Unlocking vegetation health: optimizing GEDI data for accurate chlorophyll content estimation

Unlocking vegetation health: optimizing GEDI data for accurate chlorophyll content estimation

Chlorophyll content is a vital indicator for evaluating vegetation health and estimating productivity. This study addresses the issue of Global Ecosystem Dynamics Investigation (GEDI) data discreteness and explores its potential in estimating chlorophyll content. This study used the empirical Bayesi...

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Bibliographic Details
Main Authors:	Cuifen Xia, Wenwu Zhou, Qingtai Shu, Zaikun Wu, Mingxing Wang, Li Xu, Zhengdao Yang, Jinge Yu, Hanyue Song, Dandan Duan
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2024-11-01
Series:	Frontiers in Plant Science
Subjects:	remote sensing EBKRP method modeling factor selection Bayesian optimization algorithm chlorophyll content estimation
Online Access:	https://www.frontiersin.org/articles/10.3389/fpls.2024.1492560/full
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