An RTM-Driven Machine Learning Approach for Estimating High-Resolution FAPAR From LANDSAT 5/7/8/9 Surface Reflectance

An RTM-Driven Machine Learning Approach for Estimating High-Resolution FAPAR From LANDSAT 5/7/8/9 Surface Reflectance

Fraction of absorbed photosynthetically active radiation (FAPAR) is an essential indicator of vegetation productivity and carbon uptake. Although coarse-resolution FAPAR products from moderate-resolution imaging spectroradiometer (MODIS) and visible infrared imaging radiometer suite (VIIRS) sensors...

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Bibliographic Details
Main Authors: Guodong Zhang, Gaofei Yin, Yi Zhang, Jiangchuan Hu, Zongyan Li, Changjing Wang, Dujuan Ma, Jiangliu Xie
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Coupled radiative transfer model (RTM)
fraction of absorbed photosynthetically active radiation (FAPAR)
high resolution
Landsat
random forest (RF)
Online Access:https://ieeexplore.ieee.org/document/10606354/
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