Machine learning methods’ performance in radiative transfer model inversion to retrieve plant traits from Sentinel-2 data of a mixed mountain forest

Machine learning methods’ performance in radiative transfer model inversion to retrieve plant traits from Sentinel-2 data of a mixed mountain forest

Assessment of vegetation biochemical and biophysical variables is useful when developing indicators for biodiversity monitoring and climate change studies. Here, we compared a radiative transfer model (RTM) inversion by merit function and five machine learning algorithms trained on an RTM simulated...

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Bibliographic Details
Main Authors: Abebe Mohammed Ali, Roshanak Darvishzadeh, Andrew Skidmore, Tawanda W. Gara, Marco Heurich
Format: Article
Language:English
Published: Taylor & Francis Group 2021-01-01
Series:International Journal of Digital Earth
Subjects:
leaf area index
leaf/canopy chlorophyll content
radiative transfer model
look-up table
machine learning algorithms
Online Access:http://dx.doi.org/10.1080/17538947.2020.1794064

Internet

http://dx.doi.org/10.1080/17538947.2020.1794064

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