Random Forest Algorithm Improves Detection of Physiological Activity Embedded within Reflectance Spectra Using Stomatal Conductance as a Test Case

Random Forest Algorithm Improves Detection of Physiological Activity Embedded within Reflectance Spectra Using Stomatal Conductance as a Test Case

Plants transpire water through their tissues in order to move nutrients and water to the cells. Transpiration includes various mechanisms, primarily stomata movement, which controls the rate of CO<sub>2</sub> and water vapor exchange between the tissues and the atmosphere. Assessment of...

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Bibliographic Details
Main Authors: Snir Vitrack-Tamam, Lilach Holtzman, Reut Dagan, Shai Levi, Yuval Tadmor, Tamir Azizi, Onn Rabinovitz, Amos Naor, Oded Liran
Format: Article
Language:English
Published: MDPI AG 2020-07-01
Series:Remote Sensing
Subjects:
remote sensing
hyperspectral
machine learning
random forest
artificial neural network
transpiration
Online Access:https://www.mdpi.com/2072-4292/12/14/2213

Internet

https://www.mdpi.com/2072-4292/12/14/2213

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