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

Group of Agrophysics Studies, Migal Institute, Kiryat Shemona, Upper Galilee 11016, Israel
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Remote Sens. 2020, 12(14), 2213; https://doi.org/10.3390/rs12142213
Received: 7 June 2020 / Revised: 6 July 2020 / Accepted: 7 July 2020 / Published: 10 July 2020
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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 CO2 and water vapor exchange between the tissues and the atmosphere. Assessment of stomatal conductance is available for gas exchange techniques at leaf level, yet these techniques are not scalable to the whole plant let alone a large vegetation area. Hyperspectral reflectance spectroscopy, which acquires hundreds of bands in a single scan, may capture a glimpse of the crop’s physiological activity and therefore meet the scalability challenge. In this study, classic chemometric analyses are used alongside advanced statistical learning algorithms in order to identify stomatal conductance cues in hyperspectral measurements of cotton plants experiencing a gradient of irrigation. Random forest of regression trees identified 23 wavelengths related to both structural properties of the plant as well as water content. Partial least squares regression succeeded in relating these wavelengths to stomatal conductance, but only partially (R2 < 0.2). An artificial neural network algorithm reported an R2 = 0.54 with an 89% error-free performance on the same data subset. This study discusses implementation of machine learning methodologies as a benchmark for deeper analysis of spectral information, such as required when searching for plant physiology-related attenuations embedded within reflectance spectra. View Full-Text
Keywords: remote sensing; hyperspectral; machine learning; random forest; artificial neural network; transpiration; cotton remote sensing; hyperspectral; machine learning; random forest; artificial neural network; transpiration; cotton
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MDPI and ACS Style

Vitrack-Tamam, S.; Holtzman, L.; Dagan, R.; Levi, S.; Tadmor, Y.; Azizi, T.; Rabinovitz, O.; Naor, A.; Liran, O. Random Forest Algorithm Improves Detection of Physiological Activity Embedded within Reflectance Spectra Using Stomatal Conductance as a Test Case. Remote Sens. 2020, 12, 2213. https://doi.org/10.3390/rs12142213

AMA Style

Vitrack-Tamam S, Holtzman L, Dagan R, Levi S, Tadmor Y, Azizi T, Rabinovitz O, Naor A, Liran O. Random Forest Algorithm Improves Detection of Physiological Activity Embedded within Reflectance Spectra Using Stomatal Conductance as a Test Case. Remote Sensing. 2020; 12(14):2213. https://doi.org/10.3390/rs12142213

Chicago/Turabian Style

Vitrack-Tamam, Snir, Lilach Holtzman, Reut Dagan, Shai Levi, Yuval Tadmor, Tamir Azizi, Onn Rabinovitz, Amos Naor, and Oded Liran. 2020. "Random Forest Algorithm Improves Detection of Physiological Activity Embedded within Reflectance Spectra Using Stomatal Conductance as a Test Case" Remote Sensing 12, no. 14: 2213. https://doi.org/10.3390/rs12142213

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