Evidence map›Paper›PMID 42382252›Full record

ArticlePlant phenomics (Washington, D.C.)2026

VE-MLM: A variable endmember-based multilinear mixing framework for crop FAPAR estimation using UAV multispectral imagery.

Ningge Yuan, Yadong Liu, Chaoran Zhang, Yuanjin Li, Longfei Ma, Yi Peng, Xianting Wu, Renshan Zhu, Yan Gong, Shenghui Fang

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In one paragraph

Article in Plant phenomics (Washington, D.C.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ningge YuanSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China.
Yadong LiuSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China.
Chaoran ZhangDepartment of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong SAR, 999077, China.
Yuanjin LiSouth China Sea Sea Area and Island Center, Ministry of Natural Resources (South China Sea Standard Measurement and Information Center, Ministry of Natural Resources), Guangzhou, 510300, China.
Longfei MaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China.
Yi PengSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China.
Xianting WuLab of Remote Sensing for Precision Phenomics of Hybrid Rice, Wuhan University, Wuhan, 430079, China.
Renshan ZhuLab of Remote Sensing for Precision Phenomics of Hybrid Rice, Wuhan University, Wuhan, 430079, China.
Yan GongSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China.
Shenghui FangSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The fraction of absorbed photosynthetically active radiation (FAPAR) is critical for characterizing crop photosynthetic capacity and growth status. Remote sensing technology based on unmanned aerial vehicles (UAVs) enables efficient estimation of FAPAR, but multiple scattering and transmission in the complex and dynamically changing crop canopy and background limit the accuracy of vegetation index (VI)-based methods. This study proposed an adaptive spectral unmixing framework VE-MLM for the multi-layer mixed scenarios, comprising three modules: (1)

Indexed as

CropsFAPARMultilinear mixing model (MLM)Spectral mixture analysis (SMA)Variable endmember (VE)

Identifiers

PMID42382252
PMCPMC13316481

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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.