Evidence map›Paper›PMID 39678010›Full record

ArticleFrontiers in plant science2024

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

Cuifen Xia, Wenwu Zhou, Qingtai Shu, Zaikun Wu, Mingxing Wang, Li Xu, Zhengdao Yang, Jinge Yu, Hanyue Song, Dandan Duan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Cuifen XiaCollege of Forestry, Southwest Forestry University, Kunming, China.
Wenwu ZhouGuangyuan Forestry Bureau, Guangyuan, China.
Qingtai ShuCollege of Forestry, Southwest Forestry University, Kunming, China.
Zaikun WuCollege of Forestry, Southwest Forestry University, Kunming, China.
Mingxing WangCollege of Forestry, Southwest Forestry University, Kunming, China.
Li XuCollege of Forestry, Southwest Forestry University, Kunming, China.
Zhengdao YangCollege of Forestry, Southwest Forestry University, Kunming, China.
Jinge YuSchool of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing, China.
Hanyue SongCollege of Forestry, Fujian Agriculture and Forestry University, Fuzhou, China.
Dandan DuanInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 Bayesian Kriging regression prediction (EBKRP) method to obtain the continuous distribution of GEDI spot parameters in an unknown space. Initially, 52 measured sample data were employed to screen the modeling parameters with the Pearson and RF methods. Next, the Bayesian optimization (BO) algorithm was applied to optimize the KNN regression model, RFR model, and Gradient Boosting Regression Tree (GBRT) model. These steps were taken to establish the most effective RS estimation model for chlorophyll content in

Indexed as

Bayesian optimization algorithmchlorophyll contentEBKRP methodestimationmodeling factor selectionremote sensing

Identifiers

PMID39678010
PMCPMC11638747

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.