

1. Abdel-Rahman, E. M., & Ahmed, F. B. (2008). The application of remote sensing techniques to sugarcane (Saccharum spp. hybrid) production: A review of the literature. International Journal of Remote Sensing, 29(13), 3753–3767. https://doi.org/10.1080/01431160701874603.

2. Ag Leader Technology. (2011). Precision point blog. Retrieved September 13, 2017, from http://www.agleader.com/blog/optrx-crop-sensors-a-tech-support-perspective/.

3. Alvares, C. A., Stape, J. L., Sentelhas, P. C., de Gonçalves, J. L. M., & Sparovek, G. (2013). Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, 22(6), 711–728. https://doi.org/10.1127/0941-2948/2013/0507.

4. Amaral, L. R., Molin, J. P., Portz, G., Finazzi, F. B., & Cortinove, L. (2015). Comparison of crop canopy reflectance sensors used to identify sugarcane biomass and nitrogen status. Precision Agriculture, 16(1), 15–28. https://doi.org/10.1007/s11119-014-9377-2.

5. Amaral, L. R., Trevisan, R. G., & Molin, J. P. (2018). Canopy sensor placement for variable-rate nitrogen application in sugarcane fields. Precision Agriculture, 19(1), 147–160. https://doi.org/10.1007/s11119-017-9505-x.

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