Estimating rainfed groundnut’s leaf area index using Sentinel-2 based on Machine Learning Regression Algorithms and Empirical Models-Reference-Cited by-同舟云学术

Estimating rainfed groundnut’s leaf area index using Sentinel-2 based on Machine Learning Regression Algorithms and Empirical Models

Published:2024-02-21 Issue:3 Volume:25 Page:1404-1428
ISSN:1385-2256
Container-title:Precision Agriculture
language:en
Short-container-title:Precision Agric

Author:

Ekwe Michael Chibuike,Adeluyi Oluseun,Verrelst Jochem,Kross Angela,Odiji Caleb Akoji

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11119-024-10117-0.pdf

Reference102 articles.

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2. Adeluyi, O., Harris, A., Verrelst, J., Foster, T., & Clay, G. D. (2021). Estimating the phenological dynamics of irrigated rice leaf area index using the combination of PROSAIL and Gaussian Process Regression. International Journal of Applied Earth Observation and Geoinformation, 102, 102454. https://doi.org/10.1016/j.jag.2021.102454

3. African Institute of Corporate Citizenship. 2016. Malawi Groundnut Outlook. TAURUS House Executive Offices City Centre, Along Convention Drive P/Bag 382 Lilongwe 3 Malawi.

4. Ajeigbe H.A., Waliyar F., Echekwu C.A, Ayuba K., Motagi B.N., Eniayeju D. and Inuwa A. (2015). A Farmer's Guide to Groundnut Production in Nigeria. Patancheru 502 324, Telangana, India: International Crops Research Institute for the Semi-Arid Tropics. 36 pp.

5. Alemayehu C, Berhanu A, Mulugeta T, Abdi M, Tameru A, Helge S. (2014). Opportunities and constraints of groundnut production in selected drylands of Ethiopia. DCG Report No.74. Drylands Coordination Group.